case study on ad hoc

What is Ad Hoc Analysis and Reporting? Process, Examples

Appinio Research · 26.03.2024 · 33min read

Have you ever needed to find quick answers to pressing questions or solve unexpected problems in your business? Enter ad hoc analysis, a powerful approach that allows you to dive into your data on demand, uncover insights, and make informed decisions in real time. In today's fast-paced world, where change is constant and uncertainties abound, having the ability to explore data flexibly and adaptively is invaluable. Whether you're trying to understand customer behavior , optimize operations, or mitigate risks, ad hoc analysis empowers you to extract actionable insights from your data swiftly and effectively. It's like having a flashlight in the dark, illuminating hidden patterns and revealing opportunities that may have otherwise gone unnoticed.

What is Ad Hoc Analysis?

Ad hoc analysis is a dynamic process that involves exploring data to answer specific questions or address immediate needs. Unlike routine reporting, which follows predefined formats and schedules, ad hoc analysis is driven by the need for timely insights and actionable intelligence. Its purpose is to uncover hidden patterns, trends, and relationships within data that may not be readily apparent, enabling organizations to make informed decisions and respond quickly to changing circumstances.

Ad hoc analysis involves the flexible and on-demand exploration of data to gain insights or solve specific problems. It allows analysts to dig deeper into datasets, ask ad hoc questions, and derive meaningful insights that may not have been anticipated beforehand. The term "ad hoc" is derived from Latin and means "for this purpose," emphasizing the improvised and opportunistic nature of this type of analysis.

Purpose of Ad Hoc Analysis

The primary purpose of ad hoc analysis is to support decision-making by providing timely and relevant insights into complex datasets. It allows organizations to:

• Identify emerging trends or patterns that may impact business operations.
• Investigate anomalies or outliers to understand their underlying causes .
• Explore relationships between variables to uncover opportunities or risks.
• Generate hypotheses and test assumptions in real time.
• Inform strategic planning, resource allocation, and risk management efforts.

By enabling analysts to explore data in an iterative and exploratory manner, ad hoc analysis empowers organizations to adapt to changing environments, seize opportunities, and mitigate risks effectively.

Importance of Ad Hoc Analysis in Decision Making

Ad hoc analysis plays a crucial role in decision-making across various industries and functions. Here are some key reasons why ad hoc analysis is important:

• Flexibility : Ad hoc analysis offers flexibility and agility, allowing organizations to respond quickly to evolving business needs and market dynamics. It enables decision-makers to explore new ideas, test hypotheses, and adapt strategies in real time.
• Customization : Unlike standardized reports or dashboards, ad hoc analysis allows for customization and personalization. Analysts can tailor their analyses to specific questions or problems, ensuring that insights are directly relevant to decision-makers needs.
• Insight Generation : Ad hoc analysis uncovers insights that may not be captured by routine reporting or predefined metrics. Analysts can uncover hidden patterns, trends, and correlations that drive innovation and competitive advantage by delving into data with a curious and open-minded approach.
• Risk Management : In today's fast-paced and uncertain business environment, proactive risk management is essential. Ad hoc analysis enables organizations to identify and mitigate risks by analyzing historical data, monitoring key indicators, and anticipating potential threats.
• Opportunity Identification : Ad hoc analysis helps organizations identify new opportunities for growth, innovation, and optimization. Analysts can uncover untapped markets, customer segments, or product offerings that drive revenue and profitability by exploring data from different angles and perspectives.
• Continuous Improvement : Ad hoc analysis fosters a culture of constant improvement and learning within organizations. By encouraging experimentation and exploration, organizations can drive innovation, refine processes, and stay ahead of the competition.

Ad hoc analysis is not just a tool for data analysis—it's a mindset and approach that empowers organizations to harness the full potential of their data, make better decisions, and achieve their strategic objectives.

Understanding Ad Hoc Analysis

Ad hoc analysis is a dynamic process that involves digging into your data to answer specific questions or solve immediate problems. Let's delve deeper into what it entails.

Ad Hoc Analysis Characteristics

At its core, ad hoc analysis refers to the flexible and on-demand examination of data to gain insights or address specific queries. Unlike routine reporting, which follows predetermined schedules, ad hoc analysis is triggered by the need to explore a particular issue or opportunity.

Its characteristics include:

• Flexibility : Ad hoc analysis adapts to the ever-changing needs of businesses, allowing analysts to explore data as new questions arise.
• Timeliness : It offers timely insights, enabling organizations to make informed decisions quickly in response to emerging issues or opportunities.
• Unstructured Nature : Ad hoc analysis often deals with unstructured or semi-structured data, requiring creativity and resourcefulness in data exploration.

Ad Hoc Analysis vs. Regular Reporting

While regular reporting provides standardized insights on predetermined metrics, ad hoc analysis offers a more customized and exploratory approach. Here's how they differ:

• Purpose : Regular reporting aims to track key performance indicators (KPIs) over time, while ad hoc analysis seeks to uncover new insights or address specific questions.
• Frequency : Regular reporting occurs at regular intervals (e.g., daily, weekly, monthly), whereas ad hoc analysis occurs on an as-needed basis.
• Scope : Regular reporting focuses on predefined metrics and reports, whereas ad hoc analysis explores a wide range of data sources and questions.

Types of Ad Hoc Analysis

Ad hoc analysis encompasses various types, each serving distinct purposes in data exploration and decision-making. These types include:

• Exploratory Analysis : This type involves exploring data to identify patterns, trends, or relationships without predefined hypotheses. It's often used in the initial stages of data exploration.
• Diagnostic Analysis : Diagnostic analysis aims to uncover the root causes of observed phenomena or issues. It delves deeper into data to understand why specific outcomes occur.
• Predictive Analysis : Predictive analysis leverages historical data to forecast future trends, behaviors, or events. It employs statistical modeling and machine learning algorithms to make predictions based on past patterns.

Common Data Sources

Ad hoc analysis can draw upon a wide array of data sources, depending on the nature of the questions being addressed and the data availability. Common data sources include:

• Structured Data : This includes data stored in relational databases, spreadsheets, and data warehouses, typically organized in rows and columns.
• Unstructured Data : Unstructured data sources, such as text documents, social media feeds, and multimedia content, require specialized techniques for analysis.
• External Data : Organizations may also tap into external data sources, such as market research reports, government databases, or third-party APIs, to enrich their analyses.

Organizations can gain comprehensive insights and make more informed decisions by leveraging diverse data sources. Understanding these foundational aspects of ad hoc analysis is crucial for conducting effective data exploration and driving actionable insights.

How to Prepare for Ad Hoc Analysis?

Before diving into ad hoc analysis, it's crucial to lay a solid foundation by preparing adequately. This involves defining your objectives, gathering and organizing data, selecting the right tools, and ensuring data quality. Let's explore these steps in detail.

Defining Objectives and Questions

The first step in preparing for ad hoc analysis is to clearly define your objectives and formulate the questions you seek to answer.

• Identify Key Objectives : Determine the overarching goals of your analysis. What are you trying to achieve? Are you looking to optimize processes, identify growth opportunities, or solve a specific problem?
• Formulate Relevant Questions : Break down your objectives into specific, actionable questions. What information do you need to answer these questions? What insights are you hoping to uncover?

By defining clear objectives and questions, you can focus your analysis efforts and ensure that you gather the necessary data to address your specific needs.

Data Collection and Organization

Once you have defined your objectives and questions, the next step is to gather relevant data and organize it in a format conducive to analysis.

• Identify Data Sources : Determine where your data resides. This may include internal databases, third-party sources, or even manual sources such as surveys or interviews.
• Extract and Collect Data : Extract data from the identified sources and collect it in a central location. This may involve using data extraction tools, APIs, or manual data entry.
• Clean and Preprocess Data : Before conducting analysis, it's essential to clean and preprocess the data to ensure its quality and consistency. This may involve removing duplicates, handling missing values, and standardizing formats.

Organizing your data in a systematic manner will streamline the analysis process and ensure that you can easily access and manipulate the data as needed. For a streamlined data collection process that complements your ad hoc analysis needs, consider leveraging Appinio .

With its intuitive platform and robust capabilities, Appinio simplifies data collection from diverse sources, allowing you to gather real-time consumer insights effortlessly. By incorporating Appinio into your data collection strategy, you can expedite the process and focus on deriving actionable insights to drive your business forward.

Ready to experience the power of rapid data collection? Book a demo today and see how Appinio can revolutionize your ad hoc analysis workflow.

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Tools and Software

Choosing the right tools and software is critical for conducting ad hoc analysis efficiently and effectively.

• Analytical Capabilities : Choose tools that offer a wide range of analytical capabilities, including data visualization, statistical analysis , and predictive modeling .
• Ease of Use : Look for user-friendly and intuitive tools, especially if you're not a seasoned data analyst. This will reduce the learning curve and enable you to get up and running quickly.
• Compatibility : Ensure the tools you choose are compatible with your existing systems and data sources. This will facilitate seamless integration and data exchange.
• Scalability : Consider the tools' scalability, especially if your analysis needs are likely to grow over time. Choose tools that can accommodate larger datasets and more complex analyses.

Popular tools for ad hoc analysis include Microsoft Excel and Python with libraries like Pandas and NumPy, R, and business intelligence platforms like Tableau and Power BI.

Data Quality Assurance

Ensuring the quality of your data is paramount for obtaining reliable insights and making informed decisions. To assess and maintain data quality:

• Data Validation : Perform data validation checks to ensure the data is accurate, complete, and consistent. This may involve verifying data against predefined rules or business logic.
• Data Cleansing : Cleanse the data by removing duplicates, correcting errors, and standardizing formats. This will help eliminate discrepancies and ensure uniformity across the dataset.
• Data Governance : Implement data governance policies and procedures to maintain data integrity and security. This may include access controls, data encryption, and regular audits.
• Continuous Monitoring : Continuously monitor data quality metrics and address any issues that arise promptly. This will help prevent data degradation over time and ensure your analyses are based on reliable information.

By prioritizing data quality assurance, you can enhance the accuracy and reliability of your ad hoc analyses, leading to more confident decision-making and better outcomes.

How to Perform Ad Hoc Analysis?

Now that you've prepared your data and defined your objectives, it's time to conduct ad hoc analysis. This involves selecting appropriate analytical techniques, exploring your data, applying advanced statistical methods, visualizing your findings, and validating hypotheses.

Choosing Analytical Techniques

Selecting the proper analytical techniques is crucial for extracting meaningful insights from your data.

• Nature of the Data : Assess the nature of your data, including its structure, size, and complexity. Different techniques may be more suitable for structured versus unstructured data or small versus large datasets.
• Objectives of Analysis : Align the choice of techniques with your analysis objectives. Are you trying to identify patterns, relationships, anomalies, or trends? Choose techniques that are well-suited to address your specific questions.
• Expertise and Resources : Consider your team's knowledge and the availability of resources, such as computational power and software tools. Choose techniques that your team is comfortable with and that can be executed efficiently.

Standard analytical techniques include descriptive statistics, inferential statistics, machine learning algorithms, and data mining techniques.

Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) is a critical step in ad hoc analysis that involves uncovering patterns, trends, and relationships within your data. Here's how to approach EDA:

• Summary Statistics : Calculate summary statistics such as mean, median, mode, variance, and standard deviation to understand the central tendencies and variability of your data.
• Data Visualization : Visualize your data using charts, graphs, and plots to identify patterns and outliers. Popular visualization techniques include histograms, scatter plots, box plots, and heat maps .
• Correlation Analysis : Explore correlations between variables to understand how they are related to each other. Use correlation matrices and scatter plots to visualize relationships.
• Dimensionality Reduction : If working with high-dimensional data, consider using dimensionality reduction techniques such as principal component analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE) to visualize and explore the data in lower dimensions.

Advanced Statistical Methods

For more in-depth analysis, consider applying advanced statistical methods to your data. These methods can help uncover hidden insights and relationships. Some advanced statistical methods include:

• Regression Analysis : Use regression analysis to model the relationship between dependent and independent variables. Linear regression, logistic regression, and multivariate regression are common techniques.
• Hypothesis Testing : Conduct hypothesis tests to assess the statistical significance of observed differences or relationships. Standard tests include t-tests, chi-square tests, ANOVA, and Mann-Whitney U tests.
• Time Series Analysis : If working with time series data, apply time-series analysis techniques to understand patterns and trends over time. This may involve methods such as autocorrelation, seasonal decomposition, and forecasting.

Data Visualization

Visualizing your findings is essential for communicating insights effectively to stakeholders.

• Choose the Right Visualizations : Select visualizations that best represent your data and convey your key messages. Consider factors such as the type of data, the relationships you want to highlight, and the audience's preferences .
• Use Clear Labels and Titles : Ensure that your visualizations are easy to interpret by using clear labels, titles, and legends. Avoid clutter and unnecessary decorations that may distract from the main message.
• Interactive Visualizations : If possible, create interactive visualizations allowing users to explore the data interactively. This can enhance engagement and enable users to gain deeper insights by drilling down into specific data points.
• Accessibility : Make your visualizations accessible to all users, including those with visual impairments. Use appropriate color schemes, font sizes, and contrast ratios to ensure readability.

Iterative Approach and Hypothesis Testing

Adopting an iterative approach to analysis allows you to refine your hypotheses and validate your findings through hypothesis testing.

• Formulate Hypotheses : Based on your initial explorations, formulate hypotheses about the relationships or patterns in the data that you want to test.
• Design Experiments : Design experiments or tests to evaluate your hypotheses. This may involve collecting additional data or conducting statistical tests.
• Evaluate Results : Analyze the results of your experiments and assess whether they support or refute your hypotheses. Consider factors such as statistical significance , effect size, and practical significance.
• Iterate as Needed : If the results are inconclusive or unexpected, iterate on your analysis by refining your hypotheses and conducting further investigations. This iterative process helps ensure that your conclusions are robust and reliable.

By following these steps and techniques, you can perform ad hoc analysis effectively, uncover valuable insights, and make informed decisions based on data-driven evidence.

Ad Hoc Analysis Examples

To better understand how ad hoc analysis can be applied in real-world scenarios, let's explore some examples across different industries and domains:

1. Marketing Campaign Optimization

Imagine you're a marketing analyst tasked with optimizing a company's digital advertising campaigns . Through ad hoc analysis, you can delve into various metrics such as click-through rates, conversion rates, and return on ad spend (ROAS) to identify trends and patterns. For instance, you may discover that certain demographic segments or ad creatives perform better than others. By iteratively testing and refining different campaign strategies based on these insights, you can improve overall campaign performance and maximize ROI.

2. Supply Chain Optimization

In the realm of supply chain management, ad hoc analysis can play a critical role in identifying inefficiencies and optimizing processes. For example, you might analyze historical sales data, inventory levels, and production schedules to identify bottlenecks or excess inventory. Through exploratory analysis, you may uncover seasonal demand patterns or supply chain disruptions that impact operations. Armed with these insights, supply chain managers can make data-driven decisions to streamline operations, reduce costs, and improve customer satisfaction.

3. Financial Risk Assessment

Financial institutions leverage ad hoc analysis to assess and mitigate various types of risks, such as credit risk, market risk, and operational risk. For example, a bank may analyze loan performance data to identify factors associated with loan defaults or delinquencies. By applying advanced statistical methods such as logistic regression or decision trees , analysts can develop predictive models to assess creditworthiness and optimize lending strategies. This enables banks to make informed decisions about loan approvals, pricing, and risk management.

4. Retail Merchandising Analysis

In the retail industry, ad hoc analysis is used to optimize merchandising strategies, pricing decisions, and inventory management. Retailers may analyze sales data, customer demographics , and market trends to identify product preferences and purchasing behaviors . Through segmentation analysis, retailers can tailor their merchandising efforts to specific customer segments and optimize product assortments. By monitoring key performance indicators (KPIs) such as sell-through rates and inventory turnover, retailers can make data-driven decisions to maximize sales and profitability.

How to Report Ad Hoc Analysis Findings?

After conducting ad hoc analysis, effectively communicating your findings is essential for driving informed decision-making within your organization. Let's explore how to structure your report, interpret and communicate results, tailor reports to different audiences, incorporate visual aids, and document methods and assumptions.

1. Structure the Report

Structuring your report in a clear and logical manner enhances readability and ensures that your findings are presented in a cohesive manner.

• Executive Summary : Provide a brief overview of your analysis, including the objectives, key findings, and recommendations. This section should concisely summarize the main points of your report.
• Introduction : Introduce the purpose and scope of the analysis, as well as any background information or context that is relevant to understanding the findings.
• Methodology : Describe the methods and techniques used in the analysis, including data collection , analytical approaches, and any assumptions made.
• Findings : Present the main findings of your analysis, organized in a logical sequence. Use headings, subheadings, and bullet points to enhance clarity and readability.
• Discussion : Interpret the findings in the context of the objectives and provide insights into their implications. Discuss any patterns, trends, or relationships observed in the data.
• Recommendations : Based on the analysis findings, provide actionable recommendations. Clearly outline the steps to address any issues or capitalize on opportunities identified.
• Conclusion : Summarize the main findings and recommendations, reiterating their importance and potential impact on the organization.
• References : Include a list of references or citations for any sources of information or data used in the analysis.

2. Interpret and Communicate Results

Interpreting and communicating the results of your analysis effectively is crucial for ensuring that stakeholders understand the implications and can make informed decisions.

• Use Plain Language : Avoid technical jargon and complex terminology that may confuse or alienate non-technical stakeholders. Use plain language to explain concepts and findings in a clear and accessible manner.
• Provide Context : Help stakeholders understand the significance of the findings by providing relevant context and background information. Explain why the analysis was conducted and how the findings relate to broader organizational goals or objectives.
• Highlight Key Insights : Focus on the most important insights and findings rather than overwhelming stakeholders with excessive detail. Use visual aids, summaries, and bullet points to highlight key takeaways.
• Address Implications : Discuss the implications of the findings and their potential impact on the organization. Consider both short-term and long-term implications and any risks or uncertainties.
• Encourage Dialogue : Foster open communication and encourage stakeholders to ask questions and seek clarification. Be prepared to engage in discussions and provide additional context or information as needed.

3. Tailor Reports to Different Audiences

Different stakeholders may have varying levels of expertise and interests, so it's essential to tailor your reports to meet their specific needs and preferences.

• Executive Summary for Decision Makers : Provide a concise executive summary highlighting key findings and recommendations for senior leaders and decision-makers who may not have time to review the full report.
• Detailed Analysis for Analysts : Include more thorough analysis, methodologies , and supporting data for analysts or technical stakeholders who require a deeper understanding of the analysis process and results.
• Customized Dashboards or Visualizations : Create customized dashboards or visualizations for different audiences, allowing them to interact with the data and explore insights relevant to their areas of interest.
• Personalized Presentations : Deliver personalized presentations or briefings to different stakeholder groups, focusing on the aspects of the analysis most relevant to their roles or responsibilities.

By tailoring your reports to different audiences, you can ensure that each stakeholder receives the information they need in a meaningful and actionable format.

4. Incorporate Visual Aids

Visual aids such as charts, graphs, and diagrams can enhance the clarity and impact of your reports by making complex information more accessible and engaging.

• Choose Appropriate Visualizations : Select visualizations that best represent the data and convey the key messages of your analysis. Choose from various chart types, including bar charts, line charts, pie charts, scatter plots, and heat maps.
• Simplify Complex Data : Use visualizations to simplify complex data and highlight trends, patterns, or relationships. Avoid clutter and unnecessary detail that may detract from the main message.
• Ensure Readability : Use clear labels, titles, and legends to ensure that visualizations are easy to read and interpret. Use appropriate colors, fonts, and formatting to enhance readability and accessibility.
• Use Interactive Features : If possible, incorporate interactive features into your visualizations that allow stakeholders to explore the data further. This can enhance engagement and enable stakeholders to gain deeper insights by drilling down into specific data points.
• Provide Context : Provide context and annotations to help stakeholders understand the significance of the visualizations and how they relate to the analysis objectives.

By incorporating visual aids effectively, you can make your reports more engaging and persuasive, helping stakeholders better understand and act on the findings of your analysis.

5. Document Methods and Assumptions

Documenting the methods and assumptions used in your analysis is essential for transparency and reproducibility. It allows stakeholders to understand how the findings were obtained and evaluate their reliability.

• Describe Data Sources and Collection Methods : Provide details about the sources of data used in the analysis and the methods used to collect and prepare the data for analysis.
• Explain Analytical Techniques : Describe the analytical techniques and methodologies used in the analysis, including any statistical methods, algorithms, or models employed.
• Document Assumptions and Limitations : Clearly state any assumptions made during the analysis, as well as any limitations or constraints that may impact the validity of the findings. Be transparent about the uncertainties and risks associated with the analysis.
• Provide Reproducible Code or Scripts : If applicable, provide reproducible code or scripts that allow others to replicate the analysis independently. This can include programming code, SQL queries, or data manipulation scripts.
• Include References and Citations : Provide references or citations for any external sources of information or data used in the analysis, ensuring that proper credit is given and allowing stakeholders to access additional information if needed.

By documenting methods and assumptions thoroughly, you can build trust and credibility with stakeholders and facilitate collaboration and knowledge sharing within your organization.

Ad Hoc Analysis Best Practices

Performing ad hoc analysis effectively requires a combination of skills, techniques, and strategies. Here are some best practices and tips to help you conduct ad hoc analysis more efficiently and derive valuable insights:

• Define Clear Objectives : Before analyzing the data, clearly define the objectives and questions you seek to answer. This will help you focus your efforts and ensure that you stay on track.
• Start with Exploratory Analysis : Begin your analysis with exploratory techniques to gain an initial understanding of the data and identify any patterns or trends. This will provide valuable insights that can guide further analysis.
• Iterate and Refine : Adopt an iterative approach to analysis, refining your hypotheses and techniques based on initial findings. Be open to adjusting your approach as new insights emerge.
• Leverage Diverse Data Sources : Tap into diverse data sources to enrich your analysis and gain comprehensive insights. Consider both internal and external sources of data that may provide valuable context or information.
• Maintain Data Quality : Prioritize data quality assurance throughout the analysis process, ensuring your findings are based on accurate, reliable data. Cleanse, validate, and verify the data to minimize errors and inconsistencies.
• Document Processes and Assumptions : Document the methods, assumptions, and decisions made during the analysis to ensure transparency and reproducibility. This will facilitate collaboration and knowledge sharing within your organization.
• Communicate Findings Effectively : Use clear, concise language to communicate your findings and recommendations to stakeholders. Tailor your reports and presentations to the needs and preferences of different audiences.
• Stay Curious and Open-Minded : Approach ad hoc analysis with curiosity and an open mind, remaining receptive to unexpected insights and discoveries. Embrace uncertainty and ambiguity as opportunities for learning and exploration.
• Seek Feedback and Collaboration : Solicit feedback from colleagues, mentors, and stakeholders throughout the analysis process. Collaboration and peer review can help validate findings and identify blind spots or biases.
• Continuously Learn and Improve : Invest in ongoing learning and professional development to expand your analytical skills and stay abreast of emerging trends and techniques in data analysis.

Ad Hoc Analysis Challenges

While ad hoc analysis offers numerous benefits, it also presents unique challenges that analysts must navigate. Here are some common challenges associated with ad hoc analysis:

• Data Quality Issues : Poor data quality, including missing values, errors, and inconsistencies, can hinder the accuracy and reliability of ad hoc analysis results. Addressing data quality issues requires careful data cleansing and validation.
• Time Constraints : Ad hoc analysis often needs to be performed quickly to respond to immediate business needs or opportunities. Time constraints can limit the depth and thoroughness of analysis, requiring analysts to prioritize key insights.
• Resource Limitations : Limited access to data, tools, or expertise can pose challenges for ad hoc analysis. Organizations may need to invest in training, infrastructure, or external resources to support effective analysis.
• Complexity of Unstructured Data : Dealing with unstructured or semi-structured data, such as text documents or social media feeds, can be challenging. Analysts must employ specialized techniques and tools to extract insights from these data types.
• Overcoming Analytical Bias : Analysts may inadvertently introduce biases into their analysis, leading to skewed or misleading results. It's essential to remain vigilant and transparent about potential biases and take steps to mitigate them.

By recognizing and addressing these challenges, analysts can enhance the effectiveness and credibility of their ad hoc analysis efforts, ultimately driving more informed decision-making within their organizations.

Conclusion for Ad Hioc Analysis

Ad hoc analysis is a versatile tool that empowers organizations to navigate the complexities of data and make informed decisions quickly. By enabling analysts to explore data on demand, ad hoc analysis provides a flexible and adaptive approach to problem-solving, allowing organizations to respond effectively to changing circumstances and capitalize on opportunities. From marketing campaign optimization to supply chain management, healthcare outcomes analysis, financial risk assessment, and retail merchandising analysis, the applications of ad hoc analysis are vast and varied. By embracing the principles of ad hoc analysis and incorporating best practices into their workflows, organizations can unlock the full potential of their data and drive business success. In today's data-driven world, the ability to extract actionable insights from data is more critical than ever. Ad hoc analysis offers a pathway to deeper understanding and better decision-making, enabling organizations to stay agile, competitive, and resilient in the face of uncertainty. By harnessing the power of ad hoc analysis, organizations can gain a competitive edge, optimize processes, mitigate risks, and uncover new opportunities for growth and innovation. As technology continues to evolve and data volumes grow exponentially, the importance of ad hoc analysis will only continue to increase. So, whether you're a seasoned data analyst or just beginning your journey into data analysis, embracing ad hoc analysis can lead to better outcomes and brighter futures for your organization.

How to Quickly Collect Data for Ad Hoc Analysis?

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• Lightning-fast Insights:  From questions to insights in mere minutes, Appinio accelerates the pace of ad hoc analysis, ensuring you get the answers you need precisely when you need them.
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Unveiling the Power of Ad Hoc Analysis: A Comprehensive Guide

Introduction

In the ever-evolving landscape of data analytics, the concept of ad hoc analysis stands as a dynamic catalyst for informed decision-making. Ad hoc analysis represents a departure from traditional, structured data examinations, offering the freedom to explore and derive insights on the fly. This real-time, impromptu approach enables professionals at all levels to interact with data intuitively, fostering a more responsive and agile decision-making process. In a world where business landscapes change swiftly, ad hoc analysis serves as a valuable tool for identifying trends, anomalies, and emerging opportunities. This article embarks on a comprehensive exploration of ad hoc analysis, delving into its fundamental principles, key components, and the manifold benefits it brings to organizations. By understanding the significance of ad hoc analysis and its transformative impact on user empowerment and rapid decision-making, businesses can unlock new dimensions of analytical capabilities, ensuring they stay ahead in an increasingly data-centric world. Join us as we unravel the layers of ad hoc analysis, navigating its applications, best practices, and the promising future it holds in the realm of data-driven decision-making.

Understanding Ad Hoc Analysis

At the core of modern data analytics, Ad Hoc Analysis emerges as a dynamic and indispensable tool, providing organizations with the agility to respond to ever-changing data landscapes. Ad Hoc Analysis is essentially an on-the-fly approach to data exploration, allowing users to conduct impromptu analyses without relying on pre-determined queries or structured reports. Its significance in data analysis lies in its ability to accommodate the unpredictable nature of business questions, facilitating real-time insights and informed decision-making.

Traditional data analysis methods often involve predefined queries and structured reports, limiting the flexibility to adapt to emerging trends or unexpected patterns. Ad Hoc Analysis, on the other hand, offers a dynamic environment where users can explore data interactively, posing questions and uncovering insights in real-time. This adaptability is crucial in situations where immediate decisions are required or when dealing with rapidly evolving data scenarios.

The importance of Ad Hoc Analysis is underscored by its empowerment of users at all levels within an organization. By offering a user-friendly interface and intuitive tools, individuals across various departments can independently analyze data, reducing dependence on dedicated, data analysts and teams. This democratization of data analysis enhances organizational responsiveness and ensures that decision-makers have the freedom to explore and extract insights without the constraints of predefined structures. In essence, Ad Hoc Analysis stands as a linchpin in the data analytics toolkit, championing a dynamic, user-centric, and real-time approach to uncovering actionable insights.

Key Components of Ad Hoc Analysis

Flexibility in manipulating data:.

The efficacy of Ad Hoc Analysis lies in its key components that contribute to a dynamic and user-driven approach to data exploration. At the forefront is the unparalleled flexibility it provides in manipulating and analyzing data. Unlike traditional hoc reporting and analysis methods that adhere to rigid structures, Ad Hoc Analysis allows users to interactively manipulate data, tailor analyses to specific questions, and adjust parameters on the fly. This flexibility ensures that users can adapt their analytical approach to the ever-evolving nature of business data, fostering a more responsive decision-making process.

Real-Time Exploration and Analysis:

Real-time exploration and analysis constitute another crucial component of Ad Hoc Analysis. In a rapidly changing business environment, the ability to derive insights in real-time is paramount. Ad Hoc Analysis facilitates this by allowing business users to explore data dynamically as it is generated, ensuring that organizations can respond swiftly to emerging trends, identify anomalies, and seize opportunities promptly.

User Empowerment Across the Organization:

Moreover, Ad Hoc Analysis stands out for its capacity to empower users at all levels within an organization. The tools associated with Ad Hoc Analysis often boast user-friendly interfaces and intuitive features, enabling individuals across various departments to independently analyze data without necessitating advanced technical skills. This democratization of data analysis not only reduces the burden on dedicated data teams but also ensures that decision-makers at different organizational levels have the autonomy to extract valuable insights, promoting a culture of data-driven decision-making throughout the organization. As a result, Ad Hoc Analysis stands as a cornerstone, fostering adaptability, responsiveness, and user empowerment in the data analytics landscape.

Benefits of Ad Hoc Analysis

Rapid decision-making:.

Ad hoc analysis emerges as a linchpin in facilitating rapid decision-making, offering a swift and responsive mechanism for professionals to adapt to changing scenarios. In dynamic environments where market conditions, consumer preferences, or internal factors evolve swiftly, the ability to quickly analyze and interpret data becomes paramount. Ad hoc analysis enables decision-makers to promptly access insights, empowering them to make informed choices on the spot without waiting for pre-structured reports or analyses.

Customized Insights:

A significant advantage of ad hoc analysis lies in its capacity to provide customized insights tailored to specific questions or scenarios. Unlike standardized reports that may not address niche inquiries, ad hoc analysis allows users to frame questions dynamically, ensuring that the analyses generated are directly relevant to the unique needs of the moment. This customization enhances the precision and applicability of the insights derived, supporting decision-makers in gaining a nuanced understanding of the data at hand.

Identifying Trends and Anomalies:

Ad hoc analysis serves as a proactive tool for identifying both trends and anomalies within datasets. The real-time exploration capability enables users to spot emerging patterns or irregularities that might go unnoticed in traditional reporting structures. This anticipatory approach allows organizations to stay ahead of trends, capitalize on emerging opportunities, and address anomalies before they escalate, contributing to a more resilient and foresighted decision-making process.

Reduced Dependence on IT:

Ad hoc analysis tools often boast user-friendly interfaces that empower non-technical users to conduct analyses independently. This reduction in dependence on IT teams streamlines the decision-making process, enabling professionals from various departments to explore and derive insights without requiring extensive technical skills. The democratization of data analysis through intuitive interfaces enhances organizational agility, fostering a culture where data-driven decision-making is accessible to a broader spectrum of users.

Examples of Ad Hoc Analysis in Action:

Real-world scenarios:.

Ad hoc analysis has proven invaluable in numerous real-world scenarios, showcasing its adaptability and effectiveness across diverse industries. In the financial sector, for instance, investment analysts often utilize ad hoc analysis to quickly respond to market fluctuations. By dynamically exploring data, they can make timely investment decisions, adapting to changing economic conditions and staying ahead of market trends. In the healthcare industry, ad hoc analysis plays a crucial role in patient care and resource allocation. Healthcare professionals use on-the-fly analyses to identify patterns in patient data, allowing for personalized treatment plans and more efficient use of medical resources. During public health crises, such as a pandemic, ad hoc analysis becomes instrumental in tracking the spread of diseases, predicting hotspots, and allocating resources strategically.

Industries and Use Cases:

Several industries benefit significantly from the flexibility and immediacy of ad hoc analysis. In retail, for instance, ad hoc analysis helps optimize inventory management by quickly identifying product trends and adjusting stock levels accordingly. E-commerce platforms leverage this approach to analyze customer behavior in real-time, enhancing personalized recommendations and improving the overall shopping experience.

The telecommunications sector relies on ad hoc analysis to monitor network performance and identify potential issues swiftly. Telecom operators can analyze data on-the-fly to optimize network resources, ensuring seamless connectivity and addressing disruptions promptly. Similarly, in manufacturing, ad hoc analysis aids in quality control by enabling real-time monitoring of production processes and identifying deviations that may affect product quality.

In the technology industry, especially in software development, ad hoc analysis is employed to identify bugs, optimize code performance, and make swift adjustments during the development process. The ability to analyze data dynamically ensures a more agile and responsive approach to software development, leading to faster problem resolution and product improvements.

These examples underscore the versatility of ad hoc analysis, demonstrating its applicability in enhancing decision-making and efficiency across a spectrum of industries and use cases.

Challenges and Considerations

Challenges associated with ad hoc analysis:.

Despite its numerous benefits, ad hoc analysis is not without its challenges. One significant challenge is the potential for data inconsistency and accuracy issues. Since ad hoc analyses often involve quick, on-the-fly exploration, there is a risk of overlooking data quality, leading to erroneous conclusions. Additionally, the lack of predefined structures may result in varied interpretations of the same dataset, posing challenges in maintaining consistency across analyses. Security concerns also arise, as ad hoc analyses may involve sensitive or confidential data, necessitating robust access controls to prevent unauthorized access and data breaches.

Considerations for Effective Implementation:

To maximize the benefits of ad hoc analysis while mitigating challenges, certain considerations are crucial for effective implementation. Establishing clear guidelines and best practices for ad hoc analysis is essential to maintain consistency and accuracy. Organizations should prioritize data governance, ensuring that data quality and security measures are upheld during impromptu analyses. Providing adequate training for users, especially those without a strong background in data analysis, is vital for fostering a data-literate culture and preventing misinterpretations. Collaborative platforms that enable sharing and documentation of ad hoc reports and analyses can enhance transparency and communication within the organization.

Moreover, organizations must strike a balance between flexibility and control by implementing governance frameworks that guide users in their ad hoc analyses while allowing for innovation. Regularly reviewing and updating data policies, security protocols, and analysis guidelines will ensure that ad hoc analysis of company data remains a valuable and risk-mitigated tool in the organization's decision-making arsenal.

Introduction to Ad Hoc Analysis Tools and Technologies:

As the demand for dynamic, on-the-fly data exploration rises, a variety of tools and technologies have emerged, each designed to facilitate impromptu analyses and empower users at various technical skill levels. Among these, Sprinkle Data stands out as a powerful and versatile solution, offering innovative features alongside other popular tools in the ad hoc analysis space.

Popular Tools for Ad Hoc Analysis

• Sprinkle Data:
• Sprinkle Data stands as a leading player in the ad hoc analysis arena, known for its user-friendly interface and robust functionality.
• With Sprinkle Data, users can effortlessly navigate and explore data in real-time, leveraging features that facilitate quick insights and informed decision-making.
• Its intuitive design allows for seamless ad hoc analyses, making it accessible to both technical and non-technical users.
• Tableau's reputation for an intuitive interface extends to ad hoc analysis, providing users with drag-and-drop capabilities for dynamic visualizations.
• Renowned for its visualization prowess, Tableau enables users to create interactive analyses effortlessly.
• Microsoft's Power BI is a versatile tool for ad hoc analysis, featuring natural language querying and integration with various Microsoft applications.
• Its robust suite of tools facilitates dynamic data exploration, enhancing the overall ad hoc analysis experience.
• Google Data Studio:
• Google Data Studio is celebrated for its simplicity and collaborative features, allowing users to create, customize, and share reports and dashboards effortlessly.
• Seamless integration with other Google services contributes to a user-friendly environment for ad hoc analysis.

Features Enhancing User Experience:

• Drag-and-Drop Interfaces:
• Common to many ad hoc analysis tools, drag-and-drop interfaces simplify data manipulation and dynamic visualization creation, reducing the need for complex coding.
• Natural Language Processing (NLP):
• Tools with NLP capabilities, including Sprinkle Data, enable users to interact with data using plain language, enhancing accessibility for non-technical users.
• Collaboration and Sharing:
• Robust collaboration features in these tools, such as shared workspaces and real-time collaboration, promote teamwork and contribute to a more agile decision-making process.
• Data Connectivity:
• Ad hoc analysis tools, including Sprinkle Data, often support connectivity to a diverse range of data sources, ensuring users can analyze information from various channels.

As organizations navigate the complexities of data-driven decision-making, the landscape of ad hoc reporting and analysis tools continues to evolve, with a collective focus on enhancing usability, collaboration, and the overall user experience.

Best Practices for Ad Hoc Analysis:

Tips for effective ad hoc analysis:.

• Define Clear Objectives:
• Begin by clearly defining the objectives of your ad hoc analysis. Clearly articulate the questions you seek to answer or the insights you aim to uncover. This focused approach ensures that your analysis remains purposeful and aligned with your goals.
• Start Simple:
• Begin with simple analyses before diving into complex queries. Gradually refine your approach based on the insights gained. This iterative process allows for a more thorough understanding of the data and prevents potential misinterpretations.
• Utilize Visualization Tools:
• Leverage visualization tools to represent data intuitively. Graphs, charts, and dashboards can enhance comprehension and aid in identifying patterns or outliers more efficiently. Tools like Sprinkle Data, Tableau, or Power BI offer robust visualization features.
• Regularly Save and Document:
• Save your analyses regularly and provide clear documentation. This ensures that insights are reproducible and shareable within your team. Documentation becomes crucial for future reference and contributes to a collaborative analytical environment.

Importance of Data Accuracy and Quality:

Ensure data consistency:.

Validate and ensure the consistency of your data sources. Discrepancies or inaccuracies in datasets can lead to unreliable conclusions. Regularly verify data integrity to maintain the accuracy of your ad hoc analyses.

