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The implications and impact of artificial intelligence, big data and HR analytics in HRM: A critical analysis of EU enterprises

KIU, CHUN,TUNG,THOMAS (2023) The implications and impact of artificial intelligence, big data and HR analytics in HRM: A critical analysis of EU enterprises. Doctoral thesis, Durham University.

This study offers a critical evaluation of HR analytics. Specifically, the ideas and concepts surrounding HR analytics, such as what is HR analytics, the development of HR analytics in organizations and how it may impact organizational performance. To advance and answer these research questions, this study relied on systematic reviews, logistic regression, interaction effect analysis, and interviews with the European Company Survey (ECS) to assess the interrelationship between HR analytics and organizational factors. Based on the findings, certain key areas are addressed. Firstly, research question 1 has succeeded in developing a more systematic and coherent definition of HR analytics and artificial intelligence in HR. It has also successfully identified some factors that influence the use of HR analytics in organisations. In particular, the results of study two found that factors such as firm age, firm size, the complexity of the firm process and the type of variable pay systems have been shown to be key indicators of why certain companies use HR analytics while others do not. Furthermore, the results for study three also provided a bigger picture of how organizational factors might be the reasons for explaining firms’ financial returns when examining the relationship between variables. In particular, factors such as employee motivation, the use of HR analytics, and variable pay systems are also believed to be critical in determining which factors affect a company’s financial returns. In addition, the study provides additional knowledge for five specific areas in analytics and artificial intelligence in HR, namely firm characteristics, challenges, key reasons to adopt HR software, new trends and user traits.

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How to be great at people analytics

A decade ago, someone touting the benefits of “people analytics” probably would have been met with blank stares. Was there value to be gleaned from HR data? Absolutely. But firms were thinking more narrowly about the potential—focusing on core HR systems and gathering straightforward information, such as snapshots of regional head counts or the year’s average performance evaluation rating, rather than using analytics capabilities to manage talent and make evidence-based people decisions.

Today, however, the majority of large organizations have people analytics teams, 1 Innovation generation: The big HR tech disconnect 2019/20 report , Thomsons Online Benefits, July 24, 2019, thomsons.com. 70 percent of company executives cite people analytics as a top priority, 2 “How people analytics can change an organization,” Knowledge@Wharton, May 23, 2019, knowledge.wharton.upenn.edu. and there’s little argument that people analytics is a discipline that’s here to stay. What’s striking, though, is the different ways that firms have approached building their people analytics functions. Team size, composition, and organization vary widely, and priorities for capability development and maturation differ significantly.

Most companies still face critical obstacles in the early stages of building their people analytics capabilities, preventing real progress. The majority of teams are still in the early stages of cleaning data and streamlining reporting. Interest in better data management and HR technologies has been intensive, but most companies would agree that they have a long way to go.

Leaders at many organizations acknowledge that what they call their “analytics” is really basic reporting with little lasting impact. For example, a majority of North American CEOs indicated in a poll that their organizations lack the ability to embed data analytics in day-to-day HR processes consistently and to use analytics’ predictive power to propel better decision making. 3 Based on responses of participants at a McKinsey roundtable of 45 chief human-resources officers in the autumn of 2016. Frank Bafaro, Diana Ellsworth, and Neel Gandhi, “ The CEO’s guide to competing through HR ,” McKinsey Quarterly , July 24, 2017. This challenge is compounded by the crowded and fragmented landscape of HR technology, which few organizations know how to navigate.

So, while the majority of people analytics teams are still taking baby steps, what does it mean to be great at people analytics? We spoke with 12 people analytics teams from some of the largest global organizations in various sectors—technology, financial services, healthcare, and consumer goods—to try to understand what teams are doing, the impact they are having, and how they are doing it.

Stairway to impact

It helps to think about the growth trajectory of a people analytics team as a stairway with five steps (Exhibit 1). The best teams don’t climb directly from one step to the next one; they are constantly iterating—retracing their steps and climbing the same stairs again—at every level of the journey to the top.

