Building an HR Analytics capability: from data collection to informed decision making

It is no longer a mystery by now: in an increasingly complex organizational context, characterized by volatility, uncertainty and accelerated transformation, the HR function is called upon to play a profoundly renewed role: no longer just an operational manager of processes, but a strategic partner capable of contributing concretely to business objectives.

As we showed in a recent case study, within such a scenario HR Analytics represent a key lever to enable this transition.

The increasing availability of data, coupled with the evolution of analytics and visualization tools, now makes it possible to transform the information gathered in the HR perimeter into high-value insights. From talent management to workforce planning, from diversity to employee engagement, decisions can be supported by objective evidence, shortening timelines and thus increasing the effectiveness and credibility of the HR function in front of the business.

The current state of HR Analytics in organizational settings

The usefulness and potential of people analytics are now evident throughout the entire employee life cycle. According to extensive research on the topic conducted by SHRM in 2023, which surveyed more than 3,000 HR professionals and executives, organizations globally are using people analytics to address some of the function’s most strategically important issues, such as Talent Retention and Employee Engagement (see Figure 1).

Figure 1Uses of People Analytics within HR processes (Source: Society for Human Resource Management (SHRM). 2023. The Use of People Analytics in Human Resources: Current State and Best Practices Moving Forward. Alexandria, VA: SHRM)

However, another key finding emerges from SHRM’s research, which must necessarily prompt us to reflect: although 71 percent of HR executives consider people analytics essential to their organization’s HR strategy, only 57 percent report how it has generated profits or savings for the company.

Why does the use of people analytics succeed in producing value for some organizations, while others still struggle to fully exploit its potential?

Building an HR Analytics capability: the main obstacles

The answer to this question lies in 2 different types of obstacles, which organizations encounter on the road to building an effective HR Analytics capability:

1. Difficulty in building a fully data-driven culture

Thanks to advances in technology and increased computing power, organizations of all sizes can leverage increasing volumes of data within their organizational functions. It is now possible to use data, rather than relying on hunches, to assess the efficiency of HR processes; productivity, diversity or employee engagement rates; along with other key business indicators.

However, this transformation requires a radical paradigm shift: moving from a culture of insight to a culture of data . In this sense, resorting once again to research conducted by SHRM, it seems relevant to mention the opinion gap between HR executives and function professionals regarding the development of a data-driven culture in their respective organizations.

Indeed, while 76 percent of HR Executives surveyed assure how their organization is strongly committed to building a data-driven culture, HR Professionals are far less likely (58 percent) to detect signs of such a culture within their organizations.

This gap suggests that, in many realities, the transition to a data-driven culture is still in progress, when not exclusively at the design stage.

2. Lack of expertise and dedicated organizational units

A second reason that explains the difficulty of several organizations in exploiting the full potential of people analytics concerns the difficulty of finding (internally or in the market) the appropriate skills to use them profitably. In particular, consulting the results of SHRM’s research on this point, what stands out most is the lack of organizational units dedicated to this purpose.

In fact, only 30 percent of organizations that say they leverage people analytics in HR have an employee, department or division dedicated explicitly to this function. Specifically, 62 percent of large organizations (more than 5,000 employees) have a dedicated function, a percentage that drops to 29 percent and 24 percent when considering medium-large (500-5000 employees) and medium-small (0-500 employees) companies, respectively.

Building an HR Analytics capability: levels of organizational maturity

However, the analysis of the obstacles that stand in the way of building an HR Analytics capability must necessarily take into account the presence of different levels of use and application of people analytics at the organizational level, which represent progressive stages of maturity on the road to a fully integrated and strategic use of data analytics tools.

In particular, the maturity model popularized by U.S. consultant Josh Bersin is certainly an excellent starting point for assessing one’s organization’s level of progress with respect to a strategic use of People Analytics. That model includes 4 progressive levels of maturity:

• Reporting: this is the basic level, focusing on the collection and presentation of historical data and key performance indicators (KPIs), such as turnover rate, average time to hire or absenteeism. In other words, it involves using already available data to analyze what has happened in the past.
• Advanced Reporting (descriptive analysis): enables understanding of past phenomena by identifying patterns and correlations that are useful in identifying the causes of observed trends. That is, deliberately collecting certain data on a regular basis to analyze relationships between variables or to understand the occurrence of certain patterns (e.g., the effect of proposed new benefits on the turnover rate, or the link between a new application management system and diversity in hires made).
• Analytics (predictive analytics): exploits statistical models and algorithms to anticipate future scenarios. For example, estimate the probability of resignation in the coming months based on observable behaviors.
• Advanced Analytics (prescriptive analysis): represents the most advanced level, geared toward providing operational guidance on “what to do” to achieve certain results, suggesting concrete actions based on simulations and scenarios.

Of course, evolution along this scale requires strategic vision, targeted investment, and a long-term path of organizational transformation and change management.

However, while certainly the first two levels described by Bersin’s framework can help answer certain organizational questions and issues, as analytical techniques become more complex and the level of integration into the processes of analytics tools increases, the quantity and quality of insights that can be obtained and the impact on business that they can offer also grows significantly.

For example, effectively monitoring basic metrics such as turnover rate can help describe the contours of the phenomenon, but in terms of decision support it certainly does not guarantee the same results as analytical models capable of predicting trends in workforce behavior.

Significantly, the application of Bersin’s framework to the aforementioned research conducted by SHRM confirms that most of the organizations surveyed are just beginning their journey in integrating people analytics: in fact, the results revealed that 77 percent of the organizations are still at level 1 or 2 of the model just described, while only 4 percent are at the most advanced stage.

