People analytics is a specific focus in Human Capital Management (HCM) that enables organizations to have data-driven insights that optimize the impact and value of the workforce. These analytics are essential for addressing a broad scope of HCM objectives, but while reducing compliance risks and highlighting demographic trends have dominated the people analytics landscape for decades, various technology-related advances have paved the way for these data insights to become more actionable, capable of addressing strategic issues such as improving organizational agility and employee productivity.
The proliferation of richer, more engaging HCM visualizations allow greater collaboration around problem-solving, but people analytics really crossed the chasm when guided actions could be taken ahead of potential risks and opportunities. For those actions to lead to strategic outcomes, there needed to be a confluence of technological advances. These include lightweight web capabilities for aggregating heterogeneous data from multiple sources as well as the merging of structured and non-structured data, aka “mashups”; the system intelligence needed to provide data in a much broader context, ushering in today’s examples of storytelling; and what I refer to as the three P’s of AI and machine learning in HCM: Personalize (deliver the most relevant insights), Predict (issues such as flight risks, engagement and productivity downturns, compliance issues, etc.) and Prescribe (best actions).
These modern people analytics capabilities, coupled with allowing non-data scientists and statisticians to readily leverage methods such as root cause analyses, are enabling organizations to ascend the ranks within their industry sector—typically not achievable by simply mitigating back-office risks. Therefore, one of my assertions is that by the end of 2023, one-half of organizations will have realized they lack effective workforce analytics and will invest in a new technological approach that better predicts and guides actions and improves outcomes.
Based on our extensive HCM industry research, we see more organizations planning to deploy—or are already deploying—people analytics solutions that can routinely predict and guide in managing pressing workforce issues. These organizations are clearly benefiting from the combination of much broader access to high-impact data and a more intuitive UX for data management and presentation, sometimes referred to as the democratization of data. Then came the technological advances to improve actionability and the productization of capabilities that heretofore were considered special customizations or add-ons. Cross-domain and in-context visualizations, and push notifications/alerts in the flow of work (and through preferred channels and devices) all contributed to materially elevating the people analytics function. The ability of people analytics to deliver a compelling range of strategic business benefits and competitive advantages did not exist with earlier iterations in this arena. It exists now.
When operating as an HR practitioner, my team found great value in publishing guidelines as to “how to count heads,” a foundational step to enrich the wide array of downstream metrics involving numbers of employees. By tracking and detailing the turnover of various forms of workers—including contractors, part-timers, interns, employees on salary continuance and more—we fostered a much more collaborative analytics environment between HR, the Office of Finance and department heads. We also had the epiphany that before the organization reacted aggressively to something like a rise in a particular division’s turnover, investigating the surrounding contextual data and metrics was critical. For example, we determined in one instance that the higher turnover numbers were caused by a restructuring in that business area coupled with the increased availability of top-quality professionals in the market, which allowed an upskilling of the existing workforce. Executive intervention and aggressive actions were ultimately not needed as the context proved nothing major to be concerned about. It is essential to have guideposts that offer context—and ideally, suggestions—of what to investigate for possible causal factors, thereby allowing a more informed judgment about potential actions needed.
More organizations are now enjoying the wide range of business benefits afforded by deploying modern people analytics, from directly supporting key HCM objectives to leveraging actionable insights and guidance to collaboratively solve vexing business problems related to the workforce. The advantages of partnering with a technology vendor that can seamlessly bring data science competencies to the HCM domain where it often doesn’t exist should not be overlooked, including the advantage of creating more of a data science mindset and culture in HR.
When formulating your organization’s people analytics strategy, however, there are cautions and operational dependencies that must be considered. For instance, an organization’s systems and data repositories should successfully interoperate across different cloud computing and even on-premises environments. Data quality and reliability imperatives should start with effective and properly syndicated data format standards and definitions. It is also vital to present data in the correct context of other data as single metrics in isolation can just as likely lead to wrong decisions or actions as the right ones.
And as a CHRO former boss once told me: “Let’s try to have follow-on questions for many of the answers.”