Verify Data Sources:

Verify the credibility and reliability of your data sources. Relying on accurate and trustworthy data is fundamental for making informed decisions. Cross-checking data from multiple sources adds an extra layer of validation.

Implement Data Governance:

Establish robust, data management and governance practices to maintain high data quality. This involves defining data ownership, implementing data validation processes, and ensuring compliance with data quality standards.

Data Cleansing and Transformation:

Prioritize data cleansing and transformation processes to handle missing or inconsistent data. Addressing data quality issues at the preprocessing stage contributes to the reliability of your ad hoc analyses.

Emphasizing these best practices as needed basis for effective ad hoc analysis, coupled with a commitment to data accuracy and quality, establishes a solid foundation for organizations seeking to derive meaningful insights from their dynamic data environments. As the landscape of data analytics continues to evolve, adherence to these practices ensures that ad hoc analyses contribute significantly to informed decision-making processes.

Future Trends in Ad Hoc Analysis:

Emerging trends and advancements:.

Machine Learning Integration:

The integration of machine learning algorithms within ad hoc analysis tools is an emerging trend. This advancement allows systems to learn from user interactions, offering automated insights and predictive analytics as users navigate through the data dynamically.

Natural Language Processing (NLP) Enhancements:

NLP capabilities are expected to undergo significant enhancements. Future ad hoc analysis tools may feature more sophisticated NLP, enabling users to interact with data using even more natural and context-aware language, making it accessible to a broader range of users.

Augmented Analytics:

Augmented analytics, combining machine learning and AI-driven insights, is poised to transform ad hoc analysis. These tools will proactively assist users in formulating queries, interpreting results, and suggesting relevant visualizations, making the analytical process more intuitive and efficient.

Evolution of the Landscape:

Increased Integration with Big Data Platforms:

As organizations continue to leverage big data, ad hoc analysis tools are likely to integrate more seamlessly with big data platforms. This evolution ensures that users can explore and analyze vast datasets efficiently, unlocking insights from diverse and complex data sources. Enhanced Collaboration Features:

The future of ad hoc analysis will see a heightened emphasis on collaboration features. Real-time collaborative environments will become more sophisticated, allowing teams to work together seamlessly on ad hoc analyses, fostering collective decision-making.

Advancements in Data Visualization:

The evolution of data visualization techniques will play a pivotal role. Ad hoc analysis tools will likely incorporate more advanced visualization options, including augmented reality (AR) and immersive data experiences, providing users with novel ways to interpret and communicate insights.

Greater Automation for Routine Tasks:

Routine and repetitive tasks in ad hoc analysis, such as data cleaning and basic exploratory analyses, are expected to become more automated. This allows users to focus on more complex and strategic aspects of the analysis, enhancing overall productivity.

As ad hoc analysis becomes increasingly integral to organizational decision-making, these emerging trends and advancements signify a future where the ad hoc report process is not only more sophisticated but also more accessible and collaborative. The evolving landscape promises a more intelligent, automated, and user-friendly ad hoc analysis experience, empowering organizations to glean deeper insights from their data.

Conclusion:

In the dynamic landscape of data analysis, this exploration into ad hoc analysis has revealed its pivotal role in reshaping the way organizations extract insights and make informed decisions. The ability to conduct impromptu, on-the-fly analyses emerged as a powerful tool, providing users across various industries with which analysis tools offer unprecedented flexibility and responsiveness.

In summarizing the key points, we began by defining ad hoc analysis, highlighting its dynamic nature that sets it apart from traditional, predefined approaches. The discussion then unfolded to showcase real-world scenarios where ad hoc analysis proved instrumental, emphasizing its effectiveness in diverse industries, from finance and healthcare to retail and telecommunications.

The many benefits of ad hoc analysis, from rapid decision-making and customized insights to identifying trends and reducing dependence on IT, underscored its transformative impact on organizational agility. We explored popular tools like Sprinkle Data, Tableau, Power BI, and Google Data Studio, noting how their features enhance the user experience, making ad hoc analysis accessible to both technical and non-technical users.

Delving into challenges and considerations, we acknowledged potential hurdles while providing insights into mitigating risks and ensuring effective implementation of reporting solutions. Best practices for ad hoc analysis, focusing on clear objectives, starting simple, and emphasizing data accuracy, offered practical guidance for users navigating the dynamic data landscape.

Looking towards the future, we identified emerging trends like machine learning integration, enhanced NLP capabilities, and augmented analytics, forecasting a landscape where ad hoc analysis and business intelligence become more sophisticated, collaborative, and automated.

In conclusion, ad hoc analysis stands as a cornerstone in the data-driven era, empowering organizations to navigate complexities, respond swiftly to challenges, and seize opportunities. Its significance lies not just in the analyses it produces, but in the agility, it brings to decision-making processes, ensuring organizations remain adaptive and thrive in an ever-evolving business environment. As the data analytics landscape continues to evolve, ad hoc analysis remains a key protagonist, promising continued innovation and transformative insights for those who harness its capabilities effectively.

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Understanding manufacturing repurposing: a multiple-case study of ad hoc healthcare product production during COVID-19

• Open access
• Published: 28 July 2022
• Volume 15 , pages 1257–1269, ( 2022 )

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• Wan Ri Ho   ORCID: orcid.org/0000-0003-2540-0732 1 ,
• Omid Maghazei   ORCID: orcid.org/0000-0002-2257-3550 1 &
• Torbjørn H. Netland   ORCID: orcid.org/0000-0001-7382-1051 1  

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The repurposing of manufacturing facilities has provided a solution to the surge in demand for healthcare products during the COVID-19 pandemic. Despite being a widespread and important phenomenon, manufacturing repurposing has received scarce research. This paper develops a grounded understanding of the key factors that influence manufacturing repurposing at the macro and micro levels. We collected rich qualitative data from 45 case studies of firms’ repurposing initiatives during COVID-19. Our study focuses on four types of healthcare products that experienced skyrocketing demand during the first months of the COVID-19 pandemic: face shields, facemasks, hand sanitizers, and medical ventilators. Based on the case studies, we identify and generalize driving factors for manufacturing repurposing and their relationships, which are summarized in causal loop diagrams at both macro and micro levels. Our research provides practitioners, policymakers, and scholars with a conceptual understanding of the phenomenon of manufacturing repurposing. It helps manufacturing managers understand why, when, and how they should engage in manufacturing repurposing and informs policymakers when and how to tailor incentive policies and support schemes to changing situations. Scholars can build on our work to develop and test dynamic system–behavior models of the phenomenon or to pursue other research paths we discover. The world stands to benefit from improved manufacturing repurposing capabilities to be better prepared for future disruptions.

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1 Introduction

As the COVID-19 pandemic swept across the world in 2020, the demand for particular healthcare equipment skyrocketed far beyond the level of any safety stock (Hald and Coslugeanu 2021 ). Manufacturing repurposing has been considered a rapid response to addressing the global shortage of critical items during the COVID-19 pandemic (Joglekar et al. 2020 ; López-Gómez et al. 2020 ). Manufacturers from different industries engaged in manufacturing repurposing either to gain goodwill or to capture the business opportunities presented (Betti and Heinzmann 2020 ; López-Gómez et al. 2020 ). Firms have particularly started to produce personal protective equipment (PPE) or medical equipment products. For example, beer manufacturer BrewDog began producing hand sanitizers, sports car manufacturer Ferrari manufactured respirator valves, and luxury label Prada made facemasks (Netland 2020 ; Garza-Reyes et al. 2021 ). In other cases, firms sought to fight the pandemic by inventing new products (e.g., modified scuba masks for ventilators and hygienic surgical gowns) or finding new ways to use novel technologies (e.g., collaborative robots and additive manufacturing) (Malik et al. 2020 ). However, these efforts have raised substantial challenges and risks (Garza-Reyes et al. 2021 ). For example, there has been high uncertainty related to the dynamics of the pandemic, and firms have generally lacked experience in repurposing manufacturing. During the past two years, the scale and scope of manufacturing repurposing have been unprecedented, which raises many intriguing questions for research.

We define manufacturing repurposing as a firm’s rapid conversion of capacities and capabilities to produce new-to-the-firm products. Manufacturing repurposing has been a strong phenomenon in the industry during COVID-19, but it is almost entirely new to the literature. In particular, the literature on manufacturing repurposing in the context of pandemics is in its infancy (Garza-Reyes et al. 2021 ). Existing reports have barely started to explain why and how manufacturing firms repurposed to respond to COVID-19 (e.g., Ashforth 2020 ; Avery 2020 ; De Massis and Rondi 2020 ; George et al. 2020 ; Lawton et al. 2020 ; Shepherd 2020 ; Rouleau et al. 2021 ). In contrast, there is already extensive literature on the effects of COVID-19 on existing operations and supply chains (Phillips et al. 2022 ; Barbieri et al. 2020 ; Naz et al. 2021 ; Reed 2021 ; Yu et al. 2021 ). Therefore, there should be ample opportunity to contribute new insights into manufacturing repurposing, considering the extent and variety of repurposing during the COVID-19 pandemic. In this paper, we ask the following macro-and micro-level research questions: What factors affect manufacturing repurposing activities, and how do they relate to each other?

Addressing our research question, we contribute an understanding of manufacturing repurposing and the factors that affect its dynamic development. As one of the first multi-case empirical analyses of manufacturing repurposing, this is a novel contribution to the emerging literature. We use a multiple case study approach to map and analyze a large number of manufacturing repurposing initiatives during the COVID-19 pandemic. We focus on healthcare products that experienced explosive demand growth during spring 2020, more precisely from March to June 2020, depending on location. After analyzing the data via structured coding methods, we visualized the system dynamics of manufacturing repurposing in causal loop diagrams at the macro and micro levels. By bringing forward the key constructs of manufacturing repurposing, we lay a foundation for future research. The causal loop diagrams also provide practical insights for practitioners and policymakers, which can help improve decision-making processes in future emergencies. We also elaborate on the challenges and opportunities of manufacturing repurposing and outline promising research avenues.

The remainder of this paper is structured as follows. Section  2 provides a literature review of manufacturing repurposing. Section  3 details the research methodology. Section  4 presents our structured qualitative analysis. Section  5 summarizes the results in the form of macro- and micro-level causal loop diagrams. Section  6 discusses the implications of this study for both research and practice as well as its limitations and outlook. Section  7 concludes the paper.

2 Literature review

Manufacturing repurposing is not a new phenomenon, but it lacks a dedicated and established stream of research. During all kinds of crises throughout history, humans have ingeniously developed ways to produce the needed products and tools to fix arising problems. During wartime, for example, repurposing production capacities to produce armory, ammunition, and other products in high demand is normal (e.g., Overy 1994 ). During natural disasters or other emergency events, local needs often require swift responses from local companies, which can help by producing products other than they normally do. For example, during the Aisin Seiki fire in the Kariya factory in 1997, Toyota’s supply of brake fluid valves was disrupted. This disruption drove Toyota to request other suppliers to repurpose production lines to produce valves for Toyota; within a short week, several companies began producing the needed valves. Manufacturing history is ripe with such stories, but they have not been studied collectively as phenomena. However, this is changing due to the unprecedented scale and scope of manufacturing repurposing the world has experienced during the COVID-19 pandemic.

During the recent pandemic, “manufacturing/production repurposing” has been used as a term to represent activities where a manufacturer uses its current capacities and capabilities to shift production to high-demand healthcare products like ventilators, facemasks, or sanitizers (e.g., Betti and Heinzmann  2020 ; López-Gómez et al. 2020 ). Scholars have picked up this term and studied the phenomenon using a variety of problem statements and approaches. The three dominant streams in the nascent literature have been: (1) barriers and success factors for successful repurposing, (2) supply chain issues, and (3) innovation.

Regarding the first stream of literature, Okorie et al. ( 2020 ) evaluate manufacturing repurposing as a firm-level pandemic response tool and identify enablers and barriers to repurposing. In particular, Okorie et al. ( 2020 ) recommend that manufacturing companies increase their flexibility, accelerate the adoption of digital technologies, and improve organizational processes such as decision making and organizational learning during pandemic and post-pandemic situations. The role of digital transformation in swift repurposing has also been emphasized by Soldatos et al. ( 2021 ). Relatedly, Poduval et al. ( 2021 ) use a model-based approach to identify and rank 11 types of barriers that played a central role in the repurposing of an existing manufacturing plant. Poduval et al. ( 2021 ) show that the identified barriers are interrelated and highlight the complexity of the manufacturing repurposing phenomenon. However, none of these studies have aimed to provide an understanding of all the internal and external factors that affect manufacturing repurposing and their causal relationships.

The second stream takes a supply chain perspective on manufacturing repurposing. For example, Falcone et al. ( 2022 ) use the concept of supply chain plasticity, which is defined as a firm’s “capability of rapidly making major changes to a supply chain to accommodate significant shifts in the business environment” (Zinn and Goldsby 2019 , p. 184). Falcone et al. ( 2022 ) argue that the more supply chain plasticity is developed in a firm, the more capability the firm has to repurpose existing operations during disruptions. Some industrial reports also extrapolated the repurposing concept to supply chains, which could increase resilience and social responsibility (e.g., see Accenture 2022 ). Such approaches allow mobilizing available resources in supply chains, similar to Toyota’s supply chain response during the Aisin Seiki fire (Nishiguchi and Beaudet 1998 ). Ivanov ( 2021 ) even suggests that repurposing could be used as an adaptation strategy to maintain supply chain viability during a crisis. While offering important contributions, the supply chain stream fails to capture the complex and interrelated system dynamics that occur between firms, their supply chains, and the external environment during manufacturing repurposing initiatives.

The third notable stream of research in the nascent literature on manufacturing repurposing focuses on innovation. For example, Liu et al. ( 2021a ) explore the effect of shared purpose in driving change in innovation processes and explain how design capability and manufacturing flexibility play key roles in accelerating innovation processes during disruptions. Focusing on the repurposing case of VentilatorChallengeUK, Liu et al. ( 2021b ) highlight open innovation, exaptation, Footnote 1 and ecosystem strategies during the rapid-scale-ups of ventilator production. Poduval et al. ( 2021 ) also point out that innovation is one of the main barriers. Relatedly Schwabe et al. ( 2021 ) provide a maturity model, which focuses on the speed of innovation diffusion from ideation to market saturation based on the repurposing and customization of existing mass manufacturing infrastructures during the COVID-19 pandemic. Innovation of products, processes, and organizations is key to successful repurposing, but it is not sufficient in its own right.

From the nascent but growing literature on manufacturing repurposing reviewed above, it is clear that it is a multifaceted, complex, and dynamic phenomenon. We aim to bring the facets together in a holistic understanding of the phenomenon. We empirically examine macro-and micro-level interactions within manufacturing repurposing projects, which we use to delineate dynamic cause-and-effect relationships that drive or slow down manufacturing repurposing.

3 Research method

We set out to build a grounded understanding of manufacturing repurposing. We used an inductive approach based on the systematic collection and analysis of data (Glaser and Strauss 1967 ; Gioia et al. 2013 ). To aid in collecting systematic, representative, and in-depth data, we turned to the rich methodological literature on case studies (e.g., Yin 1989 ; Voss et al. 2002 ). Case studies summarize insiders’ views of particular events to portray new insights, methods, or techniques. Figure  1 provides a high-level overview of our research process, and the details are explained in the following sections. Curved arrows represent iterations. This section explains the data collection process.

Flowchart of the research process

To increase internal validity, we narrowed down the focus of our study to four commonly repurposed products during COVID-19: face shields, face masks, hand sanitizers, and ventilators. The selected healthcare products were among those listed in the World Health Organization’s (WHO) technical guidance on essential resource planning during COVID-19 (WHO 2020 ). The unit of analysis was manufacturing repurposing operations in factories, including links to the internal and external stakeholders and partners involved, which provided the focal point of the research and served as the basis for sample selection.

To identify respondents, we used purposive and snowball sampling procedures, as explained by Miles and Huberman ( 1994 ). We explicitly sought a balanced sample that was not limited to only “successful” repurposing initiatives. The respondents were selected based on their direct involvement in producing these products. First, we focused on the most visible repurposing projects of the selected products in countless media reports and used social media platforms, such as LinkedIn or email, to contact the companies. Second, additional respondents were selected through a snowballing approach in which our primary respondents or contacted persons connected us with another potential respondent. We reached out to around 500 companies, of which about one in ten agreed to participate. In total, we interviewed 45 senior managers from 45 different companies. Semi-structured interviews were conducted from January 2021 to July 2021. The respondents’ profiles are summarized in Table 1 .

Due to travel restrictions during the pandemic, all interviews were conducted using videoconferencing. The interview questions were separated into two parts: the macro level of the supply chain and external issues and the micro level of firm repurposing operations (the interview guide is included in Appendix  A ). The semi-structured interviews lasted an average of 65 min. They were recorded, and the relevant content was transcribed. We have collected a qualitative database consisting of 915 pages (185,100 words). The interviews were carried out by two researchers, and the notes were cross-compared after the interviews. Internal reliability was improved by validating the transcribed reports with the informants.

To analyze the data, we used the Gioia method (see Gioia et al. 2013 ), which is an inductive approach that uses many iterations of analysis to arrive at higher-level concepts. We first carried out open coding with Maxqda software (Berlin, Germany). First- and second-order codes were assigned based on the in-vivo texts from the semi-structured interviews. This thematic coding was then discussed with the research team to reduce coding bias and improve the interpretation of the qualitative data. Second, for our higher-level constructs, we purposefully coded for context, antecedents, enablers, and barriers to manufacturing repurposing. As is common in qualitative research, these steps were iterative. We then conducted a within-case analysis and summarized each case along with the second-order codes. An example is shown in Appendix  B , split into the macro level of our analysis (Table B-1 Panel A) and the micro level (Table B-1 Panel B).

Once all cases were coded and described, the next step was a cross-case analysis. We used second-order codes from the interviews to build patterns of key constructs. To structure and present our findings, we applied a data visualization tool from system dynamics called causal loop diagrams (see Forrester 1994 ). This method was selected for its ability to model complex business decisions to form a structural and behavioral representation of the system (Forrester 1961 ; Sterman 2000 ). Causal loop diagrams map all essential relationships in a system. They show variables as texts, and the causal relationships among them are represented as arrows. We gradually built the causal loop diagrams through workshops, as we added case after case to the grounded emerging “story.” Consistent with our data, we developed two levels of causal loop diagrams to delineate the relationships between the factors involved externally (macro) and internally (micro) in the firm.

The causal loop diagrams in Figs.  2 and 3 show manufacturing repurposing from the macro-and micro-level perspectives, respectively.

Causal loop diagram of manufacturing repurposing at the macro-level. Notes: R Reinforcing loop, B  Balancing loop 

Causal loop diagram of manufacturing repurposing at the micro- level. Notes: R  Reinforcing loop, B  Balancing loop

5.1 Macro perspective on manufacturing repurposing

Figure  2 illustrates the system dynamics of manufacturing repurposing at the macro level. The pandemic provides an impulse to increase the market demand for specific products, which is represented by the reinforcing loop R1. Due to the surge in demand, the COVID-19 pandemic led to a supply shortage of PPE and clinical care equipment (CCE) in spring 2020, resulting in a reinforcing loop of supply shortages caused by the pandemic. Thus, a balancing loop was necessary to regulate supply shortages, leading to manufacturing repurposing activities (see balancing loop B1).

