Big Data is all the rage, with more and more companies drawing on burgeoning data streams to better understand their customers and make better business decisions. But it can be tricky when you’re trying to turn data into insights and actions that provide a competitive advantage. And both data analysts and business executives can get in the way of progress.
Data scientists might be blessed with analytical and programming skills, but many struggle to influence business execs to set or change business plans based on the analytics they do. On the other side of the divide, some senior executives may resist the idea of changing how strategies are created and executed. Many are comfortable with the traditional monthly sit-down with unit managers to discuss whether sales and spending are on track. They may disparage using big data and advanced analytics to test strategies they developed with their tried-and-true industry knowledge and gut feeling.
Can’t we all just get along?
How to break through? It starts with the data scientists and other analysts—they need to drop the “data geek language” when talking to execs, said Jim Robertson, director of financial planning and analysis at Emeritus Senior Living, a Seattle-based company that manages retirement communities throughout the U.S. “You can’t allow them to walk into a meeting with senior business executives and start talking about Pearson correlations or regression coefficients and what portion of variance is unexplainable versus explainable,” Robertson said. If executives don’t understand what they’re told, they’re not going to trust any recommendations they’re given. Robertson pointed to graphs and pictures as one way data scientists can interpret their analysis and conclusions. “Analysts need to understand that they don’t have to explain how the car works. But they do need to get the confidence of the executives to trust that they know what they’re doing.”
Organizations committed to harnessing big data and advanced analytics also need to make sure their analytics people learn the dynamics of the business. Even if the analytics team can talk a good game, an analytics program will go nowhere if business users don’t know the rules. Business execs need to know what the data scientists are doing so they can have intelligent conversations with them, Robertson said. And they must be able to explain what business problems need solving.
Elissa Tucker, APQC’s research program manager for human capital management, says it helps to develop a way for people with various business-management backgrounds to learn what it is data scientists do. That will lead to fruitful collaboration and problem solving.
Once again, the push for continuous learning and collaboration is underscored.
For more on using common languages to talk about big data and analytics, see: