At the Harvard Business Review, MIT research scientist Kalyan Veeramachaneni details some research into the question, “What would it take for businesses to realize the full potential of their data repositories with machine learning?” One key challenge he identifies is that “machine learning experts often didn’t build their work around the final objective—deriving business value”:
In most cases, predictive models are meant to improve efficiency, increase revenue, or reduce costs. But the folks actually working on the models rarely ask “what value does this predictive model provide, and how can we measure it?” Asking this question about value proposition often leads to a change in the original problem formulation, and asking such questions is often more useful than tweaking later stages of the process. At a recent panel filled with machine learning enthusiasts, I polled the audience of about 150 people, asking “How many of you have built a machine learning model?” Roughly one-third raised their hands. Next, I asked, “How many of you have deployed and/or used this model to generate value, and evaluated it?” No one had their hand up.
In other words, the machine learning experts wanted to spend their time building models, not processing massive datasets or translating business problems into prediction problems.
To get more value from their data, Veeramachaneni concludes, companies “need to focus on accelerating human understanding of data, scaling the number of modeling questions they can ask of that data in a short amount of time, and assessing their implications.” These are valuable principles for talent analytics leaders to keep in mind.
“Business impact” is not easy to achieve. In a peer benchmarking conversation at CEB’s ReimagineHR event in Miami in September, talent analytics professionals identified their main challenges as deriving better insights from the data they were collecting and communicating those insights to their internal clients, and many said their organization was still ineffective at using talent analytics to drive business decisions.
In HR, analytics teams are generally not in the position of having too much data or models that are too complex. We hear quite often that they know better than to hire data scientists who are only interested in creating beautiful models; their first priority is building a team that can translate whatever data they have into meaningful reports that drive business leaders to take action. At a time when many organizations are just getting started at building their talent analytics capabilities, it’s a good reminder to keep aspirations small, and not be discouraged if your analytics efforts don’t yet live up to the stories you’ve heard about what the Googles and IBMs of the world are doing with data.