Manufacturing repurposing serves as a balancing mechanism for the overall system. It reduces market demand by producing the required PPE and CCE to meet the gaps created by the pandemic. Manufacturing repurposing was seen through various organizations venturing into new product development or collaborating to scale up a legacy product, as denoted by the open collaboration loop (B2). This collaboration played a pivotal role in enabling manufacturing to repurpose supply chains through open-source platforms (i.e., community efforts) . Two community effort examples include the WHO’s initiative to publish the formula for manufacturing hand sanitizers online and other firms’ and designers’ initiatives to make 3D printing designs available online for direct printing. This collaborative effort significantly accelerated the manufacturing repurposing process. Manufacturers can bypass the design phase and directly channel their resources into manufacturing and scaling up, resembling the open innovation practices introduced by Chesbrough ( 2003 ).

Finally, the reinforcing loop of enablers (R2) supports manufacturing repurposing. Due to the speed and scale at which manufacturing repurposing was required, funding from the government and third parties was imperative. Initiatives such as the VentilatorChallengeUK in the UK, “Ventilators for Canadians,” and “Mechanical Ventilator Milano” are examples of government involvement that supports manufacturing repurposing activities. Furthermore, emergency regulations (e.g., announcing a national “crisis mode” where certification requirements were temporarily lifted for specific products) accelerate the process of manufacturing repurposing by reducing or removing product approval procedures.

5.2 Micro perspective on manufacturing repurposing

The micro-level diagram in Fig.  3 focuses on manufacturing repurposing seen from an individual firm’s perspective. We identified that manufacturing repurposing was reinforced by three critical drivers, represented by market drivers (R3), process know-how (R4), and product know-how (R6), as well as three barriers, represented by facilities (B3), process barriers (R5), and product barriers (R7).

First, the market driver (R3) represents an opportunity for firms to gain either goodwill or business opportunities. It incentivizes organizations to venture into manufacturing repurposing. One of the main drivers for firms to engage in manufacturing repurposing was philanthropy—to help out during times of crisis with no apparent financial motive. Although capturing a new profitable business opportunity was mentioned as the motivation of a few firms, most firms claimed that they engaged in repurposing activities for humanitarian reasons. The role of humanitarian motivation is remarkably different from the usual profit-seeking behavior of companies and hence has important implications for subsequent theory development of manufacturing repurposing.

Second, the process know-how loop (R4) reinforces manufacturing repurposing. Here, existing know-how and project management skills help speed up new product development. For example, it was technologically easy for manufacturers of alcoholic beverages to shift their production capacity to making hand sanitizers. Firms also engaged in agile product development approaches to overcome uncertainty factors in product design requirements. Firms were also seen to apply lean management practices where continuous improvement was carried out to speed up the rate of production. Additionally, we observed that a flat organizational structure with high empowerment for decision making resulted in the effective speed and success of new product development. The speed of new product development was accelerated by applying project management software to monitor various stage-gate processes. The project management software application was seen as an effective tool for coping with the iterations required in response to design-requirement uncertainty. Another effective tool to accelerate processes was 3D printing for rapid prototyping during collaboration. Examples of 3D-printed products include face-shield holders and ventilator parts.

Third, process barriers (R5) represent the challenges that restrain product design capabilities, which impact the repurposing firms’ abilities. In our case studies, process complexities mainly affected the strength of the process barriers. Ventilators were more challenging to produce than the other three products studied, raising the bar for process entry barriers. This higher bar of entry was often overcome by the effect of a community or network approach. Philanthropic acts by both corporate and private entities significantly reduced process barriers by opening access to process designs and capacities. We also observed that firms with established systems and structures for new process establishment and improvement could better manage new product development, manufacturing, and distribution.

Another reinforcing loop concerns product know-how (R6). Product capabilities were critical to the firms as they developed a product that was new to the firm—while simultaneously being under the tremendous pressure of time and resources, as well as experiencing a global pandemic themselves. Consequently, the product know-how was critical to minimizing the number of iterations and errors involved. Overcoming the challenges of high product complexity requires know-how. The challenges were often seen to be reduced through the macro-level loop of community effort that compensates for the lack of internal resources (either tangible or intangible) for the success of repurposing manufacturing.

Next, product barriers (R7) were another roadblock experienced by many case companies. Product complexity reduces the speed of development. Complexities arise due to the nature of the product and the regulatory approvals involved. The complexity of the product is also increased by the number of parts required, along with the sourcing of the parts. During COVID-19, parts’ procurement was challenging, and lead times were long and unpredictable. Like process complexities, product barriers were reduced through community efforts. Capabilities, assets, and experiences were shared across the company and national borders.

Lastly, the facilities loop (B3) serves as a balancing loop for manufacturing repurposing. The need for capacity in the form of physical space, equipment, or resources was imperative due to the organizational changes and scale imposed by the pandemic. In some cases, this capacity was found in-house, and in other cases, the needed capacity was offered by partner firms. Some firms have turned warehouses into new manufacturing spaces for products. The ability to change the setup of current processes and facilities to meet the requirements of the new products also demanded skills and resources. Again, we observed the role of community efforts in contributing to the pool of available skills and resources for the required capacities.

6 Discussion

In this section, we discuss the implications of our study for both research and practice. First, our grounded approach led us to identify four theoretical concepts related to manufacturing repurposing, and we discuss these concepts and their implications for future research. Second, for practitioners and policymakers, we explain how this research can improve decision-making processes in preparation for and during future emergencies.

6.1 Implications for research

From our analysis, four theoretical concepts appear particularly relevant during manufacturing repurposing initiatives: (1) open innovation, (2) dynamic capabilities, (3) agile product development, and (4) corporate philanthropy in manufacturing repurposing. Below, we discuss how future research can advance manufacturing repurposing research by building on these concepts.

6.1.1 Open innovation

We observed a significant level of collaboration across the firm’s boundaries during manufacturing repurposing from our empirical results. For firms to successfully engage in manufacturing repurposing, capabilities (e.g., production know-how) or capacities were sourced beyond the firms’ boundaries. Sourcing beyond the firms’ boundaries was evident to a certain extent across all case companies, including different types and levels of collaboration widespread across the entire value chain processes. For instance, in one company, collaboration with another organization facilitated the development process of the ventilator. We also observed know-how sourcing from online communities in several of the case companies. Theoretically, this type of beyond-company-border information search and collaboration has been called “open innovation” (Chesbrough 2006 ). The application of open innovation accelerated the manufacturing repurposing activities of the firms. From an operations management perspective, we suggest further research on how firms can manage, implement, and assimilate external ideas into their operations during emergencies. A related and interesting question addresses the evolution of the newly established collaborations when crises pass. Researchers could also study the extent to which companies can benefit from open innovation practices to increase their preparedness for future manufacturing repurposing.

6.1.2 Dynamic capabilities

We found that companies benefited from product-and process-related routines. In particular, innovating quickly and implementing new product and process designs are essential during manufacturing repurposing initiatives. This ability has been called “dynamic capabilities” (Teece et al. 1997 ; Eisenhardt and Martin 2000 ). Dynamic capabilities are “the capacity to renew competences so as to achieve congruence with the changing business environment” by “appropriately adapting, integrating, and reconfiguring internal and external organizational skills, resources, and functional competencies to match the requirements of a changing environment” (i.e., capabilities) (Teece et al. 1997 , p. 515). The fit of this theory to manufacturing repurposing has already been demonstrated by Ramos et al. ( 2021 ) and Puliga and Ponta ( 2021 ). Future research could further explore the dynamic capabilities that companies can develop prior to emergencies, which can potentially be evoked and help improve manufacturing repurposing operations.

6.1.3 Agile product development

Our empirical evidence repeatedly points to the application of agile product/hardware development. Agile product development involves a quick method of product development in which sub-solutions are developed and tested iteratively with the customer (Takeuchi and Nonaka 1986 ). Agile product development revolves around six characteristics: built-in instability, self-organizing project teams, overlapping development phases, multi-learning, subtle control, and organizational transfer of learning (Takeuchi and Nonaka 1986 ). This concept was further advanced by Schwaber ( 1997 ) into the SCRUM Footnote 2 framework as a process for software product release. While agile was historically developed for software products, agile hardware development is a practical approach for rapidly creating physical systems with high potential for manufacturing repurposing (Omidvarkarjan et al. 2020 ). For instance, one case company followed an agile product development approach to respond quickly to the ever-changing regulations and requirements of the product. Future research can build on early work that studies manufacturing repurposing using the concept of agile product development. For example, Schmidtner et al. ( 2021 ) discuss the role of agile working during the pandemic and its impact on the future of work environments; Janssen and Van der Voort ( 2020 ) explain the complementary (and sometimes contradictory) role of agility and adaptivity governance during the COVID-19 pandemic; and more broadly, Yayla-Küllü et al. ( 2021 ) advocate that firms need to increase the extent of their agile and adaptable operations (e.g., by using product-line flexibility or in general resource flexibility) to cope with uncertainties.

6.1.4 Corporate philanthropy

One of the most striking features of the manufacturing repurposing initiatives was that organizations did not repurpose just for the sake of economic rent. Most of the ad hoc initiatives of firms were driven by goodwill to help healthcare workforces and societies combat the shortages of critical items. The initiatives were almost always driven by individuals passionate about and driven to help out during COVID-19—sometimes as a response to a request from local governments. This important aspect of manufacturing repurposing can be studied using the notion of corporate philanthropy. Corporate (strategic) philanthropy is defined “as the synergistic use of organizational core competencies and resources to address key stakeholders’ interests and to achieve both organizational and social benefits” (McAlister and Ferrell 2002 , p. 690). The notion of corporate philanthropy offers several promising research paths. For example, how can we study manufacturing repurposing from the perspective of corporate philanthropy? How are firms’ philanthropic decision-making processes affected by firm antecedents and ownership structures (e.g., private vs. public, small vs. large, etc.)? What is the role of operations managers in philanthropic activities? How will the experiences from manufacturing repurposing during the COVID-19 pandemic impact firms’ willingness to engage in philanthropic activities in the future?

6.2 Implications for practice

Our findings offer advice for manufacturing managers and policymakers. Overall, for both interest groups, we provide a visual overview of the most central driving and braking forces of manufacturing repurposing—both at the macro and micro perspectives. The causal loop diagrams help inform decisions because they show the likely effects of interventions.

For manufacturing managers, our results highlight, in particular, the importance of collaboration within and between firms. Success in repurposing initiatives depends largely on the network. Through collaborations (e.g., buyer–supplier relationships), firms can mobilize a larger pool of resources, which provides better access to product and process know-how and designs. Maintaining a close relationship with key partners and suppliers provides access to the required know-how and capabilities. If these collaborative relationships are built before a crisis, they are much easier and quicker to evoke when needed. The more complex the product, the more important the role of the network is. Among the products that we studied, within-and between-firm collaborations were essential for ventilator production and less evident for sanitizers. Relatedly, firms with agile internal product and process design capabilities exhibited more successful manufacturing repurposing operations. They rallied more people faster to the repurposing mission and drew on the necessary internal and external expertise and information to design the products and processes. Again, this capability can be nurtured and built before the next crisis.

For policymakers, we first point out anecdotal evidence that firms that collaborate closely with the agencies responsible for developing emergency regulations report a higher success rate in their manufacturing repurposing projects. Second, we emphasize the role of policymakers in general. During a crisis, companies should not be left alone. While market forces help create economic incentives to engage in manufacturing repurposing, companies need assistance in building collaborative networks, receiving funding, and navigating juridical frameworks. Furthermore, because corporate philanthropy plays a large role, governments should not rely on market forces to make repurposing happen. Closer relationships and frequent and faster feedback cycles between regulatory agencies and manufacturers generally result in faster product development processes with fewer iterations and more effective products for end users. Therefore, a high degree of collaboration between firms, governments, and regulatory agencies increases the success of manufacturing repurposing. Such collaborative frameworks should be developed before or during the early stages of emergencies.

6.3 Limitations and outlook

Our study has several limitations. Most importantly, although our research is based on the largest multiple-case study of manufacturing repurposing in the literature, we are limited by having only qualitative insights. Future research could provide quantitative evidence. For example, one promising research direction is to map the continuum of case companies from unsuccessful to successful manufacturing repurposing projects—in terms of, for example, the degree to which they manufactured the intended products at scale—and reflect on the characteristics, hurdles, and best practice. For example, is there a link between companies with established lean practices or flexible manufacturing operations and their success or unsuccess rates during manufacturing repurposing? Scholars drawing on quantitative evidence can build maturity models to assess the best practices and readiness levels of firms and societies to engage in manufacturing repurposing projects. Future quantitative research could also explore the role of contextual factors, such as the size of companies, product complexity, technological capabilities, organizational culture, and country characteristics in manufacturing repurposing projects.

There is also a range of other potentially relevant and interesting research streams. Researchers can study how companies design, prototype, and develop radically different products and what they can learn from their experiences. Future scholars can also examine the regulatory aspects of manufacturing repurposing, such as contracts, intellectual property, standardizing processes, and licensing. The open-source culture movement, including both software and hardware, can be studied. Other promising research avenues are the role of new technologies, mainly additive manufacturing, digital platforms, digital infrastructure, and advanced robots. Future research can also study organizational-related aspects, such as project management, team building, leadership, knowledge management, organizational learning, training, and organizational design during manufacturing repurposing projects. In short, manufacturing repurposing offers a broad pallet of relevant and interesting research avenues for scholars.

7 Conclusion

This research has provided an in-depth understanding of manufacturing repurposing, drawing on rich empirical insights from the manufacturing repurposing of four product categories during the early stages of the COVID-19 pandemic: face shields, face masks, hand sanitizers, and ventilators. To structure our analysis, we systematically coded the interview data to identify the key constructs of the manufacturing repurposing phenomenon. We summarized the findings in two causal loop diagrams at the macro and micro levels. These diagrams visualize and conceptually illustrate the dynamic behavior of manufacturing repurposing operations. Our study contributes one of the first empirical analyses of the manufacturing repurposing phenomenon to the literature.

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Acknowledgements

We acknowledge financial support from European Union’s Horizon 2020 research and innovation programme. We are also indebted to our many informants in various companies, who generously shared their experiences and knowledge with us. We would also like to thank the editors for their constructive comments, which helped us improve our manuscript.

Open access funding provided by Swiss Federal Institute of Technology Zurich. The research leading to these results received funding from Eur3ka, European Union's Horizon 2020 under Grant Agreement No 101016175.

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Appendix A. Interview guide

1.1 part 1: impact of covid-19.

What was the impact/challenges of Covid-19 on your factory?

What were the operational changes to your production lines/processes due to Covid-19? (if any)

1.2 Part 2: Repurposing manufacturing (Micro Level)

How did your factory move from producing core products to the new repurposed products?

Installing new machines/equipment, using new technologies, and setting up new production lines?

Training staff?

Defining new organizational practices?

How long did it take for you to implement changes?

What were the main challenges?

Macro Level: Regulatory, funding, competition, distribution

Firm Level: Know-how, manpower, facilities

What was the main driving force/motivation to repurpose and who made the decision to repurpose?

Top management

Team wanted to make a change

Goodwill/ Mandatory obligations

Are still producing that product? If not:

Why did you stop?

What have you done with the equipment? Will you dispose of them, store them, sell them?

What have you done with the excess capacity?

What were the best practices/learnings from repurposing?

How to cope with uncertainty

New organizational practices

New ways of managing operations/new project management style

Knowledge management of competencies/ know-how

1.3 Part 3: Repurposing manufacturing (Macro Level)

How did you manage the disruption from the suppliers’ side?

How did you distribute your products to the customers? How did you identify them? Were there any challenges?

How could you improve the supply chain of manufacturing repurposing for the future?

How did this change your current organizational management of supply chains (e.g., multiple suppliers, digital systems)

Suppliers’ relationships

Appendix B. Coding Approach

2.1 table b-1 excerpts of coding, rights and permissions.

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Ho, W., Maghazei, O. & Netland, T.H. Understanding manufacturing repurposing: a multiple-case study of ad hoc healthcare product production during COVID-19. Oper Manag Res 15 , 1257–1269 (2022). https://doi.org/10.1007/s12063-022-00297-1

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DOI : https://doi.org/10.1007/s12063-022-00297-1

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How to Hold an Ad Hoc Meeting that Gets Results

By Rad Aswani • July 14, 2023

In today’s fast-paced business environment, teams often encounter unforeseen circumstances that require immediate attention. Ad hoc meetings, or impromptu gatherings called to address specific issues, have become increasingly important for their ability to facilitate quick decision-making and problem-solving. But how can we ensure that these ad hoc meeting events are effective and optimize our team’s collaboration? In this blog post, we will explore the purposes of ad hoc meetings, strategies for success , the role of technology, and how to overcome common challenges, while showcasing real-life case studies of successful ad hoc meetings in action.

Short Summary

• Ad hoc meetings are essential for addressing urgent issues, making collaborative decisions, and adapting rapidly to changes.
• Strategies such as defining objectives and goals, keeping them focused and short, and selecting the right participants can ensure effective ad hoc meetings.
• Technology provides digital collaboration tools that help teams stay connected and collaborate effectively in ad hoc meetings leading to enhanced team productivity.

Understanding the Purpose of Ad Hoc Meetings

Ad hoc meetings serve specific purposes, such as addressing urgent issues, making collaborative decisions, and adapting to sudden changes. While they might not be as structured as regular meetings, they are crucial for resolving pressing matters in a timely manner, fostering teamwork, and maintaining the agility needed to navigate today’s dynamic business landscape.

Let’s delve deeper into these three key purposes and understand why ad hoc meetings are indispensable.

Urgent Issues and Problem Solving

Imagine a high-value client considering terminating their contract in the near future or the need to find a replacement keynote speaker for your organization’s annual conference within a two-day period. In such situations, ad hoc meetings are ideal for addressing urgent problems that require immediate attention and resolution. By bringing together key team members in a focused, time-sensitive discussion, ad hoc meetings can provide an opportunity for creative problem-solving and fast, informed decisions.

However, it is important to use ad hoc meetings sparingly and only when necessary. Overuse of such meetings can lead to decreased productivity and team morale, as they may lack structure and could potentially disrupt workflows. To strike the right balance, ensure that ad hoc meetings are reserved for truly urgent matters and that they are planned and executed effectively.

Collaborative Decision-Making

When a collective decision needs to be made quickly, ad hoc or unplanned meetings can facilitate the process by bringing together the right stakeholders for a focused discussion. Collaborative decision-making ensures that all parties involved are taken into consideration and that the most suitable solution is achieved. This approach can help construct agreement, encourage team collaboration, and generate a sense of ownership among team members, even in unplanned meetings.

