Talent Analytics Isn’t Enough
I think it’s safe to say that “THIS was the year of talent analytics”. Global discussions about talent analytics, as a concept, have certainly been great for our brand, and have happily led to new customers and connections.
However, when we review our client work, it’s clear that the industry is just at the very beginning of something much bigger. Many firms are now collecting better talent and workforce information. Others are moving upwards in analytics maturity, as measured by firms such as Bersin by Deloitte. But most have yet to transcend what Dr. Eric Siegel calls “descriptive” analytics.
Reporting last year’s employment attrition rates isn’t enough. Those employees have left your company. Such a report of past activity doesn’t connect with business objectives, which always exist in the future. Basic talent reporting elements, which sometimes pass for analytics, are just a stepping-stone to greater things.
The goal in any analytics initiative is to deliver intelligence that leads to tangible business results. Not HR results, business results. For example, you might predict next year’s attrition rates and create a table of who is likely to leave, so the firm can train, intervene, and retain. Better still, the goal is to model the “top performers”, and predictively hire job candidates who match the model.
HR systems deliver employee data like hire-date, salary, performance review ratings, attrition codes, competency ratings, training, and the like. But that’s only a start. None of these variables (no matter how fancy your analytics models) indicate the dimensions of value that the employee brings to the company.
Only the business unit can deliver performance data; real business performance metrics like sales quota achieved, number of calls handled in a call center, customer service scores, cash drawer shortages for tellers, how many lines of code a software developer writes, number of orders processed, and other KPIs (key performance indicators) directly measured and valued by the business.
To predict outcomes the business cares about, analytics models need input from both sides of the equation. Many in HR are focused on solving attrition challenges, without including data about performance of the people leaving. Don’t try to solve attrition challenges without including business performance information.