To move from the first step of the stairway (poor data) to the second step (good data), an organization must focus on building a foundation of high-quality data. This usually means that data needs to be extracted from the transactional systems where it is entered and then reshaped, cleaned, and re-coded into a more manageable and easier-to-understand structure that is aligned to the goals of the people analytics team. The more that analysts and data scientists need to clean and recode data to make it usable for even simple analysis, the less efficient the analytics team will be and the longer it will take to develop its skills and capabilities. This is arguably the most difficult step to get right. Significant resources, time, and investment are required to identify and manage core HR data systems, establish a common language and consistent data structure, and determine a basic set of guidelines for data collection, processing, and engineering. These are iterative processes, with no definitive solutions; rather, the processes and their outcomes change as the internal and external talent environments shift, systems are retired and renewed, and links are established among HR teams such as recruiting, training and development, and employee benefits.

As the operating environment changes at an increasingly rapid pace, both capabilities and the technology used to manage and transform data need to be increasingly flexible. In people analytics, as in many other tech-enabled fields, taking an agile approach is now a fundamental requirement. People analytics teams must work together with their enterprise-wide technology groups in a rapid and nimble way to institute new technology platforms, evolve existing infrastructure, and maintain consistent enterprise-wide standards.

Once a strong data foundation is in place, the people analytics team can climb to the third step, making the useful data accessible to the organization and experimenting with new technologies to analyze and disseminate the data. The sophistication that organizations are able to achieve at this step is variable. At the simplest end of the spectrum, teams might focus on automating and visualizing HR dashboards via standard business-intelligence platforms such as Tableau, in order to generate standard reports or respond to ad hoc requests. More advanced teams might prioritize custom builds and software development for self-serve applications, perhaps using their own front-end developers.

It’s evident from our interviews that organizations arrive in different ways at the ability to put data and actionable insights into the hands of decision makers. At several points, organizations must make decisions related to technologies and platforms—decisions such as whether to use homegrown talent or third-party vendors—and the answers vary by organization. As one would expect, the ability to attain advanced automation and self-serve capabilities depends greatly on the quality and accessibility of the underlying data.

Teams that mastered descriptive and automated reporting at step three are ready to climb to step four and build advanced-analytics capabilities. Data scientists, rather than business-information specialists, use programming languages like R, Python, and Julia to join disparate sources of data, build models to help understand complex phenomena, and provide actionable recommendations to leaders making complex and strategic business decisions.

We spoke to people analytics teams at a handful of organizations that are experimenting heavily at this level of the stairway and still have significant room to grow as their companies become open to new statistical tools, scale their data-science talent bench, and pursue a wide range of use cases. While some companies employ “broad-spectrum” data scientists who work cross-functionally to support a wide range of business needs, we found that the most advanced teams have created specific subspecialties in data science (for example, natural-language processing, network analytics, and quantitative psychometrics). These allow people analytics teams to increase their impact on their organizations by providing the advanced insights necessary to support strategic decision making on diverse and complex types of talent issues.

No people analytics team we interviewed has been able to take a full fifth step to reach the top level of the stairway: creating reliable, consistent, and valid predictive analytics. Reliable predictions will enable people analytics teams to analyze and explore practical options for management action. While some organizations have built fit-for-purpose predictive models—mostly for workforce planning—implementing predictive analytics in the context of employee selection, development, or engagement decisions requires a substantially scaled-up data-science operation, massive amounts of highly accurate data (“very big data”), cutting-edge algorithmic technology, and organizational comfort with how to address the impact on fairness and bias.

Beyond the required resources and the complexity of the analytics techniques, the infrastructure also poses a challenge to scalability and could require the use of cloud services. Most of the teams we spoke with are still working from on-premise technological infrastructures and show few signs of migrating their data and analytics capabilities to cloud services in the near future.

Ingredients for success

Our conversations with people analytics teams in leading organizations reveal a set of six best-in-class ingredients that have helped to propel the teams’ impact, success, and continued growth. These ingredients fall into three main categories: data and data management, analytics capabilities, and operating models. If we were to build a leading people analytics team from scratch, this is what we would strive for.

Data and data management

All great analytics teams are enabled by strong data standards, engineering, and management, and our interviews confirmed that this is no different in people analytics.

Significant and dedicated data-engineering resources. We found that the greatest team differentiator was the level of dedicated data-engineering resources available to it for propelling data creation and quality control. The leading teams have full ownership of their own data repositories, allowing them to rapidly test new ideas, iterate, and reduce dependencies on enterprise-level technology resources.

An added benefit of dedicated data-engineering resources is that they enable strategic alignment. Data engineers who are steeped in the strategic context of their organization’s people analytics teams are able to design the data foundation and analytics solutions more thoughtfully and deliberately from the beginning.