But how can we ensure successful collaborative decision-making in ad hoc meetings? The key lies in following a structured process, which includes:

• Identifying the issue
• Collecting relevant data
• Generating possible solutions
• Assessing the solutions
• Making a choice
• Executing the decision

Following these steps can help avoid poorly organized meetings, including those conducted via Zoom meetings, and ensure a successful outcome, as demonstrated by the Tilt case study, which utilized Kumospace to enable virtual meetings and assist the team in reaching agreements on key decisions.

Adapting to Sudden Changes

Ad hoc meetings can also serve as a platform for teams to quickly adjust to sudden changes, enabling them to come up with ideas and take action in response to unforeseen circumstances. For example, when an emergency or crisis arises that necessitates swift decision-making and strategy development , ad hoc meetings can provide a focused space for rapid ideation and implementation of solutions.

However, adapting to sudden changes can be challenging, as it can bring uncertainty, necessitate rapid decision-making, induce stress, and disrupt routines, including scheduled meetings. To navigate these challenges effectively, it is crucial to have a clear agenda, maintain focus, and ensure that all participants are engaged and actively contributing to the discussion.

Strategies for Effective Ad Hoc Meetings

Now that we understand the purposes of ad hoc meetings, let’s explore some strategies to ensure their success. This includes defining objectives and goals, keeping meetings short and focused, using the right tools, and selecting the right participants.

Implementing these strategies can lead to more effective ad hoc meetings that truly serve their intended purposes and contribute to overall team productivity and collaboration.

Defining Objectives and Goals

Before starting an ad hoc meeting, it is essential to set clear goals and objectives, which helps keep the discussion focused and productive. This may involve:

• Identifying the specific issue or problem to be addressed
• Outlining the desired outcome
• Assigning clear deliverables and responsibilities to each participant

In addition to meeting notes and setting objectives, it is also helpful to create a concise meeting agenda , even if it is just a few bullet points. This ensures that the meeting discussion remains on track and that all pertinent topics are addressed within the designated time frame.

By clearly defining objectives and goals, you can lay the foundation for a productive and efficient ad hoc meeting.

Keeping Meetings Short and Focused

Short and focused meetings maintain participant engagement and prevent unnecessary time wastage. To help achieve this, consider setting a time limit for the purpose of the meeting, with most ad hoc meetings not exceeding 30 minutes. By keeping meetings concise, participants are more likely to stay focused on the task at hand and contribute meaningfully to the discussion.

Another helpful strategy is to use a question-based agenda, which comprises questions rather than discussion points. This approach encourages participants to think critically and engage in problem-solving, leading to more efficient and effective ad hoc meetings.

Selecting the Right Participants

Inviting only relevant team members ensures that the meeting remains efficient and on-topic. To do this, consider employing the two-pizza rule introduced by Jeff Bezos at Amazon, which suggests that every internal team should be small enough to be fed with two pizzas. This approach helps maintain a manageable group size, ensuring that discussions remain focused and decisions can be made quickly.

In addition, be mindful of the roles and responsibilities of each participant, ensuring that they are clear on their tasks and can contribute effectively to the discussion. By selecting the right participants, you can create an environment that fosters efficient problem-solving and decision-making in ad hoc meetings.

Tools and Techniques for Successful Ad Hoc Meetings

Utilizing digital collaboration tools , time management techniques, and follow-up measures can contribute to successful ad hoc meetings. By leveraging technology and implementing best practices, teams can overcome common challenges and enhance overall productivity and collaboration.

Let’s explore some of the tools and techniques that can help facilitate successful ad hoc meetings.

Digital Collaboration Tools like Kumospace

Kumospace offers an innovative platform for remote work, providing a virtual office environment that fosters seamless collaboration . Its real-time interaction capabilities transcend geographical boundaries, enabling teams to communicate, brainstorm, and manage projects as if they were in a physical office. 

The platform's user-friendly interface allows for easy file sharing and instant feedback, fostering productivity. Whether you're running ad hoc meetings or long-term projects, Kumospace promotes effective teamwork and enhanced efficiency, contributing to superior business outcomes.

Time Management Techniques

Effective time management techniques help keep meetings on track and prevent them from becoming too long. By setting a time limit for the meeting, participants are more likely to stay focused on the discussion and ensure that all topics are addressed within the designated time frame.

To further optimize time management in ad hoc meetings, consider scheduling shorter calendar time slots, such as 15 or 20 minutes, instead of the default 30-minute intervals. This approach encourages participants to remain on task and prioritize the most important issues at hand, leading to more efficient and effective meetings.

Follow-Up and Accountability Measures

Implementing follow-up and accountability measures ensures that action items on formal agenda are completed and progress is tracked. After the meeting, it is essential to:

• Distribute meeting minutes or notes
• Assign tasks and deadlines
• Establish a system for tracking progress
• Provide feedback and support throughout the process

These steps can help ensure that team members stay on track and complete their tasks efficiently.

Digital collaboration tools , such as Kumospace, can be an effective way to assign tasks, monitor progress, and offer feedback and support. By leveraging technology and implementing follow-up and accountability measures, teams can ensure the success of their ad hoc meetings and drive better results.

Overcoming Common Challenges in Ad Hoc Meetings

Ad hoc meetings can present a variety of challenges, such as disruptions to workflows, lack of structure, and the need to balance flexibility and efficiency. By addressing these challenges and implementing the appropriate strategies, teams can ensure that their ad hoc meetings are productive and contribute to overall team collaboration and success.

Strategies for successful ad hoc meetings include setting clear objectives, establishing ground rules, and assigning tasks.

Minimizing Disruptions to Workflows

Ad hoc meetings can sometimes disrupt workflows and impact productivity, especially if they are called frequently or without adequate notice. To minimize disruptions, consider scheduling ad hoc meetings at appropriate times, giving team members ample notice, and ensuring that the planned meetings are kept short and focused.

Additionally, make sure to use digital collaboration tools like Kumospace, which can help teams stay organized and connected even when working remotely. By minimizing disruptions to workflows, teams can maintain focus on their tasks and ensure that ad hoc meetings contribute positively to productive work sessions and to overall productivity and collaboration.

Maintaining Structure and Organization

While ad hoc meetings may lack the structure of formal meetings, it is still important to maintain a sense of organization to keep the discussion on track and achieve the desired outcomes. This can be achieved by setting clear objectives and goals, creating a concise agenda, and assigning roles and responsibilities to each participant.

In addition, using digital collaboration tools like Kumospace can help maintain structure and organization in ad hoc meetings by providing a platform for real-time communication , file sharing, and collaboration. By maintaining structure and organization, teams can ensure that their ad hoc meetings are productive and effective.

Balancing Flexibility and Efficiency

Balancing flexibility and efficiency in ad hoc meetings is crucial to ensure that teams can adapt to new circumstances and make quick decisions while staying focused on the meeting’s purpose. To strike this balance, consider implementing the strategies discussed earlier, such as defining objectives and goals, keeping meetings short and focused, and selecting the right participants.

Additionally, be prepared to adjust and adapt as needed during the meeting, allowing for changes in direction or new ideas to be explored while still maintaining focus on the intended outcome. By balancing flexibility and efficiency, teams can ensure that their ad hoc meetings are both productive and adaptable.

Case Studies: Successful Ad Hoc Meetings in Action

Real-life case studies can provide valuable insights into the successful implementation of ad hoc meetings in a meeting room, conference room, or virtual meeting or space, showcasing how tools like Kumospace and effective strategies can lead to improved team collaboration and productivity.

Let’s explore two case studies that demonstrate the benefits of ad hoc meetings in action.

Tilt case study using Kumospace (https://www.kumospace.com/customer-stories/tilt-virtual-workspace)

The Tilt case study highlights the benefits of utilizing Kumospace for virtual ad hoc meetings , resulting in enhanced communication and collaboration within the company. By adopting Kumospace as their virtual workspace, Tilt was able to transition to a 100% remote model and double their revenue, eliminating the need for a physical office.

This case study demonstrates the power of digital collaboration tools like Kumospace in facilitating seamless communication and collaboration during ad hoc meetings, ultimately leading to improved team performance and success.

Phonesales case study with switching to Kumospace (https://www.kumospace.com/customer-stories/phonesales.com-virtual-workspace)

The Phonesales case study showcases the positive impact of switching to Kumospace for their virtual workspace , leading to more efficient ad hoc meetings and improved team visibility. By transitioning from Gather to Kumospace, Phonesales was able to enhance their team’s operations, foster more effective collaboration, and boost productivity.

This case study highlights the importance of selecting the right tools and digital collaboration tools for ad hoc meetings, as well as the potential for improved efficiency and success when leveraging technology to support team communication and collaboration.

In this section, we will address some common questions about ad hoc meetings, their importance, challenges, and the role of technology in facilitating effective meetings. These FAQs will provide a quick reference for readers seeking additional information and clarification on the topic.

Ad hoc meetings are important for organizations because they allow for quick decision-making and problem solving.

What is an ad hoc meeting?

An ad hoc meeting is an unplanned, impromptu meeting called to address a specific issue or topic. These meetings are often utilized to address urgent issues, make collaborative decisions, and adapt to sudden changes in the business environment . To initiate such a meeting, one can simply call an ad hoc meeting when the need arises.

Ad hoc meetings, also known as one off meetings, can be a great way to quickly address issues and make decisions in a timely manner, especially when they turn into a productive ad hoc meeting. In contrast to recurring meetings, these spontaneous gatherings can offer more flexibility for urgent matters.

Why are ad hoc meetings important?

Ad hoc meetings are important for addressing urgent matters, making collaborative decisions, and adapting to sudden changes. They provide a platform for quick decision-making and problem-solving, which can be crucial for maintaining team agility and responding effectively to unforeseen circumstances.

Ad hoc meetings can help teams stay on top of their tasks and ensure that everyone is on the same page.

How can we ensure effective ad hoc meetings?

Ensuring effective ad hoc meetings involves setting clear objectives, keeping meetings short and focused, and inviting the right participants. Implementing these strategies can help create an environment conducive to more productive work, problem-solving and decision-making. Ultimately, this contributes to overall team collaboration and success.

What challenges might we face in ad hoc meetings?

Challenges in ad hoc meetings may include disruptions to workflows, lack of structure , and balancing flexibility with efficiency. By addressing these challenges and implementing the appropriate strategies, teams can ensure that their ad hoc meetings are productive and contribute positively to overall team collaboration and success.

Organizations should strive to create an environment that encourages collaboration and open communication. This can be done.

What role does technology play in ad hoc meetings?

Technology plays a crucial role in ad hoc meetings by providing digital collaboration tools like Kumospace to facilitate seamless communication and organization. These tools can help teams stay connected, share information, and collaborate effectively, ensuring that ad hoc meetings are both productive and efficient.

By leveraging the power of technology, teams can quickly and easily organize and manage ad hoc meetings.

How can ad hoc meetings enhance team collaboration?

Ad hoc meetings can enhance team collaboration by providing a platform for quick decision-making and problem-solving. By bringing together the right stakeholders and facilitating focused discussions, ad hoc meetings can help teams address pressing issues and make fast and informed decisions. Ultimately, this can contribute to improved collaboration and productivity.

In conclusion, ad hoc meetings play a crucial role in addressing urgent issues, making collaborative decisions, and adapting to sudden changes. By implementing effective strategies, selecting the right participants, and leveraging digital collaboration tools like Kumospace, teams can ensure that their ad hoc meetings contribute positively to overall productivity and collaboration. As the case studies and best practices discussed in this blog post demonstrate, successful ad hoc meetings can have a significant impact on team performance and organizational success. So, the next time you face an urgent matter or need to make a quick decision, embrace the power of ad hoc meetings and watch your team’s collaboration and productivity soar.

Frequently Asked Questions

What is ad hoc meeting in teams  .

Ad hoc meetings are a great way to instantly come together for impromptu meetings and conversations within Microsoft Teams. By simply clicking the “Meet Now” button, you can start an immediate meeting with the relevant people involved.

This is especially useful if it becomes apparent that a real-time discussion needs to take place rather than exchanging messages back and forth.

How do you use ad hoc meeting in a sentence?  

Ad hoc meetings are an efficient way of handling urgent topics that require immediate support and timely decisions.

Who attends ad hoc meeting?  

Ad hoc meetings typically include those who have an important stake in the matter being discussed. This could be managers, team members, and individuals who will need to take action or provide feedback in order for the project to move forward successfully.

Thus, attendance at an ad hoc meeting is essential for the project’s success.

What is an example of an ad hoc meeting?  

An ad hoc meeting is a gathering convened to discuss or plan something out of the ordinary. Examples of when an ad hoc meeting might be necessary include when the company needs to make an important decision or take urgent action quickly, such as handling a crisis situation or addressing an unexpected change in company policy.

What are the main purposes of ad hoc meetings?  

Ad hoc meetings are an essential tool for enabling quick, effective decision-making in times of emergency or unexpected events. They can provide an opportunity to address urgent issues, ensure collective buy-in on decisions, and allow teams to rapidly adapt to changes as needed.

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Rad has over 7 years of experience in Marketing. Currently, she is the fun Digital Marketer at Kumospace. She leads initiatives such as influencer marketing, SEO management, and social media to name a few. Outside of work, Rad enjoys traveling, working out, and spending time with her family and friends.

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Ad hoc analysis: a tool for quick decisions

What is an ad hoc analysis.

Ad hoc analysis is a needs-based evaluation of company data in order to answer specific questions that are not covered by standard reports .

Its purpose is to help you make informed decisions in a timely manner .

Difference to standard reports

In contrast to a standard report, which provides routine, recurring evaluations, the ad hoc analysis is flexible and situation-specific . It answers specific questions that go beyond the scope of a standard report and thus provides decision-relevant information.

Significance for companies

Ad-hoc analyses are an essential tool for you to react quickly to changing situations. They allow you to delve deeper into the data and identify patterns, trends and deviations that could be overlooked in standard reports. This enables fast, well-founded decisions to be made on the basis of data.

The relevance of ad hoc analyses

In dynamic business environments, the ability to react quickly to new situations and challenges is crucial.

This is where ad hoc analyses come into play. They address the problem of promptly answering specific, often unforeseen questions that cannot be covered by standardized reports.

Situational application

A typical example is an unexpected sales fluctuation. Standard reports show the deviation, but the cause remains unclear. Ad hoc analyses make it possible to delve deeper into the data, identify patterns and provide answers that support decision-making.

Application examples in various areas of the company

• Ad hoc analyses are valuable in many areas of a company.
• In marketing , they can help to understand the impact of specific campaigns.
• In the finance department , they can help to identify unusual spending patterns.
• In sales , they can be used to explain sudden changes in sales figures.
• The possibilities are manifold and demonstrate the necessity and relevance of ad hoc analyses in the modern business environment.

Carrying out an ad hoc analysis

The process begins with the formulation of a specific question . This is followed by the search and data analysis of relevant data . The conclusion of the process is the interpretation of the results and the decision-making process based on them.

IT and technology

IT and modern technologies play a key role in ad hoc analysis. Tools such as Google Analytics , OLAP databases and big data technologies enable the rapid processing and analysis of large volumes of data.

Use of data sources and big data

Access to relevant data sources is essential for an ad hoc analysis. Big data often comes into play here, as it provides access to extensive, diverse information. These data sources can then be used to answer the specific question.

Ad hoc analysis and standard reporting

Differences.

While standardized reports provide regular, ready-made reports that help monitor overall business performance, ad hoc analysis is more flexible and aims to answer specific, one-off questions.

Both forms complement each other: while standardized reports continuously provide general business data, ad hoc analysis enables deeper insights when required.

Deviations in standardized reports can be the reason for an ad hoc analysis.

Both contribute to well-founded decisions in the company.

Ad-hoc reporting tools:

Ad-hoc reporting with qlik, olap databases and google analytics.

Various tools, including Qlik, OLAP databases and Google Analytics, are valuable resources when performing ad hoc data analysis. While OLAP databases offer the ability to perform multidimensional analyses of large amounts of data, Google Analytics offers specialized web data analyses. At the center of these tools is Qlik, a powerful data visualization and discovery application that provides an intuitive interface for ad hoc reporting and analysis.

Practical example: Ad hoc analysis in use

Case study:

Suppose a company notices sudden deviations in sales.

Standard reports can show the deviation, but the cause remains unclear.

An ad hoc report is carried out to investigate the sudden drop in sales.

Challenges, solutions and results

The challenge is to sift through the data and recognize patterns.

With the help of ad hoc analysis tools, a deeper examination of the sales data is carried out.

It turns out that the decline in sales is attributable to a specific product line. With these findings, the company can now take targeted measures to address the problem.

Conclusion: The power of ad hoc analysis

Ad-hoc reporting plays an important role in deciphering unexpected business events and phenomena. The ability to respond to individual, situational questions and thus gain insights that go beyond the limits of standardized reporting is a decisive factor for your company's success.

Ad hoc analyses not only enable you to find precise answers to specific questions, but also open up new perspectives and provide actionable insights. They are a source of competitive advantage in a data-driven economy by enabling deeper insight and a better understanding of business dynamics.

A look into the future

In an increasingly networked world in which data volumes are constantly growing and business models are constantly changing, the importance of ad-hoc analysis will continue to grow.

The shift towards data-driven decision-making in companies is increasing the need for an analytics capability that can respond to specific business issues in a flexible, agile and timely manner.

In view of the technological advances that are constantly opening up new analysis possibilities and the increasing demands in the business world, it is likely that ad hoc analyses will become even more important in the future. This development underlines the need for you to establish a culture of data-driven decision making and to master the skills and tools for effective ad hoc analysis.

In summary, it can be said that ad hoc analyses are an essential component of successful business strategies today and in the future.

They provide the ability to make informed and timely decisions in a rapidly changing business world.

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• v.83(11); 2017 Nov

Can ad hoc analyses of clinical trials help personalize treatment decisions?

Eman biltaji.

1 Division of Clinical Pharmacology, Department of Paediatrics, University of Utah, School of Medicine, Salt Lake City, Utah, USA

2 Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, Utah, USA

3 Program in Personalized Health, University of Utah, Salt Lake City, Utah, USA

Shaun S. Kumar

Elena y. enioutina.

4 Department of Pathology, University of Utah, School of Medicine, Salt Lake City, Utah, USA

Catherine M. T. Sherwin

The value of ad hoc analyses of clinical trials may not be clear. Ad hoc analyses are carried out after developing analysis plans and are considered exploratory in nature. Such analyses should be conducted on a ‘rational’ biological basis, rather than ‘aimless’ data dredging. Ad hoc analyses are done to generate new hypotheses that guide future research studies. Such retrospective analyses are of great value, especially if the differences for the primary outcome (adequately powered) were not detected. Analyses of positive trials can also identify populations that are treatment‐resistant or susceptible to adverse effects. Ad hoc analyses are highly underutilized to guide future studies to personalize treatment recommendations, but could have a great potential to personalize treatment decisions.

