Before you leap into talent analytics, understand where innovations are occurring and how they can help your organization.
Talent analytics solutions are rapidly gaining traction as learning technology capabilities mature. Now is the time to explore how talent analytics can enhance the value that the learning function brings to the organization.
The analytics challenge
Talent analytics covers a vast landscape. Many best-in-class, talent-driven companies have designated analytics teams to evaluate hundreds, if not thousands, of data sets across their organizations. Many of these companies are appointing senior leaders to gather and assess talent data and provide human capital insights.
An overwhelming majority of talent analytics relied on to make critical decisions come from internal data generated by performance management systems, learning management systems, and so forth. Yet strategic talent management—and the decisions critical to driving organizational performance—is rooted firmly in the organization's forward-looking strategic agenda and the external environment in which companies must compete.
To compound the challenge, talent management benchmarking to date has focused on efficiency measures such as cost per hire and time to hire. Although this can be helpful for defending current activities and identifying existing inefficiencies, such data are a function of the environment. Do these data actually help to make smarter, predictive talent decisions? More important, do they answer the right questions, or do they instead distract by redirecting attention to the mechanics of the process and to past performance?
Lessons from talent analytics innovators
While it's challenging to get right, several organizations are taking the leap to incorporate analytics and external benchmarks into their talent decision making processes. The following lessons are from innovators at the forefront of talent analytics—companies that are rethinking how they use and incorporate analytics data within their human capital mindset.
Ask the right questions. This is, without hesitation, the most difficult step in the talent analytics journey. It requires a fresh perspective and often challenges entrenched methods, possibly even questioning current strategies and programs.
Time Warner Cable (TWC) made a major investment in recruitment and training programs for key roles, including its frontline managers. These managers play a critical role in the company, affecting the business at multiple levels and functions. With an already heavy investment in selecting, training, and developing these roles with solid internal metrics supporting the company's work, TWC raised the challenge: We think our managers are good, but are they good enough? The company understood that the manager role was so critical to the organization that it decided to question its own success and look for analytics that could—or could not—support the direction and investments the company had made.
The learning function may be focused on important and relevant questions—What do we need to do to reduce turnover? How can we develop our midlevel managers to prepare them for leadership roles?—but are these inquiries focused on treating symptoms rather than diagnosing the cause? Asking the right questions is a first step in understanding the analytics needed to answer them.
Examine internal data. Too often, internal talent data are unquestioned yet used as the evidence to support decisions. There is a contingent of thought leaders who are beginning to understand that data are a function of context—if the system or organization generating that data is flawed, then using that data for predicting next steps can have serious consequences.
Take, for example, a software company that is expanding rapidly into Europe and the Nordic countries. This organization supported the development of its senior sales reps based on internal data that showed they needed to be highly analytical and technical experts to successfully engage with major clients. The data went unquestioned until the Nordic sales directors began to claim that their reps wouldn't succeed against top competitors.
What went wrong? Although the company's data were solid and the execution well-delivered, the outcome was far from ideal. Only after using external best-in-class benchmarking data did the company discover that its internal data didn't account for key market differences. In the Nordics, top sellers are far more likely to be trusted advisers who lead clients toward their solutions rather than technical experts recommending options. Internal data must be vetted carefully before they are used in predictive models.
Go external. Both above examples highlight a factor of critical importance in developing a talent analytics strategy: Go external. The advent of "Big Data" has made it possible for analytics providers to consolidate data pools across many organizations to create true best-in-class data sets. In fact, many of the most critical questions that companies are beginning to ask about their talent strategies can't be fully answered without an external perspective.
For example, TWC had solid statistics supporting the effectiveness of its frontline manager talent program; however, the company felt compelled to ask: What if we're wrong? TWC decided to use external analytics benchmarks to compare the caliber of its frontline managers and confirmed that, indeed, its efforts were paying off—TWC managers rated at the top of the pack when compared with best-in-class peer organizations.
Similar analyses also showed that other teams were underperforming, providing both confirmation and redirection for TWC's talent strategy. Were the company's internal analytics accurate? Yes. Did its data support talent decisions? Yes. Did the data provide the whole picture? No.
Time and again, innovators like TWC are learning that it is essential to benchmark against external data to confidently use talent analytics for predictive purposes.
Avoid using data in isolation. Some data analytics are useful on their own. Retention rates, for example, are a talent manager's dashboard standard. However, many learning leaders are questioning the way organizations tend to isolate metrics in functional or specific areas. For example, United Heath Group (UHG) has long been a leader in understanding and using human capital data to drive decision making and talent programs. Company executives are proud—as they should be—of the metrics that show success in recruiting, training, and development across their organization.
Yet Big Data opens up the next big move for UHG—aggregating isolated talent data to develop more predictive and multipurpose analytics. Currently the company is exploring an analytics model that will incorporate multiple facets of data on key talent—such as performance data, assessment data, recruiter ratings, manager ratings, and external best-in-class benchmarks—into a predictive insights model. Its goal is to develop analytics from historically disparate data (typically measured and tracked in isolation) that offer new insights into improving training, development, and recruitment effectiveness.
Big Data is here to stay
These analytics innovators are paving the way. Their use of human capital data is shaping the next generation of data-driven capabilities for managing people intelligence. Most important, these companies understand that external talent benchmarking lends context to internal talent data and provides insight into how talent strategies must evolve to support the organization's long-term strategic agenda.
Lessons from these innovators already are helping other organizations to answer historically challenging and elusive questions. And once the right data are used to answer the right questions, learning leaders can more effectively unlock the talent potential within their teams.