Breadth and depth of data sources. Leading teams have invested heavily in a strong HR-data foundation but also have advanced ways of going beyond the core HR systems to use several additional internal sources of data. The most straightforward way might be seamlessly linking the HR data with finance data, though data priorities will differ depending on organizational context. A few teams have begun to step beyond relational databases to build graph databases 4 A type of NoSQL database, graph databases are able to model relationships within data in a powerful and flexible manner. For more, see Antonio Castro, Jorge Machado, Matthias Roggendorf, and Henning Soller, “ How to build a data architecture to drive innovation—today and tomorrow ,” June 3, 2020. for advanced network analytics. In addition, leading teams have a robust and flexible survey strategy for monitoring employee sentiment. They are also able to integrate their survey data with multiple other data sources to create multidimensional quantitative and psychometric models that help explain employee engagement trends and dynamics.

While it is common for people analytics teams to feel constrained by a lack of easily available data, leading teams are more creative with data, acquiring new sources or combining existing ones in new ways to attack the problem at hand. For example, time-sheet data could be transformed and loaded into a graph database and linked by activity or project codes to allow better analysis of teamwork and collaboration.

Analytics capabilities

Advanced people analytics projects can require both deep technical knowledge and the ability to integrate and translate across a wide array of expertise and input. The best teams are building their talent bench with breadth and depth.

Robust data-science function. As we expected, all the leading people analytics teams we interviewed have invested heavily in acquiring data-science talent, though their approaches differ. Some teams focus on hiring “all-around athletes,” while others prioritize specialized backgrounds such as quantitative psychometrics or natural-language processing. Leading teams have sizable data-science “pods” that span a wide range of advanced analytical methodologies, programming languages, and academic backgrounds. The best teams hire and develop specialists in specific disciplines of data science but nevertheless expect all of these individuals to operate in a nimble, cross-functional way in order to meet evolving needs.

Strong translation capability. Leading teams also complement their high-caliber technical talent with skilled “translators”: specialized “integrators,” who bridge the gap between business leaders and technical experts. They translate strategic challenges into analytic questions and use evidence-based practice to interpret insights derived from the analytics, engage stakeholders, and ultimately propel business changes. Translators often serve as an entry point to the people analytics team, helping to raise awareness of the team in the organization and build the team’s credibility. Some of the leading people analytics teams have built benches of internal consultants to partner directly with individual businesses on their specific problems.

Operating models

In a fast-developing field, people analytics teams need to deliver impact across the organization and stay ahead of the curve to maintain that impact into the future. The best teams align themselves well against organizational priorities while maintaining space for open experimentation and innovation.

Innovation as the norm. Members of leading teams are explicitly expected to explore and innovate beyond their day-to-day fulfillment of the needs of their clients. Some companies have rules of thumb for the percentage of time that teams spend on exploration as opposed to core foundational work. These expectations allow teams to fully experiment and build out proofs of concept.

This process can take a variety of forms, but the important distinction is that the areas of innovation need not directly support an existing business priority or client need; they might be purely exploratory. For example, some data scientists relish the extra time to play around in a sandbox and learn how analytic tools and services work in the cloud. Others might want to explore creative new ways to visualize data in order to equip business leaders with helpful insights. The goal is to ensure that all team members are constantly forming new ideas and looking for new ways to meet the analytic needs of the organization and thereby help it achieve its objectives.

Clear alignment with clients and organizational use cases. People analytics teams take different approaches to organizing themselves and aligning with different clients. What is consistent, however, is the presence of a mechanism for attaining an in-depth understanding of enterprise-wide priorities as well as the specific needs of individual clients. This mechanism creates feedback loops that enable continuous learning and iterative development, and it ensures that people analytics teams are working on the most pressing and high-impact topics.

A culture of trust, empowerment, and ownership is the critical foundation for ensuring that a people analytics team is aligned with its clients as well as the enterprise. People analytics teams routinely deal with urgent (and often ambiguous) client needs and questions, highly sensitive data, and challenges to extrapolating meaningful and actionable insights that will guide business decisions. The bar to entry for the best teams is high: members must own their work from end to end and be empowered to define the constraints of any analysis, protect privacy as well as fairness and equity, flag issues that arise, and use their own judgment to derive insights. Being reactive and incremental is not enough in human resources, where priorities change and the top ones require immediate attention.