Ad‐hoc analyses of clinical trials investigating cetuximab use in colorectal cancer resulted in tailoring treatment decisions in this population. Cetuximab received US Food and Drug Administration (FDA) approval for advanced colorectal cancer treatment in February 2004 under ‘the accelerated approval of biological products regulations, 21 CFR 601.40‐46’ 1 . This approval was granted based on surrogate endpoint results until adequate evidence supporting the clinical benefit of cetuximab is developed. Although a significant improvement in overall survival (OS; hazard ratio [HR] 0.77; 95% confidence interval [CI] 0.64–0.92; P  = 0.005) was reported among cetuximab users 2 , resistance was commonly noticed 3 . These investigators relied on the lung cancer literature to understand this phenomenon, where the correlation between KRAS mutation status and response to tyrosine kinase inhibitors, other therapies directed against anti‐EGFR, has been reported 4 , 5 . The ad hoc analysis of cetuximab clinical trial samples showed a similar pattern of response to anti‐EGFR targeted therapy, with preferential benefit to cetuximab observed in wild‐type KRAS subgroup 3 . Patients with wild‐type KRAS tumours had significant improvement in OS (HR 0.55; 95% CI 0.41–0.74; P  < 0.001), compared to no significant difference (HR 0.98; P  = 0.89) reported among patients with mutated KRAS tumours. Results of this and subsequent relevant studies changed colorectal cancer clinical guidelines and practice. Testing for RAS mutation status is now required to determine eligibility for anti‐EGFR targeted therapies in patients with colorectal cancer diagnosis, and even for reimbursement purposes 6 .

Another example where the ‘ ad hoc analysis’ approach may be useful to personalize treatment decisions is in multiple sclerosis. A recently published pivotal Phase 3 trial (ORATORIO) evaluated the safety and efficacy of ocrelizumab in primary progressive multiple sclerosis (PPMS) 7 . Ocrelizumab is a humanized monoclonal antibody that selectively depletes CD20‐expressing B cells, which is thought to slow disease progression in PPMS 8 . The FDA recently approved this drug for PPMS management based on results of this trial. Reviewing the study results, the primary and first secondary endpoint were a 12‐week and 24‐week confirmed disability progression, respectively. According to figures presented in the published manuscript, the curves depicting the cumulative probability of disability progression overlapped and there may not be a significance difference between ocrelizumab and placebo at these time points. Indeed, if the trial was stopped at 96 or 108 weeks, cut points used by a prior study 9 , the difference between the two arms would fail to reach significance for the primary endpoint. However, significant differences reported at longer follow‐up times imply a delayed effect of ocrelizumab to show the anticipated benefits. In addition, there was underreporting of the degree of missing data, and in which variables. This is of utmost importance given the progressive nature of the disease and the method used to impute missing data (last‐observation‐carried‐forward method). Safety information is still under assessment in the open‐label phase of the study. However, the observed increase in neoplasms in the ocrelizumab group was concerning and required further evaluation.

So, how is this study relevant to our topic? The answer simply lies is the OLYMPUS trial, an earlier study that assessed the use of rituximab, another anti‐CD20, in the PPMS population 9 . The OLYMPUS trial was a randomized, double‐blinded, placebo‐controlled multicentre trial study that did not show a significant difference in confirmed disease progression between rituximab and placebo through 96 weeks. However, their subgroup analyses showed a potential benefit for younger patients, specifically those having inflammatory lesions. This implies that the benefits of anti‐CD20 agents may be more evident if these agents are used in the treatment‐sensitive PPMS population for appropriate prolonged durations.

Learning from the colorectal cancer example presented in this editorial, more ad hoc analyses should be performed to identify the optimal patient–treatment combinations, in light of new relevant evidence and to guide future research. In addition, we recommend an ad hoc subgroup analysis of the ORATORIO trial to identify the targeted population that has the maximal benefit of the promising drug, ocrelizumab, with the least exposure to uncertain side effects. However, this approach is associated with obvious limitations, such as: limited power, potential biases, and possible ‘data dredging’. Ad hoc analyses can identify a target population that should be used in the design of a future adequately‐powered clinical trial.

Competing Interests

There are no competing interests to declare. Dr. Catherine Sherwin is a Senior Editor of BJCP .

Biltaji, E. , Kumar, S. S. , Enioutina, E. Y. , and Sherwin, C. M. T. (2017) Can ad hoc analyses of clinical trials help personalize treatment decisions? . Br J Clin Pharmacol , 83 : 2337–2338. doi: 10.1111/bcp.13377 . [ PMC free article ] [ PubMed ] [ Google Scholar ]

What Is Ad Hoc Analysis? A Comprehensive Guide to On-Demand Data Exploration

Table of Contents

Ad hoc analysis can become a potent solution to the increasing complexity of modern business environments. Learn about it in this article! 

I. Introduction

A. why lack of analytics is dangerous: google’s failure.

Speed of analysis is essential in the modern business world. If you’re too slow to analyze the existing trends in the development of technologies, your business will inevitably fail. A strong example of massive damage from insufficient analysis is Google. Before 2023, Google was considered one of the most undefeatable businesses. Despite minor failures of its pet projects, the business managed to capture multiple large-scale markets. It became a de facto monopoly in online searches. Its Workspace suite continues to be the best corporate product for startups. Still, in 2023, Google lost 100 billion dollars in response to one miscalculation. The company failed to allocate enough investments into artificial intelligence. As a result, its quality was lower than expected. 

Do you want to avoid such situations? In that case, it’s time to learn about ad hoc analysis.

B. Ad hoc analysis: Definition

What is ad-hoc analysis? In our opinion, its definition consists of two major components. Firstly, ad hoc analysis is notable for maximal speed. It uses modern tools, such as AI, to review varying aspects of reality fast. Secondly, a major part of the ad hoc analysis is its targeted nature. The ad hoc analysis doesn’t target all marketing issues or potential innovations. It concentrates on one hypothesis regarding one specific issue. 

We believe that Google could have prevented its problems through ad hoc analysis. Why? On the one hand, it would have been capable of targeting AI alone. On the other hand, the analysis would have been fast, allowing a quick reaction to market trends.

C. Importance of ad hoc analysis

Why is ad hoc analysis so important in modern data-driven decision-making? In our opinion, this approach to analytics is vital due to the increasing pace of innovation. In the 1920s, Ludwig von Mises formulated the so-called calculation problem. Targeted at socialism, it nonetheless has great implications for all information-centric societies. According to the libertarian approach, our society is too advanced to calculate. Humans are the pinnacle of development for matter. Consequently, their actions can be infinitely complex. Moreover, the progress of our civilization makes calculation increasingly difficult . The potential for innovation that stems from AI and quantum computing is tremendous. In the end, the complexity of our society will always outpace our calculation capabilities.

In this light, ad hoc analysis is an ideal solution to the presented problem. Why? It allows for introducing on-the-fly changes to our existing prediction models. In the future, businesses will focus on quarterly analytics and continuous (even daily) ad hoc analysis. Only they can provide enough information to help with reasonable decisions in the ever-changing market. Ultimately, the key goal of this article is to outline how to do ad hoc analysis. You’ll learn about the key practices for it and study the benefits of the methodology in-depth.

II. Understanding Ad Hoc Analysis

A. definition of ad hoc analysis.

We’ve already outlined the key aspects of ad hoc analysis in the introduction. Let’s unite the two elements, speed, and targeted nature, into a definition. So, ad hoc data analysis is a high-speed analysis of a specific business phenomenon. A strong example of ad hoc analysis is the review of click-through rates in isolation from other aspects. Why is it so useful? In our opinion, fast analysis can help discover major trends outside common-sense thinking. You can then use ad hoc analysis to learn if you need new criteria for your business intelligence. 

B. Key concepts in ad hoc analysis

What are some key concepts you need to know in ad hoc analysis? In our opinion, you should pay attention to the following features:

Exploratory Data Analysis

The ad hoc platform represents a strong example of this concept. We usually do ad hoc analysis to find answers to specific questions. Is it rational to do this analysis for all phenomena? In our opinion, no. There are too many phenomena. As a result, the key reason to do ad hoc analysis is to analyze some black swan events. Nassim Taleb developed this concept to describe events that go beyond our expectations. 

For many years, Europeans believed that only white swans existed. Later, they found black swans on other continents, breaking their theory. Ad hoc analysis is perfect for reviewing such “black swans.” The idea is to first analyze them in isolation to understand how they change the existing models. In short, ad hoc analysis is exploratory.

Hypothesis generation and testing 

Since ad hoc analysis deals with unexpected events, you need to think about them in the context of your existing prediction models. Ad hoc analysis requires an ability to think scientifically. Your key goal is to create multiple ideas for how a black swan event lands in your model. You must be able to establish hypotheses about those events. What are they? How do they fit into the existing framework? Do they require new frameworks? 

After that, the key idea is to test the presented hypotheses. Overall, we see ad hoc analysis as the expansion of advanced scientific methods in business analytics. In a way, 17th-century rationalist philosophers such as Locke and Hobbes laid its foundation. Read those classics if you want to understand the methodological basics of ad hoc analysis.

Real-time analysis

One of the key differences between ad hoc analysis and “normal” analysis is its real-time nature. The real-time nature of ad hoc analysis is present in two aspects. Firstly, ad hoc analysis is designed to be as fast as possible. Secondly, and more importantly, its key ability is to react to events as they appear. For example, if a trend like AI in language learning appears, ad hoc analysis helps one react immediately. Platforms like Duolingo Max are developed en masse on the market. When traditional models reflect these issues, they’ll already be outdated. 

Iterative (step-by-step) approach

A significant strength of many ad hoc models is the relative simplicity of data within them. It’s difficult to perform iterative analysis in a full-scale analytical tool. Usually, they require tremendous amounts of information, so every iteration can take months. In turn, ad hoc analysis enables several iterations of data testing. You can put your information through numerous hypotheses. For instance, it’s possible to tie the growth of AI to manufacturing and education. You can test those hypotheses in small sessions that take two to three hours each. As a result, this analysis is much more flexible than past approaches.

Information-oriented visualization 

A major principle of ad hoc analysis is data visualization. On the one hand, every ad hoc model has to be information-oriented. The more numerical information you have for it, the better. On the other hand, this data should also be easy to read. The key idea behind any ad hoc analysis is user-friendliness. Various innovations may be difficult to understand even for seasoned managers. For example, some of the most interesting papers on AI are philosophical. You need to be a philosopher to work with the arguments of people like Eliezer Yudkowski. Rationalism and materialism are often extremely complex. Only visualization can prepare non-expert audiences for them.

Self-service 

Most advances in science come at the intersection of disciplines. We see the ultimate failure of physicalism today. It’s impossible to simplify everything to the physical level. Consequently, real advances occur at the intersection of humanities and STEM. AI is the prime example of this phenomenon. For instance, linguists propose to use corpora (collections of words and expressions) as an alternative source for modern generative AI . According to them, corpora-based models will promote a better understanding of language nuance. Sociologists and philosophers also play a major role in AI development. For instance, they highlight that AI models of the future will need human-like evolution. 

Consciousness is a social process. It doesn’t emerge out of nothing. What’s the key challenge for all those specialists? Many of them don’t know math and statistics at a high level. For this reason, modern ad hoc analysis must enable them to engage in self-service. Self-service stands for user-friendly tools in data analysis. A person should be able to calculate and visualize data without advanced knowledge. If we involve linguistics, sociologists, and philosophers in AI discussions, major opportunities for improvement will appear. Self-service exists to help with that.

B. The role of ad hoc analysis in data analytics

In our opinion, ad hoc analysis plays a gigantic role in modern data analytics. It does several important things for the modern decision-makers. Firstly, ad hoc analysis is strong when you want to explore new topics. Due to the rise of AI tools, the complexity of our economy is growing all the time. When innovations come into being, you can use ad hoc analysis to integrate them into your models. Secondly, ad hoc analysis is a perfect way to work with multiple hypotheses fast. If you have numerous opinions on the reasons something happened, this framework will work well. It allows you to take multiple small elements and easily review them. Thirdly, ad hoc analysis is usually easy to implement. This factor means various non-experts can now engage in analytics. As mentioned above, an interesting insight can come from many directions. Linguists and philosophers may often have enough expertise to transform the key practices in AI or RPA. 

Scientific methodology and ad hoc analysis

Ultimately, modern science methodology is the best way to see the interaction between ad hoc analysis and data analytics. Modern science has three levels of knowledge: hypothesis, theory, and law. A hypothesis is a realistic assumption about something. It doesn’t have a full-scale confirmation and doesn’t form into a complete system. A theory is a well-supported framework with a series of clear laws. It uses empirical data to establish rules that always work in certain conditions. For example, Newtonian gravitation is a proven law that works in certain conditions. 

Ad hoc analysis as a tool for creating hypotheses

How does ad hoc analysis adapt to this triad? In our opinion, ad hoc analysis encompasses hypotheses and hypothesis testing. In short, its goal is to give some interesting propositions and then test them. Data analytics, such as business intelligence, is at a greater level. It creates theoretical explanations and even laws out of hypotheses. Ad hoc analysis essentially provides material for bigger theoretical assumptions. It creates new theories and destroys old ones. If one uses the language of Thomas Kuhn , a philosopher of science, ad hoc analysis brings paradigm shifts to well-established theories in business.

C. Benefits of ad hoc analysis

The benefits of ad hoc analysis are tremendous. In our opinion, every company should try implementing it as fast as possible:

1. Flexibility and customization

The first benefit of ad hoc analysis is flexibility and customization. On the one hand, ad hoc frameworks are extremely flexible. In what ways? They’re typically user-friendly and, more importantly, ease the analysis of various topics. On the other hand, it’s possible to configure ad hoc tools for every type of analysis. They’re extremely customizable.

Consequently, it makes sense to try ad hoc analysis because it can account for many black swan events. Traditional business tools may have no high-quality instruments for quantifying the effects of AI or RPA. Ad hoc analysis can give you several ideas for this quantification. As a result, ad hoc models are the most flexible tool on the market.

2. Speed and efficiency

What’s the key issue of large-scale models? They require a lot of time for configuring. Creating a dashboard in Google Analytics is a complex task. Various users of the platform complain about its complexity. When you’re configuring your Google dashboard for analysis, certain trends may become outdated in the meanwhile.

Consequently, it’s vital to have a tool that provides fast analysis. What is this tool? In our opinion, this tool is ad hoc analysis. It’s simpler than a full-scale data review. As a result, this instrument allows you to make fast decisions on new trends.

Some critics may argue that ad hoc analysis will never be complete. We agree. Many of its conclusions can be incomplete. However, there’s a major factor we need to consider: we don’t always need accuracy to make valuable predictions. Occasionally, what matters are broad trends. If a giant wave manifests at sea, it won’t cease to be giant despite being 15 meters instead of 20.

Similarly, major trends like AI may seem bigger than they’re in reality. Still, ad hoc analysis can correctly identify them as a strong field for investment. Even if the investments bring 20 billion dollars instead of 25, profits are still tremendous. Since innovations are increasingly complex, we have no time for months-long planning. You need to act as soon as possible to capture interesting niches. Ad hoc analysis is the fastest data-driven framework for decision-making.

3. Empowerment of business users

Lastly, every proper ad hoc framework empowers business users. It allows everyone to create high-quality analyses for certain topics quickly. Ad hoc analysis tools are typically easy to use. Consequently, you don’t need to be a statistician to use them well. Why is this so important for modern business? In our opinion, the answer is simple: more people can perform analytics. This, in turn, leads to better analysis. Different stakeholders have valuable views on business systems. For example, rank-and-file workers can report many problems with some practices that seem efficient. Ad hoc analysis lets them access the decision-making process. This approach, in our opinion, enables democracy in the workplace.

III. Types of Ad Hoc Analysis

Multiple types of ad hoc analysis exist. Let’s explore the key ways to perform it,

A. Ad hoc querying

The first type of ad hoc analysis and reporting is querying. In this regard, the key idea is to use modern databases. They enable two important things, easing ad hoc analysis:

1. SQL and NoSQL queries

SQL and NoSQL frameworks undoubtedly represent the key technology in ad hoc querying. The first databases for computers were linear. One had to go through all their elements to access data. For instance, a common method was to print large blocks of information. Contrary to past database frameworks, SQL and NoSQL methods allow an analysis of single data artifacts. It’s possible to do a query for user click rates for a particular element. Ultimately, this information means it’s easy to analyze small theories fast. How is this possible? Modern SQL and NoSQL frameworks store information in honeycomb-like structures. Every data element has its separate location. As a result, separate data elements aren’t connected into one block. This approach enables the users to print out information from small elements rather than whole files on a certain topic.

2. Data filtering and extraction

The honeycomb-like model would have been useless without proper search tools. Strong organization also requires potent instruments for searching data. In this regard, SQL and NoSQL both offer a solution through data filtering and extraction. Users can filter information based on certain criteria and get it from the relevant systems. For example, imagine you want to find out how many users from the 25 to 35 age range pay for a premium version of your product. In this respect, you can set search criteria to target a particular age and a premium app version. A NoSQL or SQL framework will immediately offer you some reports on this topic. For instance, they can be in the form of a chart. With the rise of AI, this tool will become especially strong. It may even be possible to generate a full-scale written analysis in particular cases.

B. Ad hoc reporting

Another set of tools that help with ad hoc analysis includes ad hoc reporting frameworks. They have two main features that the users should consider, in our opinion:

1. Customized reporting templates

Above all, a key tool for ad hoc reporting is a customized template. A customized template is precisely what it says on the tin. It combines data presentation formats and data generation tools that enable one to report information. For example, you can use this tool to quickly create charts for your colleagues. A reporting template can download information from some SQL database and then format it for analysis. This approach will be especially potent with the rise of AI. In this respect, we believe AI will soon be able to create high-quality templates. Telemus AI , an AI development business, openly proclaims that this is one of the strongest uses for this technology. The company expects ad hoc AI reporting tools to become popular. We agree with this analysis. Customized templates aren’t only the present but also the future of the ad hoc approach.

2. Dynamic data visualization

A major element of ad hoc reporting tools also includes advanced visualization. The key to efficient learning is multimodality . A person who wants to show they’ve learned something should be able to express their knowledge through text, audio, and visuals. For instance, top learners often express their knowledge through mind maps. Linear (text-based) note-taking and presentation are typically the worst ways to structure knowledge. 

In many ways, ad hoc analysis considers this aspect. It promotes visualization for all data. For example, ad hoc tools advance visual explanations through embedded charts. Why is it so important? Many analytical concepts are difficult to explain. They involve complex subjects that require multiple years of education to understand. Proper visualization can make them more accessible to non-expert stakeholders. In this light, dynamic data visualization based on SQL and NoSQL queries is the key tool. With the help of AI and various algorithms, it’s possible to quickly generate advanced presentations for all stakeholders. Ad hoc analysis is notable not only for speed but also for simplicity.

C. Ad hoc data exploration

Apart from presentations, ad hoc tools must also have strong exploration tools. Making decisions on presentations is impossible without understanding what data you need first. For this reason, let’s look at the key data exploration tools you require here.

1. Data discovery and pattern recognition

The key tool in proper data exploration is data discovery and pattern recognition. Above all, every strong tool for ad hoc analysis must be able to quickly find data. How can it do this? The best way is to connect those frameworks to the SQL and NoSQL databases. Connection to the Internet and other data sources, such as Google Analytics, is also essential. Using it, the relevant tool can quickly get key data and present it to you. 