So what are the key steps to follow to progress up the ladder of maturity levels, and build a solid HR Analytics capability?

An integrated approach: building an HR Analytics capability

Building an HR Analytics capability requires a holistic approach that includes several key dimensions. It is not enough to focus only on the technology aspect or the development of appropriate skills; a systemic approach and a coordinated effort involving skills, tools, processes and an alignment with business needs is required. This approach is therefore based on four key dimensions:

1. Skills

The first enabling condition is the development of analytics skills. Since these skills are not natively part of the HR “toolbox,” the willingness to experiment assumes crucial importance for learning and improving this type of skill. This attitude not only affects the eventual figures to be identified for the creation of an HR analytics function, but invests the entire HR function, which must become familiar with basic statistical concepts, critical data reading skills and understanding of visualization tools. Training, ongoing and contextualized, is an essential investment to enable a fully data-driven culture

2. Tools and technological infrastructure

Technology infrastructure naturally plays a crucial role. The adoption of business intelligence solutions, interactive dashboards, data integration systems (such as data warehouses) and automation tools enables the efficient collection, processing and distribution of information, while also significantly improving the final use of the data by decision-makers, the ultimate recipients of the analyses.

Despite this, on this issue within organizations there is still a large gap between stated intentions and factual reality. The latest research on this topic conducted by MIT Technology Review Insights in 2024 confirms that 78 percent of the companies surveyed are in fact failing to derive value from their artificial intelligence and data analytics applications precisely because of insufficient investment in building data infrastructures and data platforms.

3. Data governance

Building a capability also means defining structured processes and mechanisms for data governance. Data governance-including roles, standards, privacy and security-is the foundation of reliable and consistent data management. While this is an extremely broad topic, impossible to exhaust in a few lines, it is possible to identify a few expedientsgeneral :

• We need to start with a clear definition of who owns and manages HR data. This means assigning specific tasks related to data quality to dedicated individuals or teams, or providing for their creation when they are not yet on the organizational chart.
• Next, a key step concerns the application of quality standards for all HR data. With this in mind, it is important to define what is acceptable in terms of accuracy, completeness, and timeliness for different types of data and analysis.
• Next, rules must be developed on how HR data are accessed, used and protected. This includes implementing access based on organizational roles and compliance with privacy regulations. The final output of the process may be a document that lays out the goals of HR data governance, how success is measured, and the different roles and responsibilities within the data governance team.

Finally, a final useful expedient concerns the implementation of processes and tools to manage HR data metadata. This includes creating data dictionaries and keeping track of the data sources used.

Of course, the establishment of data governance mechanisms cannot be separated from the creation of an organizational culture that values it and understands its centrality to the operation of the entire analytics pipeline. From this perspective, there are at least two best practices to consider:

• The creation a group of data stewards (data stewards) throughout the organization to promote data governance practices and support other departments and functions in this regard.
• Implementing recognition programs for employees who demonstrate good data governance practices or contribute to improving data quality. Incentives can motivate staff to prioritize data governance in their daily work.

4. Dialogue with business (with a view to co-design)

Finally, an effective HR Analytics capability requires an ongoing and structured dialogue with the business . Co-designing analyses, actively listening to needs, and translating evidence into shared actions strengthen the link between the HR function and strategic direction.

It is therefore necessary to first understand who the main target audiences of HR analytics will be and to understand the needs of the internal customer in terms of the form, depth and frequency of the analytics.

With this in mind, it is a must to consult with business leaders to define the starting questions for the analysis and the construction of mock-ups to be jointly validated.

Of course, framing this dialogue within a structured process, with clear and defined actors and responsibilities, is essential here as well. Ultimately, close collaboration and communication with the rest of the business are crucial to escape the danger of self-referentiality of analytics, while ensuring that HR Analytics efforts are focused on solving strategic business challenges and delivering tangible value.

HR Analytics capability: what strategic value for the function?

As we mentioned at the outset, building a robust HR Analytics capability, in the ways just described, enables significant impact at the organizational level, both operationally (increased process efficiency and cost optimization) and strategically (increased speed and accuracy in top management decisions).

However, beyond these types of organizational impacts, what we have observed in a recent case study is the possibility that adopting a fully data-driven approach in HR succeeds in generating strategic value for the function, greatly enhancing its credibility in the eyes of the business and enabling it to evolve from an internal service provider to a role as an advisor and strategic partner, capable of dialoguing on an equal footing with other business functions, offering tangible solutions and useful insights to address the most relevant organizational challenges.

Conclusion: the next frontier of people analytics

In conclusion, the path to a truly strategic HR function today passes inescapably through the development of a robust HR Analytics capability.

It is not just a matter of adopting the necessary technological infrastructure or creating data pipelines for collecting large volumes of data, but rather of building an organizational culture capable of valuing data (and its analysis) as the main reference for decision-making, integrating skills, processes and a shared vision with the business.

Organizations that can make this leap will be able not only to improve operational efficiency, but also to more consciously guide business decisions, contributing to the creation of sustainable value.

However, we are witnessing a new evolutionary phase, where artificial intelligence enters HR Analytics processes in an overbearing way. AI-driven HR Analytics promises to automate, amplify and personalize analytics, opening up new predictive and prescriptive possibilities. This will be the topic of the next article, in which we will explore how artificial intelligence is redefining the frontiers of people analytics and what ethical, technological, and cultural challenges it poses to organizations.