Over time, as organizations become increasingly dependent on the quality of their insights, the best people analytics teams play a stronger role in shaping the HR agenda, influencing how the organization manages its talent at both a policy and a process level.

The pulse survey

The COVID-19 crisis provided a natural experiment for one large, global organization with a strong people analytics team to use the ingredients outlined in the previous section by rapidly creating a homegrown weekly pulse survey to track the opinions and feelings of tens of thousands of employees around the globe. This capability enabled the organization to better understand the best ways to support employees in a challenging time and a fully remote work environment.

Setting up the pulse survey required intensive collaboration between diverse, highly skilled individuals already embedded in the organization’s people analytics team as well as rapid and close collaboration with the leadership of the organization. Translators navigated the need to craft questions that engaged employees, gathered high-quality data to feed the analytic models, and communicated insights back to leaders who had urgent decisions to make about how to best support their workforce in an external environment that was highly unpredictable and changing week by week.

To speed the time to insights, data engineers established an automated and continuous link among weekly survey-response data, core HR data systems, and a broader set of additional data sources, including data sets that data engineers had developed and customized for this purpose. This process cleaned, tested, and prepared the data for analysis. In addition to rapidly providing analysts with weekly data to examine and synthesize, it fed these data to a prototype self-service reporting tool, which gave leaders the ability to directly investigate aggregated pulse data within six hours of the survey’s close.

The customized data sets supported both exploratory and targeted analyses and helped generate actionable insights for the leaders. Analyses were designed to build on the organization’s current understanding of the health of its employees, marrying new and existing information to yield new insights that guided various efforts. For example, specialists in natural language processing used structural topic modeling to identify and quantify topics in the free-text comments that employees submitted as part of the survey each week. Sentiment analysis was used to understand the emotion behind each topic. These results were then married to the demographic information prepared by data analysts, allowing managers, leaders, and other decision makers to understand how the conversations and associated feelings varied by subpopulation, such as parents and less tenured employees. The combination of data sources and analytic approaches ultimately revealed population-specific needs, which allowed the organization to target specific groups and tailor the type of support it offered to maximize impact.

Exhibit 2 is a view of the major topics generated from the free text of the employees who responded to the pulse surveys and how their emphasis on these topics changed over the course of two months of the crisis. At the beginning, employees were thankful for the health of their families and peers and had generic concerns about the developing situation, but as the crisis evolved, their thoughts crystallized into the more particular concerns of isolation, remote work, childcare, and work-life balance.

The ability to rapidly develop this capability, turn around a wide range of sophisticated analytics within 24 hours after the survey closed, and repeat the survey weekly did not come easily to the organization or the people analytics team. The capabilities required to pull it off were tightly rooted in the data, analytics, and operating-model ingredients that we have identified as the hallmarks of great people analytics teams.

Despite the vast differences that exist among organizations’ data quality, integration, and infrastructure, we all certainly have a lot to learn from each other. Answering the following questions will be helpful to leaders who want to identify where their organization’s people analytics is now and where they would like them to be:

• Where is the organization on the people analytics stairway? Where does it aspire to be in the next year, three years, and five years?
• How does the organizational context influence the mandate of the people analytics team?
• What ingredients does the organization possess today, and which does it need to build?
• How should the organization determine its priorities in building people analytics capabilities? For example, should it build to support certain specific internal use cases, or should it build a broad bench of capabilities to support an unpredictable or rapidly changing internal environment?
• If the organization had to get one thing right over the next 12 months, what would it be? What would get in the way of its getting there?

While no single model is the “correct” one for developing the capabilities of a people analytics team, leading teams seem to have a set of ingredients in common. While the past decade has brought about real change, even the best teams—those that iterate at each step of the stairway and learn as they ascend—have barely scratched the surface of what’s possible with people analytics.

Elizabeth Ledet is a partner in McKinsey’s Atlanta office; Keith McNulty is a director, people analytics and measurement, in the London office; Daniel Morales is a director of analytics in the Washington, DC, office; and Marissa Shandell is an alumna of the New York office.

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Human Resource (HR) analytics

Ivan Andreev

Demand Generation & Capture Strategist, Valamis

December 20, 2021 · updated April 2, 2024

13 minute read

What is HR analytics?

Why is hr analytics needed, examples in hr analytics, how does hr analytics work, examples of hr analytics metrics, pros and cons of hr analytics, predictive hr analytics.