More importantly, modern ad hoc tools promote more and more pattern recognition frameworks. We’re especially optimistic about the rise of AI frameworks. What can they do in this respect? In our opinion, these tools automate many data analysis elements. Pedro J. Navarro from the University of Cartagena and his colleagues have presented a strong ad hoc AI-based analysis tool. It’s called 3DeepM. This tool analyzes images to then produce interesting insights on them. For instance, it’s usable for the analysis of various physical products. You can use it to find defects in the tools your company manufactures. 

Another efficient approach is to scan various dialogues with customers on social media. 3DeepM and similar AI tools find many interesting trends and assist you with capitalizing on them. Why is this tool so important? It showcases how comfortable ad hoc analysis will be in the future. We believe decision-makers will be able to skip many steps in their analysis due to this advantage. 

2. Interactive data visualization

We’ve already mentioned that a strong ad hoc solution involves data visualization. This isn’t sufficient, however. Another vital element of an efficient ad hoc framework is interactivity. The users should also be able to configure data according to their needs. In this regard, ad hoc interactive data tools are becoming increasingly popular. What’s the best way to display this information properly? In our opinion, you should look toward dashboards. A strong dashboard possesses all the key information about a certain data set. In this respect, IBM, for example, reports that ad hoc tools are essential for its internal dashboard solutions. More importantly, it’s possible to put in queries for different types of data. You can ask for a breakdown of click-through rates according to date or user type. In other cases, one can review how people listen to a particular musical style. In short, you have a vast number of options here. A well-prepared ad hoc interactive data set will likely provide data for many users over the years.

IV. Ad Hoc Analysis Tools and Techniques

Ad hoc analysis tools and techniques are numerous. In this part of the article, we want to review the most important ones:

A. Spreadsheets and data manipulation tools

Let’s first review the key frameworks for working with data analysis. In this respect, some of the most well-received tools include spreadsheet tools. Why are they so great for ad hoc analysis? In our opinion, two reasons for this exist. Firstly, these tools are all-pervasive. Most business customers actively use Excel or Google Sheets in sales. Consequently, Google Sheets or Microsoft Excel are the most common tools for storing data. Secondly, they’re also convenient. Both frameworks have advanced visualization tools.

1. Microsoft Excel

Microsoft Excel is a brand name synonymous with spreadsheets. When people talk about them, the majority means Excel. Let’s review the key advantages and problems of this platform.

On the one hand, Excel undoubtedly offers the most advanced spreadsheet tools on the market. No other instruments have the macros tools and formula capabilities of Excel. Excel is among the top choices if you want to create a complex outline of some data. It supports gigantic spreadsheets. More importantly, it analyzes them extremely fast. Professional comparisons clearly show that Excel is superior to Google Spreadsheets for complex tasks. Our experience also shows it’s more efficient than open-source and proprietary alternatives. LibreOffice and WPS Office are incapable of approaching Excel in terms of speed.

Disadvantages

On the other hand, Microsoft Excel has major issues, too. Firstly, its data visualization tools are poor. It has typical office visualization instruments such as charts. Any other approaches require outside software. Many of them are inconvenient due to being, for example, community-made. 

Secondly, a major problem with Excel is that it’s primarily desktop-oriented. Even in the 365 cloud form that Microsoft presents today, its desktop-like paradigm remains. Integration of different Excel files is often difficult, even if they’re present on one platform. Besides, one should note that Office 365 isn’t cheap. Businesses have to spend sizeable amounts of funds to buy Microsoft subscriptions. Old one-time purchases were expensive, too.

Consequently, the benefits of this platform for various non-technical frameworks can be low. In numerous instances, its spreadsheet capabilities are too complex. At the same time, the visualization capacity of those tools isn’t as advanced.

2. Google Sheets

Google Sheets will likely become a de facto standard for most spreadsheets this decade. The tool gained popularity over the 2010s and is ready to take the spreadsheet throne. Hjalmar Gislason reports that young businesses (startups) overwhelmingly use Google Sheets. Yes, Excel is more popular for now. It had 800 million users in 2018 , as opposed to approximately 200 million for Google Sheets. This trend is valid today, too. Still, the user base of Excel isn’t growing. Once older organizations facing vendor lock-in close down, we expect a massive popularity boost for Google Sheets. Why does this happen? In our opinion, ad hoc analysis capabilities are among the key explanations.

Reasons why Google Sheets is superior to Excel

Firstly, Google Sheets has better visualization capabilities. It’s a part of a massive ecosystem of analytical tools. For instance, one can connect their spreadsheet to Google Analytics. In this way, they’ll access one of the most advanced analytical platforms on the web. Secondly, Google Sheets is much more interactive. The tool promotes online collaboration for the users. As a result, it stimulates a positive data management culture. Stakeholders may combine analysis of data in Google Analytics and then analyze the spreadsheets behind them. Sharing and checking all the information is much easier than in other cases. Why is this important? This approach will make ad hoc in finance much easier. Analysts from different firms will be able to review data from their colleagues and check it for validity. With Microsoft Excel, such collaboration is much more difficult to imagine.

Future prospects

Consequently, we expect Google Sheets to continue gaining popularity. Yes, it’s simpler than Excel. However, most companies don’t need all those features. Excel will likely become an incredibly niche tool reserved for expert tasks.

B. Business intelligence (BI) and analytics platforms

Some tools are more tailored towards data analysis. Let’s look at modern business intelligence and analytics platforms in this regard. As you can see, many of them offer interesting options for a high-quality ad hoc analysis. 

Tableau has the following features that can assist you in making decisions:

• Drag-and-Drop Interface. Tableau provides a user-friendly drag-and-drop interface. It lets users quickly create visualizations by dragging and dropping fields onto a canvas.
• Instant Data Exploration: Users are able to easily explore data by interacting with visualizations. They can click on data points to learn more details and filter information. As a result, different stakeholders get an opportunity to review information from their standpoint.
• Live Connection to Data: Tableau supports live connections to various data sources. It enables users to work with real-time data. More importantly, it guarantees that the analysis reflects the most up-to-date information.

Dashboard Interactivity: Dashboards in Tableau are interactive. They allow users to change parameters and apply various filters. In this way, any person can find information that is vital to them.

Power BI is another vital tool for users who want to do ad hoc analysis. Here are some of the key elements you need to review:

• Power Query: Power BI’s Power Query Editor allows users to shape data before it’s visualized. This flexibility is crucial for ad hoc analysis, enabling users to prepare data on the fly.
• DAX (Data Analysis Expressions): Power BI uses DAX for creating calculated columns and measures. This powerful formula language allows users to perform complex calculations. What’s the problem here? Integration with Microsoft tools isn’t as potent as it could have been in this case.
• Quick Insights: Power BI has a feature called Quick Insights that automatically generates insights and visualizations based on given data. Using it, you can provide the clients with starting points for further exploration.
• Natural Language Query: Power BI supports natural language queries. It allows users to ask questions about their data using plain language. Why is this feature so vital? It enables users without technical expertise to make their findings. Analysis isn’t limited to people with technical expertise, in this case. Ultimately, this approach allows many companies to promote a data-oriented culture.

Lastly, let’s review the tools that can help organize knowledge in Qilk. In our opinion, this platform also has great tools for organizing information.

• Associative Data Model: Qlik’s associative model allows users to explore data without predefined paths. Users can make dynamic associations between different data points, facilitating ad hoc exploration.
• Drag-and-Drop Interface: Similar to Tableau, Qlik uses a drag-and-drop interface for creating visualizations. This ease of use helps users quickly build and modify charts and dashboards.
• On-the-Fly Data Transformation: Qlik allows users to transform and manipulate data on the fly. How does it do this? It uses a special in-memory engine, enabling quick iterations in ad hoc analysis.
• User Collaboration: Qlik’s associative sharing and collaboration features allow multiple users to explore and analyze data together. As a result, the platform fosters a collaborative ad hoc analysis environment.

C. Ad hoc analysis techniques

Now that we understand the tools for ad hoc analysis, it’s time to understand how to improve their utility. The best way to do this is to use specific data analysis methods. Here are some of the best options for this goal:

1. Pivot tables and charts

Let’s first analyze the things you can create in Excel and Google Sheets. In this respect, the key instruments available to the users are pivot tables and charts. What do they do? A pivot table is a summary table with all key data from certain documents. For instance, you can collect all financial data in one massive pivot table. Why is it so important? This approach allows integrating all data sources into one format. Consequently, analyzing them together and finding interesting connections between data sets becomes easier. 

And what about charts? They can help visualize the knowledge you have. Ultimately, it becomes easier to see data in movement. You can understand growth in your daily stats or problems with them. All that is necessary to create such data sets is to use spreadsheets. Modern tools in Excel and Google Sheets automate the majority of other processes.

2. Conditional formatting and filtering

Conditional formatting and filtering are another vital step in analyzing static data. The first option allows highlighting the data that fits your needs. For example, you can ask a BI platform or a spreadsheet manager to showcase financial information. In this way, one can separate important information from non-important. As for the second option, we have filtering. Filtering allows one to exclude elements they would rather not see. Ad hoc reporting tools have many configurations that enable this function. Ultimately, basic filtering is possible even with search tools. One can look only for the keywords they want to review. Why is all this essential? In our opinion, such frameworks help remove noise in information and concentrate on vital aspects.

3. Data blending and aggregation

Data blending and aggregation are significant techniques for getting insights from different datasets. In tools like Tableau, data blending integrates information from multiple sources. This approach allows users to correlate and analyze disparate data sets. This capability is vital for holistic analysis. After all, it helps uncover connections between different data elements. Aggregation, meanwhile, involves summarizing data to reveal trends and patterns. Functions like sum, average, and count provide an advanced view of the data, simplifying complex datasets. These techniques empower users to perform in-depth ad hoc analysis. Ultimately, we get an opportunity to combine and analyze data flexibly for more informed decision-making.

V. Best Practices for Effective Ad Hoc Analysis

We believe that several best practices for effective ad hoc analysis exist. Here are the key things you need to consider:

A. Ensuring data quality and accuracy

No ad hoc financial reporting would be accurate if data is forged. This factor reveals the key challenge for all types of analysis. It relies on the quality and accuracy of the available data. Many companies have tremendous amounts of data, but it’s inaccurate. For example, it may easily reflect the biases of certain influence groups. A widespread situation is for the leaders of certain departments to hide the real state of affairs. In fact, the 2008 financial crisis became an outcome of such a problem. Lehman Brothers management deliberately hid vital transaction data from the government. When the lies of the management became obvious, it was already too late.

Ultimately, we see two ways to improve the existing issues with data analysis.

Solution 1. Hiring more managers

On the one hand, you can try to hire more managers and have greater control over the workers. What’s the concern here? Your firm may eventually become overmanaged. Even today, we have one manager per 11 people. If this number goes down, up to a fifth of our workforce can be made of managers. Why is this so bad? A tremendous number of non-productive workers will appear in the economy.

Solution 2. Using automation

On the other hand, you can start using more and more automatic tools for analysis. Modern AI can find various trends in user behavior without human input. More importantly, its complexity makes the majority of its choices independent. What’s the problem with this approach? It’ll likely be costly in the short run, requiring major investments into training and development. More importantly, you’ll have to find people with unique skills for your workforce. What are some control-oriented competencies for getting high-quality data through AI? In our opinion, the relevant workers must be able to process data fast and have a good eye for spotting irregularities. Otherwise, you risk distorting the data through hallucinations common for modern AI .

B. Defining clear objectives and questions to be answered

A key goal for advanced ad hoc analysis is asking a specific question. This approach allows you to test one hypothesis instead of several issues. In this respect, you should learn to define clear objectives and present proper questions. How to do this? In our opinion, you should learn the following skills:

Prioritizing information

You should understand how to prioritize information. We recommend training your deep processing skills to achieve this. Deep processing involves the ability to find key trends in the information you’re reading. According to Bloom’s Taxonomy of Learning , you must have skills in knowledge analysis, evaluation, and creation to perform deep processing. How to train those skills? We have an interesting exercise for doing this: try to present all your learning and analysis in a minimal format. Your goal is to have a minimal word count for all those tasks. Why is this important? The fewer words you use, the more likely you’ll see vital information. Feynman’s technique is great, too: try to explain your analysis to a five-year-old or a non-expert. If you can do this, then your explanation is clear enough.

Being mindful and present

You must be constantly present in your work. Various Nobel-worthy inventions stem from chance discoveries. For instance, penicillin was discovered by chance , according to the National Library of Medicine (U.S.). Some readers may claim that people like Alexander Fleming were lucky. We disagree. They had one major virtue many people don’t have. This virtue is attention. They were present in their work and managed to see trends others couldn’t discover. You should learn to do the same thing if you want to succeed. In this respect, the best practice is to try mindfulness meditation. You should learn to concentrate for periods that are as long as possible.

C. Utilizing appropriate data visualization techniques

We’ve already mentioned that visualization is the basis of high-quality ad hoc analysis. Let’s review the best practices for data visualization. Firstly, you should learn to minimize information in your visuals. It’s fine to have detailed breakdowns, but they shouldn’t be the highlight of your presentation. Put them at the end of the visualization. The “face” of your presentation should be simple. In this regard, the key goal is to choose and showcase the key metric. 

Secondly, we recommend learning several data presentation formats. In this light, you should understand how line graphs and pie charts work. Moreover, it’s important to test all data points with multiple elements. Certain information is much easier to see in pie charts than in area charts. 

Lastly, we recommend paying attention to high-quality visuals. Modern charting tools in Microsoft Excel and Google Sheets are so potent that you rarely need changes. Simultaneously, if you use BI frameworks, it may be beneficial to use outside tools. If your company has site designers, you can also ask them to present findings for you in a proper format. Ad hoc analysis should look as clean as possible. Only this approach will help attract enough attention to it. 

D. Collaborating and sharing insights with stakeholders

The final element of all ad hoc analysis is proper collaboration. Your goal is to prepare data for a specific set of stakeholders. This factor allows us to raise several additional points. The primary one is that all your ad hoc analysis presentations should be interactive. Your audience must be able to access a presentation and then search for insights it wants to find. The preceding steps are essential to make this process possible.

Why is collaboration and insight sharing with stakeholders so important? Ad hoc analysis is extremely fast and often involves new trends. This information means missing vital elements in the key data is easy. When you share data with stakeholders, you decrease the risks of missing significant insights. For example, marketing and development departments can uniquely contribute to AI analysis. In this way, sharing enables a truly multifaceted analysis. When multiple people from your organization provide their opinions, maximizing the benefits of insights is easier.

VI. Challenges and Limitations of Ad Hoc Analysis

Nonetheless, ad hoc analysis also offers some significant challenges and limitations. We believe it’s essential to understand the problems of the methods you use to avoid major issues. So, what are the key challenges? Here they’re:

A. Data security and governance concerns

Data security is the first concern of all analyses, including ad hoc ones. Most types of ad hoc reports are available only via Internet tools. In this light, situations when a breach in some service can leak vital insights are common. Data leaks happen every year. They affect both large and small firms. More importantly, they impact both “serious” and “relaxed” companies. For instance, there was a major information leak from Equifax in 2017. Equifax is one of the best credit reporting businesses in the American market. Engadget reports that Signal, a messenger proclaiming itself to be among the safest, also had data breaches due to third-party troubles . Consequently, you should consider maximal security for your ad hoc data. 

B. Risk of misinterpretation and misuse of data

Another major problem of ad hoc analysis is misinterpretation. Misinterpretation can happen in “bigger” analysis, too. However, ad hoc analysis is especially prone to it due to being speed-oriented. 

Still, there’s an additional issue. Ad hoc analysis is also limited to particular challenges. As a result, it rarely gives enough insights into the bigger picture. For instance, various businesses misinterpreted AI based on its presentations. A common reaction was to downsize content teams. In reality, LLM AI models often hallucinate.

Consequently, companies like CNET faced massive reputation concerns due to rash decisions. In this case, AI texts produced for CNET proved to be low-quality and full of falsehoods . Ad hoc analysis performed when this technology arose was incorrect. It was too fast and too limited when it came to data.

C. Scalability and performance limitations

Lastly, many types of ad hoc analysis aren’t scalable. This issue manifests in two ways. On the one hand, the results of this analysis tend to be isolated. Advancing it to bigger problems in your firm is difficult. On the other hand, various tools of this type are inadequate for expanding analysis. Their goal is to be user-friendly and visually appealing. This means many visualization tools for ad hoc analysis don’t work well with large amounts of data. In many situations, ad hoc analysis will be limited to some particular challenges.

VII. Integrating Ad Hoc Analysis with Traditional Analytics Approaches

Ad hoc analysis primarily helps with transforming old theories. At the same time, it has difficulties with scalability. In this light, you should learn to integrate ad hoc analysis with traditional approaches.

A. Balancing ad hoc analysis with predefined reports and dashboards

You should first learn to balance ad hoc analysis with predefined reports and dashboards. In this regard, we believe the best choice is to look at the philosophy of science. In general, two schools exist concerning the development of scientific knowledge. Some philosophers think science develops through evolution. Others proclaim that it goes through major revolutions. In our opinion, both approaches are correct. Past theories are the lower level of knowledge. They describe some particular cases in bigger theories. Revolutions are a part of a bigger evolution.

Consequently, ad hoc analysis is the mechanism for such transformation. It helps transform past business theories into parts of newer and bigger ones. Here are the key three steps we recommend:

• Never throw away past theories. Learn how to integrate them.
• Use ad hoc analysis to challenge old assumptions and create bigger theories.
• Understand that absolute truth doesn’t exist: our knowledge is constantly expanding.

B. Incorporating ad hoc insights into business processes and decision-making

Another important step we recommend is to make ad hoc analysis a part of your processes. It shouldn’t just be a random activity in your company. No, you must perform ad hoc analysis as a part of a bigger analysis. What approach do we recommend? 

• Create large-scale theories based on well-verified data;
• Use ad hoc analysis to review new trends and challenge the existing strategies.

C. Establishing a data-driven culture within the organization

Lastly, establishing a data-driven culture in your organization is a major aspect of ad hoc analysis. Ad hoc tools are simple to use. In this light, you should promote them among as many people as possible. A positive data-driven culture involves two elements:

• Thorough approach to data collection;
• The ability of every employee to participate in the analysis.

How to promote it? The best option is to offer rewards for interesting insights. Workers who engage in the analysis should receive monetary bonuses. Another significant aspect is to advance worker democracy. A strong data-driven culture demands freedom of speech. Your workers should be able to present their vision of various challenges.

VIII. Future Outlook: The Role of Ad Hoc Analysis in the Age of Big Data and AI

No ad hoc report example is possible without mentioning innovations transforming ad hoc analysis.

A. The impact of Big Data on ad hoc analysis requirements

The first technology impacting ad hoc analysis is Big Data. In our opinion, it’s obvious why this innovation is so vital. Big Data greatly improves two aspects. Firstly, it raises the amount of available information. Secondly, it allows for reviewing more available data in general. As a result, it’s possible to analyze more and see greater trends. If you want to do a strong ad hoc analysis, learning Big Data is essential.

B. Leveraging AI and machine learning for advanced ad hoc analysis

Another important technology for ad hoc analysis is AI. Machine learning tools behind it are notable for their ability to review a lot of data. For example, ChatGPT 3.5 uses 45 terabytes of information as its basis. In this light, you can train machine learning models to analyze tremendous amounts of data. In this way, it’ll be possible to create Big Data that will highlight some genuinely in-depth issues.

