Predictive People Analytics: Relax and Take the Long View

Feb 14, 2017

Predictive People Analytics Relax and Take the Long View.jpg

Contrary to the yammering in the industry and the blogosphere, lack of predictive people analytics will not swallow you up like a sinkhole. If you have been paying even a little attention, you know analytics can use information about people to make better decisions. But the analytics movement, if we can call it that, is still in its infancy.

It is time to get started, but it will be a long time before you become a world-class power in predicting the impact of human behavior.

The practice of predictive analytics has been around for a long time, beginning with Operations Research in U.S. and U.K. military operations in the 1930s. It has, from the beginning,  included the analysis and prediction of human behavior. Many disciplines have grown up over the past few decades, including behavioral operations management, industrial-organizational psychology, organizational behavior, and now people analytics.

The Modern Analytics Movement in HR

But if analytics about people and work have been around for so long, why is it a modern phenomenon? Cost, availability, and trust have been the limiting factors, and technology has overcome many of the obstacles.

  • Until recently, the ability to capture, store, and analyze vast amounts of data has been available only to large enterprises with the resources to fund it. The practices started in military and government organizations because they had the resources and political will to do it.
  • The talent needed to perform the analysis has been scarce. Until the democratization of data we have seen in the past few years, there were few jobs for data scientists, I/O psychologists, and their academic and scientific colleagues, and the supply has yet to catch up with the demand.
  • Most business leaders spent their entire working lives making judgments and decisions about people based on instinct and experience. They went their whole careers making decisions based on biases learned from isolated incidents. Only recently has the mistrust shifted.

While costs have become manageable for almost any size business, growing a skilled team and changing your culture will take time.

G9_Predictive_Analytics_ebook-1Build Analytics on a Solid Foundation

We advise a measured approach. The journey to analytics maturity can ten 10 years or more. Lay the foundation before you build your analytics house.

Don’t fall prey to what Scott Mondore of SMD calls the shiny object syndrome. Dazzling visualizations offered by some vendors and embedded analytics may not give you the answers you need or even lead to the right questions. The right visuals can be helpful, but the right path is to focus on outcomes.

How you approach your analytics development depends on the current culture and maturity of your enterprise. If you have an enterprise-wide analytics effort and supportive, engaged leadership, your approach will differ greatly from what you need to do to overcome cultural headwinds.

  • If you have a robust analytics team but are not yet leveraging the power of people analytics, you may find it sufficient to partner with the team and line of business leaders to address a business problem. The resources you have may be enough if you only add expertise in human capital management, organizational behavior, and I/O psychology.
  • If you don’t have an analytics team, work with Marketing and Finance to leverage their experience and expertise. Both disciplines have been using analytics for decades. Rather than trying to hire scarce talent, consider working with a consulting firm to limit the cost.
  • Use the data you have. If you have had automated human capital management for even two years, you have a wealth of information about people and performance. You have information on the qualifications and attributes of all the people you have hired, and you know how they have performed. Use what you have before you spend resources on gathering more.
  • If your analytics team and business leadership don’t yet trust HR data, work with your master data management team to get your data under control. If you don’t have an MDM function, get with your CIO to build that effort. You will find that cleaning up your data, instituting data governance controls, and stopping errors at the source will build trust. Starting the conversation will uncover allies throughout the business.
  • Build trust by starting with a small project. We don’t necessarily mean “low-hanging fruit,” but a small, controlled-risk project can happen with little support. Work with managers responsible for KPIs or other performance indicators to explore how changes in behavior or knowledge about people will improve results.
  • Try to delay significant investments in analytics technology under you have a firm understanding of your needs and know what those solutions will do for you. There are many options available, and none of them does everything perfectly. The 2016 Gartner report on business intelligence and analytics platforms will be helpful after you have rigorously assessed your needs.
  • Mind the risks. Algorithms and machine learning may help improve the quality of hire, but are they creating adverse impact? Are word patterns in résumés defensible? Does using your top performers as the model for selection and promotion reinforce bias? If you expose information about individuals to managers, will they overreact and bring on a lawsuit?

Digitization, including people analytics, is the future of HR, but a measured approach will minimize the risks and multiply the impact. Take the journey, but do it one step at a time.

Pixentia is a full-service technology company dedicated to helping clients solve business problems, improve the capability of their people, and achieve better results.

G14_The Datafication of HR

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