HR analytics is the process of collecting and analyzing Human Resource ( HR ) data in order to improve an organization’s workforce performance. The process can also be referred to as talent analytics, people analytics, or even workforce analytics.

This method of data analysis takes data that is routinely collected by HR and correlates it to HR and organizational objectives. Doing so provides measured evidence of how HR initiatives are contributing to the organization’s goals and strategies.

For example, if a software engineering firm has high employee turnover, the company is not operating at a fully productive level.

It takes time and investment to bring employees up to a fully productive level.

HR analytics provides data-backed insight on what is working well and what is not so that organizations can make improvements and plan more effectively for the future.

As in the example above, knowing the cause of the firm’s high turnover can provide valuable insight into how it might be reduced. By reducing the turnover, the company can increase its revenue and productivity.

Read: How to Successfully Implement Learning Analytics in Your company

Most organizations already have data that is routinely collected, so why the need for a specialized form of analytics? Can HR not simply look at the data they already have?

Unfortunately, raw data on its own cannot actually provide any useful insight. It would be like looking at a large spreadsheet full of numbers and words.

Without organization or direction, the data appears meaningless.

Once organized, compared and analyzed, this raw data provides useful insight.

They can help answer questions like:

• What patterns can be revealed in employee turnover?
• How long does it take to hire employees?
• What amount of investment is needed to get employees up to a fully productive speed?
• Which of our employees are most likely to leave within the year?
• Are learning and development initiatives having an impact on employee performance ?

Having data-backed evidence means that organizations can focus on making the necessary improvements and plan for future initiatives.

With the ability to answer important organizational questions without any guesswork, it is not surprising that many businesses using HR analytics are attributing performance improvement to HR initiatives.

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How can HR Analytics be used by organizations?

Let’s take a look at a few examples using common organizational issues:

1. Turnover

When employees quit, there is often no real understanding of why.

There may be collected reports or data on individual situations, but no way of knowing whether there is an overarching reason or trend for the turnover.

With turnover being costly in terms of lost time and profit, organizations need this insight to prevent turnover from becoming an on-going problem.

HR Analytics can:

• Collect and analyze past data on turnover to identify trends and patterns indicating why employees quit.
• Collect data on employee behavior, such as productivity and engagement, to better understand the status of current employees.
• Correlate both types of data to understand the factors that lead to turnover.
• Help create a predictive model to better track and flag employees who may fall into the identified pattern associated with employees that have quit.
• Develop strategies and make decisions that will improve the work environment and engagement levels.
• Identify patterns of employee engagement , employee satisfaction and performance.

Exit interview template

Conduct an exit interview and collect valuable information that can help improve the work culture in your organization.

2. Recruitment

Organizations are seeking candidates that not only have the right skills, but also the right attributes that match with the organization’s work culture and performance needs.

Sifting through hundreds or thousands of resumes and basing a recruitment decision on basic information is limiting, more so when potential candidates can be overlooked. For example, one company may discover that creativity is a better indicator of success than related work experience.

• Enable fast, automated collection of candidate data from multiple sources.
• Gain deep insight into candidates by considering extensive variables, like developmental opportunities and cultural fit.
• Identify candidates with attributes that are comparable to the top-performing employees in the organization.
• Avoid habitual bias and ensure equal opportunity for all candidates; with a data-driven approach to recruiting, the viewpoint and opinion of one person can no longer impact the consideration of applicants.
• Provide metrics on how long it takes to hire for specific roles within the organization, enabling departments to be more prepared and informed when the need to hire arises.
• Provide historical data pertaining to periods of over-hiring and under-hiring, enabling organizations to develop better long-term hiring plans.

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Understanding the process of HR analytics

HR Analytics is made up of several components that feed into each other.

• To gain the problem-solving insights that HR Analytics promises, data must first be collected .
• The data then needs to be monitored and measured against other data, such as historical information, norms or averages.
• This helps identify trends or patterns. It is at this point that the results can be analyzed at the analytical stage.
• The final step is to apply insight to organizational decisions.

Let’s take a closer look at how the process works:

1. Collecting data

Big data refers to the large quantity of information that is collected and aggregated by HR for the purpose of analyzing and evaluating key HR practices, including recruitment, talent management , training, and performance.

Collecting and tracking high-quality data is the first vital component of HR analytics.