C. The importance of ad hoc analysis in the era of data democratization

Lastly, ad hoc analysis is central because data is becoming increasingly open. Today, most people can access tremendous repositories of data. For instance, sites like Statista offer a lot of open information. If there’s an opportunity to analyze modern trends in the market, why not use it? After all, many types of information are free. Many governments and private organizations share a lot of insights.

IX. Conclusion

To summarize, ad hoc analysis is a strong tool for modern companies. It’s crucial for reviewing new trends in the market. In many ways, ad hoc assumptions can become a path to transforming your entire business model. For this reason, we recommend every firm on the market to try this practice. The market is changing all the time. Ad hoc analysis may help you avoid this problem. If you want assistance with this practice, don’t hesitate to contact professionals. In this respect, Keenethics has over eight years of experience developing software for various businesses.

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A Case Study on Mobile Adhoc Network Security for Hostile Environment

A mobile adhoc network (MANET) is a peer to peer wireless network where nodes can communicate with each other without infrastructure. Due to this nature of MANET; it is possible that there could be some malicious and selfish nodes that try to compromise the routing protocol functionality and makes MANET vulnerable to Denial of Service attack in military communication environments. Hence security is an important challenge while deploying MANET. This research effort examines the case study for a Layerwise Security (LaySec) framework that provides security for an ad-hoc network operating in a military environment. LaySec incorporates three security features (Secure neighbor authentication and Layerwise Security techniques and multipath routing) into its framework while maintaining network performance sufficient to operate in hostile environment. layerwise security protocol has been implemented and simulated on Qualnet 5.0. Based on the simulation result, it is observed that the proposed approach has shown better results in terms of Quality of Service parameters like Average packet delivery ratio, Average throughput, Average end to end delay and Routing Overhead.

Related Papers

Parthasarathy Subashini

A mobile ad-hoc network (MANET) is a peer-topeer wireless network where nodes can communicate with each other without any infrastructure. Due to this nature of MANET, it is possible that there could be some malicious and selfish nodes that try to compromise the routing protocol functionality and makes MANET vulnerable to Denial of Service attack in military communication environments. The ultimate goal of the security solutions for MANET is to provide security services, such as authentication, confidentiality, integrity, anonymity, and availability to mobile users. To achieve the goals, the security solution should provide complete protection across the entire protocol stack. The primary focus of this work is to provide transport layer security for authentication, securing end-to-end communications through data encryption and to provide security services for both routing information and data message at network layer. It also handles delay and packet loss. This paper considers military scenarios and evaluate the performance of Securityenhanced- Multicast AODV (Ad hoc On-demand Distance Vector Routing) routing protocol called SNAuth-SPMAODV (Secure Neighbor Authentication Strict Priority Multipath Ad hoc On-demand Distance Vector Routing) with WTLS to minimize the packet dropping by Denial of Service attack (DoS) in the network by applying WTLS in SNAuth- SPMAODV routing protocols and compared the results without WTLS protocols. The protocol discovers multiple paths between sender and receiver nodes without introducing extra packets into the network and authenticates the neighbor offering robustness in a secured MANET. The simulation results demonstrates the success of the proposed approach and maximizes the overall performance of MANET in presence of Denial of Service attack.

Military Technical Institute, Defensive Technologies Department, Material Resources Sector, Republic of Serbia Ministry of Defence

Dragan Mladenovic, PhD

Mobile ad hoc networks (MANET) are becoming increasingly popular wireless networking model in various areas of technology usage from every-day to specific military purposes. Security in ad hoc networks is a key concern in order to provide protected communication between mobile nodes in an unknown and heterogeneous environment. The main challenge of MANETs is their vulnerability to security attacks and how to operate securely and efficiently while preserving its own resources. This paper discusses the most common type of attack on MANET and their security challenges. There are a lot of existing solutions in this field which propose increasing of MANETs security by new protocol design, evaluate existing protocols and their abilities, propose new routing protocols, find solutions and prevention measures for existing attacks or improving specific characteristics of MANET such as the packet drop rate, the overhead, end-to-end packet delays, network throughput etc.This paper attempts to provide an overview of MANET security challenges.The paper finishing by presenting current and possible solutions as a problems requiring for further research in the future.

IJRCAR JOURNAL

A Mobile Ad-hoc NETwork (MANET) is an autonomous collection of mobile users that communicate over relatively bandwidth constrained wireless links. One of the main issues in such networks is performance- in a dynamically changing topology; the nodes are expected to be power-aware due to the bandwidth constrained network. Another issue in such networks is security - since every node participates in the operation of the network equally, malicious nodes are difficult to detect. There are several applications of mobile ad hoc networks such as disaster recovery operations, battle field communications, etc. To study these issues, a scenario based simulation analysis of a secure routing protocol is done and is compared with traditional non-secure routing protocols. The scenarios used for the experiments depict critical real-world applications such as battlefield and rescue operations, which tend to have contradicting needs. An analysis of the tradeoffs between performance and security is done to gain an insight into the applicability of the routing protocols under consideration.

Vanitha Kumar

The Mobile Adhoc Network (MANET) consists of several wireless mobile nodes and due to its mobility their network topology changed dynamically. So the network does not have any fixed infrastructure. In this paper discussed about the various problem and security attacks in each layer in the MANET and also about what are the security services in mobile adhoc network. Routing in adhoc network is main issues because of node mobility and limited resource. Here we discussed two types of routing protocol In this paper also reviewed about the working principles of on demand routing protocol AODV and DSR.Finally compare both the protocol. This will provide guidance for the security related research works in this area.

Journal of Global Research in Computer Sciences

Vijay Singh Rathore

With the proliferation of cheaper, smaller, and more powerful mobile devices, mobile ad hoc networks (MANETs) have become one of the fastest growing areas of research. This new type of self-organizing network combines wireless communication with a high degree node mobility. Unlike conventional wired networks they have no fixed infrastructure (base stations, centralized management points and the like). Mobile ad hoc networks (MANETs) are one of the fastest growing areas of research. Conventional networks use dedicated nodes to carry out basic functions like packet forwarding, routing, and network management. In ad hoc networks these are carried out collaboratively by all available nodes. In this paper, we’ll discuss about the MANET specific attacks, security challenges, goals and protocols alongwith the techniques used to secure MANETs.

Journal of Electrical Engineering

Ladislav Hudec

Security in mobile ad hoc networks (MANETs) has been an actively researched topic for the several years. As opposed to wired networks, MANETs have dynamic topology, limited resources, limited bandwidth and are usually deployed in emergency scenarios outside, where landscape plays important role. MANETs are susceptible to insider and outsider attacks and bring new security challenges which were not present in the wired networks. The most important difference is that every node in MANET acts as a router and routes traffic throughout the network. Compromising one node can hugely affect network performance. In this paper, we present our security architecture for MANETs which secures important aspects of the network. We bring trust model into the network and nodes are secured by different mechanisms tailored specifically for the use in distributed environment. We use Omnet++for network simulations. Simulations use delays measured on the real hardware and we analyze performance of the net...

International Journal of Engineering Research and Technology (IJERT)

IJERT Journal

https://www.ijert.org/an-analysis-of-issues-in-security-and-routing-protocol-in-manet https://www.ijert.org/research/an-analysis-of-issues-in-security-and-routing-protocol-in-manet-IJERTV3IS10391.pdf The Mobile Adhoc Network (MANET) consists of several wireless mobile nodes and due to its mobility their network topology changed dynamically. So the network does not have any fixed infrastructure. In this paper discussed about the various problem and security attacks in each layer in the MANET and also about what are the security services in mobile adhoc network. Routing in adhoc network is main issues because of node mobility and limited resource. Here we discussed two types of routing protocol In this paper also reviewed about the working principles of on demand routing protocol AODV and DSR.Finally compare both the protocol. This will provide guidance for the security related research works in this area.

MANET is a kind of AdHoc network with mobile, wireless nodes. Because of its special characteristics like dynamic topology, hop-by-hop communications and easy and quick setup, MANET faced lots of challenges allegorically routing, security and clustering. The security challenges arise due to MANET’s self-configuration and self-maintenance capabilities. In this paper, we present an elaborate view of issues in MANET security. Based on MANET’s special characteristics, we define three security parameters for MANET. In addition we divided MANET security into two different aspects and discussed each one in details. A comprehensive analysis in security aspects of MANET and defeating approaches is presented. In addition, defeating approaches against attacks have been evaluated in some important metrics. After analyses and evaluations, future scopes of work have been presented.

International Journal of Communication Systems

Mengchu Zhou

A mobile ad-hoc network (MANET) is a wireless network where nodes can communicate with each other without infrastructure. Due to this nature of MANET, some malicious and selfish nodes that try compromise the routing protocol functionality and makes MANET vulnerable to Denial of Service attack in military communication environments. This paper considers military scenarios and evaluates the performance of security-enhanced-Multicast AODV (Ad hoc On-demand Distance Vector Routing) routing protocol called SNAuth-SPMAODV (Secure Neighbor Authentication Strict Priority Multipath Ad hoc On-demand Distance Vector Routing) with Session Initiation Protocol (SIP) provides application layer and network layer security and authenticates the neighbor is robust against Denial of Service attack.The SNAuth-SPMAODV protocol has been implemented and simulated on Qualnet 5.0.The performance metrics used to evaluate the proposed method such as packet delivery ratio, end to end delay, throughput, routing overhead and jitter.

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Ad Hoc Crisis Response and International Organisations (ADHOCISM)

• New Projects
• October 14, 2021
• Time to Read: 8 min

International organisations (IOs) are created with the aim of solving collective action problems when a crisis arises. Yet, member states have repeatedly established ad hoc crisis responses in situations where IOs might be expected to play a central role. ADHOCISM asks what is the impact of ad hoc crisis responses on international organisations? In this way, ADHOCISM wants to contribute to filling this knowledge gap through a systematic study of ad hoc crisis responses in two policy domains: security and health. With this paired comparison, ADHOCISM wants to tap into a broader empirical governance phenomenon. Ad hoc crisis responses are here understood as loose groups of actors that agree to solve a particular crisis at a given time and location outside of an existing international organisation in the same policy domain. Ad hoc crisis reponses can, in the short-term, lead to more rapid and effective crisis responses among like-minded states, but if international organisations are no longer seen as the principal instruments to confront global challenges, the risk is also that the relevance of these international organisations will diminish, and similar trends may unfold in other domains.

The complex web of international and (sub-)regional organisations has been one of the principal subjects of inquiry in international relations. While, initially, much scholarly attention went to explaining the proliferation of IOs, focus gradually shifted to studying the effects of this wider palette of options ( Alter & Meunier, 2009 ; Hofmann, 2019 ; Jupille, Mattli, & Snidal, 2013 ). A central claim has been that memberships in institutions with similar mandates increases the chances of forum-shopping , reflecting a functionalist logic ( Drezner, 2009 ; Hofmann, 2009 ). Member states can nowadays select from an increasingly broad menu of options in global governance, ranging from traditional multilateral strategies by working through formalised IOs, minilateral solutions via so-called ‘governance clubs’ to informal governance ( Rogers, 2020 ), of which loose ad hoc crisis responses are an integral part. The result is an era of “contested multilateralism” ( Morse & Keohane, 2014 ) or “global governance in pieces” ( Patrick, 2015 ). Governance clubs and informal multilateralism or ad hoc coalitions are often seen as more effective, flexible and nimble than IOs. At the same time, they are criticised for lacking legitimacy.

Scholars tend to agree that IOs at global, regional and sub-regional levels overlap in terms of mandate and memberships, which can lead to cooperation or competition to be first responders ( Brosig, 2010 ). More recently, Hofmann ( 2019 ) has highlighted how the interplay between membership overlap and preference diversity might not only lead to forum-shopping, but also to “brokering” and, more disruptively, even “hostage-taking”. In general, membership overlap between institutions with a similar geographical and functional mandate is seen as offering states the chance to pick and choose the vehicle that best suits their interests (see e.g. Haftel and Hofmann, 2019 ). Obvious examples include the EU’s and NATO’s security architecture, but military crisis response interests by the AU and the Economic Community of West African States (ECOWAS), ad hoc coalitions like the Joint Force of the Group of Five Sahel (JF-G5S) in Mali, the Multinational Joint Task Force (MNJTF) fighting Boko Haram in northern Nigeria and the Contact Group on Piracy off the Coast Somalia (CGPCS) are equally good illustrations. As such, inter-organisationalists not only highlight these opportunities of forum-shopping and cooperation, they also increasingly stress the risk of rivalry between institutions, such as competition for resources and legitimacy ( Biermann & Koops, 2017 ; Brosig, 2017 ). What we thus see is an increasing literature theorising the effects of overlapping organisations, but so far with a blind eye towards the impact of ad hoc coalitions on the multilateral system.

For many good reasons, ad hoc coalitions are mostly viewed in a positive fashion. They are seen as giving member states more choice and flexibility, inter alia creating “a framework for states to cooperate while pursuing their national interests” ( Brubacher, Damman, & Day, 2017, 11 ). Ad hoc coalitions also avoid bureaucratic delay and do not create precedents for future crises responses ( Reykers & Karlsrud, 2017 ). They enable states to pursue national interests ( Brubacher et al., 2017 ) and avoid bureaucratic delay and future precedents ( Karlsrud and Reykers, 2019 ). Rynning ( 2013 ) has underscored the strategic need for better connecting coalitions of the willing, institutions and so-called “tents” or contact groups – where ad hoc coalitions are viewed as the “sharp end of the spear” and IOs and broader groupings of like-minded nations can offer necessary strategic guidance and political legitimacy. Other scholars have focused more on the on-the-ground effects of institutional proliferation and ad hoc coalitions. Ad hoc coalitions allow states to remain more in control – of, for instance, their military troops or personnel – and they provide an opportunity for “pivotal states” to “buy allies” through financially or politically rewarding third parties “to serve in multilateral coalitions”, in order to pursue national goals ( Henke, 2019 ; see also Stone, 2013 ). However, this approach does not explain the continued investment in rapid response mechanisms such as the AU African Standby Force, the EU Battlegroups or the NATO Response Force, all of which have not been put to use to date.

A key problem is that the term ‘ad hoc coalitions’ is generally used as a catch-all concept, which does not reflect empirical complexity. Ad hoc coalitions can differ in e.g. duration, resources, membership, geographical scope and relationship to formal IOs ( Karlsrud and Reykers 2020: 1527-29 ). Because we lack understanding about what ad hoc crisis responses are, we also do not know how different ad hoc coalitions might affect existing and emerging IOs. We do not know if, when, and how these ad hoc coalitions compete with, or perhaps even undermine, established or developing IOs. In this way, also the long-term effects of ad hocism and the resilience of IOs to this phenomenon remains a black-box.

Cases and methods: To advance knowledge on ad hoc coalitions, ADHOCISM will establish a dataset on ad hoc crisis responses in global health and security. In health, the case study will be on the relationship between the World Health Organization (WHO – IO) and the Vaccine Alliance (Gavi), the Coalition for Epidemic Preparedness Innovations (CEPI) and the joint COVAX project. In the security domain our case studies will be the AU African Standby Force and EU Battlegroups (IOs) and the Multinational Joint Task Force fighting Boko Haram (MNJTF); the Joint Force of the Group of Five Sahel (JF-G5S) and Barkhane, primarily in Mali; and the Contact Group on Piracy off the Coast of Somalia (CGPCS).

ADHOCISM will also quantitatively and qualitatively map select member states’ strategic choices to explore and explain variation among ad hoc coalitions, and their relationship with IOs in the same domain. Through a set of case studies, it will make a significant academic contribution to our understanding of the complex interrelations between member states, ad hoc coalitions and IOs.

Besides Karlsrud and Reykers, the team includes Malte Brosig (University of Witwatersrand), Stephanie C. Hofmann (EUI and Collaborator, NSA Network) and Pernille Rieker (Norwegian Institute of International Affairs).

The authors: John Karlsrud (Norwegian Institute of International Affairs) and Yf Reykers (Maastricht University and Collaborator, NSA Network)

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Bibliographic Reference

John Karlsrud & Yf Reykers, “ Ad Hoc Crisis Response and International Organisations (ADHOCISM) ,” New Projects, Network for Strategic Analysis, 14 October 2021.

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Developing reliable backend operations and public-facing access to healthcare.gov.

To ensure millions of Americans have secure and reliable access to manage their healthcare enrollment, Ad Hoc supports the Centers for Medicare & Medicaid Services (CMS) in maintaining the site’s backend technical needs and the frontend user experience.

Redesigning Search.gov for trust and efficiency

Search.gov was designed to be a reliable, quality tool to help government web teams. However, research showed the site had navigation and other difficulties, so we set out to improve specific pain points while also making it more attractive to new users.

Enabling Veterans to schedule COVID-19 vaccinations online

When the COVID-19 vaccine became available, the VA needed an online option to help as many Veterans as possible schedule their vaccine appointments. We chose to use existing infrastructure to shorten the time needed to build the tool, which provided Veterans with an efficient, user-friendly experience.

Modernizing how Medicare beneficiaries find and enroll in plans

Ad Hoc worked with the Centers for Medicare & Medicaid Services to rethink how CMS could replace the decade-old Medicare Plan Finder tool with one that better serves Medicare beneficiaries and those who help them.

Creating a COVID-19 dashboard to help the VA prioritize their response

During the COVID-19 pandemic, the Department of Veterans Affairs needed to respond quickly and effectively to best serve Veterans and their families. Ad Hoc, in support of prime contractor Oddball, helped the VA build a COVID-19 digital response dashboard that included call center feedback and website usage to help measure the trajectory and success of the VA's COVID-19 digital services.

Retooling Find Local Help using human-centered design

When using the Find Local Help tool to connect with experts who could help with health insurance enrollment, users ran into difficulties with the site. Ad Hoc partnered with the Centers for Medicare & Medicaid Services to uncover the issues and build a simpler, more efficient process.

Using product management to meet user needs and business goals on Search.gov

To meet the needs of those using Search.gov and increase its presence in the market, Ad Hoc, along with prime contractor Fearless and the Search.gov team, incorporated product management processes and focused strategies to guide our work and improve the site that provides search results for more than 2,200 government websites.

Accelerating how agencies move from zero code to launch

When people shop for health insurance plans on HealthCare.gov, more than a quarter of API traffic is from people trying to determine if a plan covers their doctors and medication. This coverage data changes constantly, so to meet the demand, Ad Hoc helped the Centers for Medicare & Medicaid Services (CMS) develop a website to validate coverage data. And we were able to build it in less than a month.

Developing data-rich resources for teams building on VA.gov

To support the growing number of teams working on the Veteran-facing Services Platform (VSP), Ad Hoc’s Platform Analytics team created a standardized, scalable data warehouse and visualization tools that would meet the various teams’ needs and provide meaningful data for them to use when designing their products.

Expanding equality through accessible data

When the Consumer Financial Protection Bureau held a one-week Innovation Tech Sprint, Ad Hoc responded with prototypes, content recommendations, and a long-term plan CFPB could use to create an effective developer experience that expanded access and use of home mortgage data.

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