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11 Watchwords on the State and Future of Talent Management Analytics

By Evan Sinar, Ph.D.

Evan Sinar, Ph.D. I recently had the opportunity to join the Talent Management Alliance’s Human Capital Analytics Summit. Though this was only the second year for this event, it has quickly distinguished itself as an extremely well-architected forum for high-caliber presentations and collaborative networking amongst an engaged audience of HR analytics practitioners and researchers.

To synthesize and visualize key themes from a very information-rich two days, I created a word cloud with the most common terms used in the many discussions and presentations, sized based on how often they were used during the two days (note, words such as “data,” “analytics,” “talent,” and “HR” were very common too, but in an attempt to dig a bit deeper I excluded them from this analysis):

11 Watchwords on the State and Future of Talent Management Analytics

All of these terms were used at least a dozen times across the event, so all are important in the context of advanced approaches to talent-focused analytics. However, to unpack this word cloud a bit, 11 of what I considered the most critical and future-focused terms and associated observations—grouped into six related clusters—are highlighted above and described below:

  1. “Context” and “Problem” – To avoid irrelevance or even worse, apathy, analytics must take into account the business context and address a question that either is already being asked by senior leaders, or is overdue to be asked. Analytics outputs underpinned by business problem statements that are defined more clearly and through the input of a larger number of stakeholders will be much more likely to hit their target. Every analysis needs to start with a problem statement having meaning and relevance to the business, not just the analysis team.
  2. “Story” and “Visualization” – Storytelling techniques are the essential antidote to data overload, an inability to connect with the audience, and a tendency for stakeholders to expect an input metric at Point A to predict an output metric at Point Q without recognition of the intervening variables which also need to be captured to understand not just “what” is occurring, but “why” and most importantly, “now what.” Closely related, data visualization—representing analytics outputs graphically and interactively—is an impactful technique surging in popularity. Data visualizations capture audience attention while telling a story and democratic data across the wider-than-ever range of individuals charged with data-driven decision making. Many of these decision makers prefer graphical to numerical formats for processing large amounts of complex information—visualization meets this need. Visualization also guides stakeholders to draw their own conclusions from and generate their own hypotheses about the information available—increasing their commitment to the analysis and engagement with the results, while also capturing their informed observations about patterns and trends that can only be easily diagnosed using visual forms of analytics.
  3. “Optimization,” “Predictive,” and “Model”- Future-focused analytics always trump looking-backward views of the data in the value they provide to analytics stakeholders, yet HR has historically lagged other functions, particularly operations and finance, in adoption and mastery of these methods. Advanced approaches such as scenario planning and “What If” investigations, while never definitively predicting the future of a complex resource such as talent, do provide a projection that can be matched up with and compared against other trendlines being plotted within the business—for example, revenue growth or global expansion rates. Optimization, then, involves closing the gap between these two trendlines by modifying talent programs and/or how they are implemented (e.g., in terms of managerial support, communication, and alignment with other HR programs). Models that allow interactivity can also be invaluable in gaining buy-in from stakeholders who don’t agree with a particular parameter, allowing them to simply change it themselves, and to see the resulting impact directly.
  4. “Financial” – It is an obvious but worth-repeating statement that linking HR practices to financial impact brings a distinct level of attention and awareness to the results of talent-focused analytics. Financial metrics provide common ground across all forms of analytics within an organization, from operations to sales to HR—essential when talent management initiatives are competing for budgetary allocations with those from other functions. In many organizations, the analytics team extends beyond HR to include financial professionals who can provide advice and techniques for making sure that financial outcomes are not only included wherever possible, but that these results are credible and take into account relevant adjustment factors and controls (for example, seasonal trends within a retail organization).
  5. “Insights” and “Action” – Insight is a surprisingly loaded term, and one for which the bar raises daily alongside the ever-increasing flow of information to stakeholders. For analytics to truly be insightful, it must tell the audience something they hadn’t heard before or that convincingly refutes their view of reality—the more they know (or think they know), the more challenging it is to tell them something new. Insights are necessary to drive action—what choices need to be made differently based on the outputs of analytics—and to avoid (in the near-term) squandering the data painstakingly gathered and (in the long-term) poisoning the well for the next application of analytics, if the outcomes are seen as nothing new and lacking clear implications. Another essential but less-often-discussed aspect of action is coaching and guiding business partners to take next steps with the data as they in turn bring it back to their business units. While the analysis-business partner is certainly important, the next conversation that occurs between the business partner and his or her own stakeholders may in fact be even more critical—the more the HR analytics team sets their partners up for success having these conversations, the more influential the analytics program will be.
  6. “Strategy” – Encompassing many of the other terms above, the ultimate goal of analytics is, of course, to influence strategy. Our research has shown that HR professionals who adopt and successfully execute on analytics, particularly of the future-facing variety, are much more likely to be seen by business partners as providing data which is valuable, and to have forged a closer and earlier connection between strategic planning and talent planning. Many organizations that succeed in translating analytics into strategy create and communicate using what Sunny Patel from Cardinal Health, one of the speakers at the HC Analytics Summit, terms a “Strategic Questions Map,” which gathers classifying questions from business partners about “What,” “Why,” and “What If,” and aligns these with the analytical methods used to answer them. This ensures that from the beginning, each question matches a clear business need, hugely increasing the likelihood of strategic influence for the research.

Overall, I came away from the Human Capital Analytics Summit very optimistic about the state of data science as applied to talent management. HR has reached a tipping point: Applying advanced analytics to talent data is now more often the rule than the exception, taking shape and taking hold in ways that are achieving true financial—and strategic—impact. The participants at this conference are among those modeling the future of analytics for HR, and key themes such as those above have immediate implications for those who are either starting a new or sustaining an existing program of talent management analytics.

Evan Sinar, Ph.D., is the chief scientist and director of DDI’s Center for Analytics and Behavioral Research (CABER).

For more information about DDI’s point of view on talent management analytics, see our White Papers “GPS for Talent Management Analytics: The Right Map Really Matters” and “Six (Avoidable) Blind Spots That Cripple Talent Management Analytics Initiatives.” For our recent research into the successes and struggles of over 1,500 global organizations with various forms of talent management analytics, see “Leadership Analytics - How HR Can Use Big Data to Provide Big Value” in our Global Leadership Forecast 2014|2015.

Posted: 20 Mar, 2015,
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