The data needs to be easily obtainable and capable of being integrated into a reporting system. The data can come from HR systems already in place, learning & development systems, or from new data-collecting methods like cloud-based systems, mobile devices and even wearable technology.

The system that collects the data also needs to be able to aggregate it, meaning that it should offer the ability to sort and organize the data for future analysis.

What kind of data is collected?

• employee profiles
• performance
• data on high-performers
• data on low-performers
• salary and promotion history
• demographic data
• on-boarding
• absenteeism

2. Measurement

At the measurement stage, the data begins a process of continuous measurement and comparison, also known as HR metrics.

HR analytics compares collected data against historical norms and organizational standards. The process cannot rely on a single snapshot of data, but instead requires a continuous feed of data over time.

The data also needs a comparison baseline. For example, how does an organization know what is an acceptable absentee range if it is not first defined?

In HR analytics, key metrics that are monitored are:

Organizational performance Data is collected and compared to better understand turnover, absenteeism, and recruitment outcomes.

Operations Data is monitored to determine the effectiveness and efficiency of HR day-to-day procedures and initiatives.

Process optimization This area combines data from both organizational performance and operations metrics in order to identify where improvements in process can be made.

Here are some examples of specific metrics that can be measured by HR:

• Time to hire – The number of days that it takes to post jobs and finalize the hiring of candidates. This metric is monitored over time and is compared to the desired organizational rate.
• Recruitment cost to hire – The total cost involved with recruiting and hiring candidates. This metric is monitored over time to track the typical costs involved with recruiting specific types of candidates.
• Turnover – The rate at which employees quit their jobs after a given year of employment within the organization. This metric is monitored over time and is compared to the organization’s acceptable rate or goal.
• Absenteeism – The number of days and frequency that employees are away from their jobs. This metric is monitored over time and is compared to the organization’s acceptable rate or goal.
• Engagement rating – The measurement of employee productivity and employee satisfaction to gauge the level of engagement employees have in their job. This can be measured through surveys, performance assessments or productivity measures.

3. Analysis

The analytical stage reviews the results from metric reporting to identify trends and patterns that may have an organizational impact.

There are different analytical methods used, depending on the outcome desired. These include: descriptive analytics , prescriptive analytics , and predictive analytics .

Descriptive Analytics is focused solely on understanding historical data and what can be improved.

Predictive Analytics uses statistical models to analyze historical data in order to forecast future risks or opportunities.

Prescriptive Analytics takes Predictive Analytics a step further and predicts consequences for forecasted outcomes.

Examples of analytics:

Here are some examples of metrics at the analytics stage:

• Time to hire – The amount of time between a job posting and the actual hire is a metric that enables HR to gain insight into the efficiency of the hiring process; it prompts investigation into what is working and what is not working. Does it take too long to find the right candidate? What factors could be impacting the result?
• Turnover – Turnover metrics that indicate the rate at which employees leave the organization after hire can be analyzed to determine what specific departments within the organization are struggling with retention and the possible factors involved, such as work environment dissatisfaction or lack of training support.
• Absenteeism – The metric indicating how often and how long employees are away from their jobs as compared to the organization’s established norm could be an indicator of employee engagement. As absenteeism can be costly to the productivity of an organization, the metric enables HR to investigate the possible reasons for high absence rates.

4. Application

Once metrics are analyzed, the findings are used as actionable insight for organizational decision-making.

Examples of how to apply HR analytics insights:

Here are some examples of how to apply the analysis gained from HR analytics to decision-making:

• Time to hire – If findings determine that the time to hire is taking too long and the job application itself is discovered to be the barrier, organizations can make an informed decision about how to improve the effectiveness and accessibility of the job application procedure.
• Turnover – Understanding why employees leave the organization means that decisions can be made to prevent or reduce turnover from happening in the first place. If lack of training support was identified as a contributing factor, then initiatives to improve on-going training can be put together.
• Absenteeism – Understanding the reasons for employee long-term absence enables organizations to develop strategies to improve the factors in the work environment impacting employee engagement.

HR analytics is fast becoming a desired addition to HR practices.

Data that is routinely collected across the organization offers no value without aggregation and analysis, making HR analytics a valuable tool for measured insight that previously did not exist.

But while HR analytics offers to move HR practice from the operational level to the strategic level, it is not without its challenges.

Here are the pros and cons of implementing HR analytics:

• More accurate decision-making can be had thanks to a data-driven approach, which reduces the need for organizations to rely on intuition or guess-work in decision-making.
• Strategies to improve retention can be developed thanks to a deeper understanding of the reasons employees leave or stay with an organization.
• Employee engagement can be improved by analyzing data about employee behavior, such as how they work with co-workers and customers, and determining how processes and environment can be fine-tuned.
• Recruitment and hiring can be better tailored to the organization’s actual skillset needs by analyzing and comparing the data of current employees and potential candidates.
• Trends and patterns in HR data can lend itself to forecasting via predictive analytics, enabling organizations to be proactive in maintaining a productive workforce.
• Many HR departments lack the statistical and analytical skillset to work with large datasets.
• Different management and reporting systems within the organization can make it difficult to aggregate and compare data.
• Access to quality data can be an issue for some organizations who do not have up-to-date systems.
• Organizations need access to good quality analytical and reporting software that can utilize the data collected.
• Monitoring and collecting a greater amount of data with new technologies (eg. cloud-based systems, wearable devices), as well as basing predictions on data, can create ethical issues.

Predictive Analytics analyzes historical data in order to forecast the future. The differentiator is the way data is used.

In standard HR analytics, data is collected and analyzed to report on what is working and what needs improvement. In predictive analytics, data is also collected but is used to make future predictions about employees or HR initiatives.

This can include anything from predicting which candidates would be more successful in the organization, to who is at risk of quitting within a year.

How does it work?

Advanced statistical techniques are used to create algorithmic models capable of identifying trends and future behaviors. These future trends can describe possible risks or opportunities that organizations can leverage in long-term decision-making.

Predictive HR examples

Let’s take a look at how predictive analytics can be used:

Turnover With predictive analytics, an algorithm can be devised to predict the likelihood of employees quitting within a given timeframe. Being able to flag which employees are at risk enables organizations to step in with preventative measures and avoid the cost of losing productivity and the cost of re-hiring.

Organizational Performance Historical data can pinpoint reasons for poor performance, but predictive analytics can make predictions about what initiatives are most likely to improve performance. If engagement levels are identified as being correlated with performance, then organizations can implement specific initiatives that boost employee engagement.

The benefits and challenges of predictive HR analytics

Benefits: Predictive HR analytics enables organizations to become proactive in their use of data.

Instead of fixing past problems, organizations can create a future that prevents problems and solves future challenges before they even happen. This can save on future costs, both in revenue, goals, and productivity.

Challenges: Predictive HR analytics requires a level of skill, technology and investment that many organizations do not yet have.

Many factors also need to be taken into consideration in order to make predictions about employees or potential candidates.

Human beings can be unpredictable and have different personalities, backgrounds and experiences. Slotting people into a black and white algorithm in order to make predictions about their job performance or future poses not just a risk, but an ethical question.

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Rippling Announces Series F Fundraising and Tender Offer

Apr 22, 2024

Parker Conrad

We’re pleased to announce Rippling has raised $200M in new financing, and signed agreements with investors to repurchase up to $590M of equity from current employees, former employees, and early investors. The financing was led by Coatue with participation from Founders Fund, Greenoaks, and other existing investors. Dragoneer is joining the round as a new investor.

The financing values the company at $13.5 billion. 

We’re grateful for these investors’ conviction in Rippling, for the employees that have gotten us to this point, and for our clients, without whom none of this would be possible.

Rippling’s core thesis is that employee data is critical to a surprisingly large number of business systems, including the ones well outside of HR.

Maintaining the fidelity of the same employee data across all these disconnected systems—effectively, across multiple separate databases—is the reason it’s a lot of work for companies to have many different business systems in the first place. Rippling solves this problem by giving companies and employees a single place to make changes, which then propagate everywhere automatically.

The system that does this isn’t just a time-saver—we believe it will be a critical primitive for business software going forward.

Products that are built on top of a rich graph of data about the organization, employees, their devices and apps aren’t just easier to manage. They are better as software products—with more intelligent workflows and approvals, better role-based policies and permissions, and more powerful analytics.

This system we’ve built helps companies run more efficiently and achieve their business goals faster than their competitors. We will continue to expand in new markets and invest deeply in R&D to enhance our current offering and build new products to support our clients. 

To better understand Rippling’s approach and where we’re headed, you can read a lightly redacted version of the memo I wrote for investors ahead of this financing. 

We are, of course, hiring across all roles: rippling.com/careers .

Rippling CEO

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