“In God we trust. All others must bring data.” – W. Edwards Deming.
Deming died in 1993, but the oft-quoted statistician and quality management guru’s words still ring true in today’s competitive environment where data is gaining more importance in business dealings.
Specifically, it’s estimated by IDC that worldwide revenues for big data and business analytics will hit $203 billion in 2020, a significant jump from the $130.1 billion spent last year. That nearly 12% annual growth reflects the business shift toward data-driven decisions and the increasing availability of data.
But while businesses are showing no hesitation in beefing up data warehouses, does anyone really know what to do with all that data to make money now with what they currently possess?
Andrew Wells, CEO of Aspirent and co-author of “Monetizing Your Data: A Guide to Turning Data Into Profit-Driving Strategies and Solutions” with Kathy Williams Chiang, believes too many companies do not.
The problem, he says, is that company leaders are issuing the wrong instructions to data scientists.
“In the old era of analytics, the analytics were clustered around the question,” Wells says. “The question helped you describe what was going on in the business.”
Today, leaders should instead make a decision—such as redesigning a website – and then collect the data to see if that’s a viable option or will cost too much money.
“A decision is actionable. It’s what you go do. So, you center your analytics around the decision,” Wells says. “Simply helping someone answer a question doesn’t necessarily help them move the needle on a particular part of their business. But if you help them make a decision, you’re making the analytics actionable and something that they can go achieve.”
Wells says he has heard more than one story about how data scientists spent a considerable amount of time collecting data only to find they didn’t understand the question correctly and so collected the wrong data. Time and resources are wasted and the business is left without a way to earn revenue from all that data.
That’s not to say this is the fault of the data collectors, as they sometimes are not brought into the loop soon enough to really understand what is being sought by senior business leaders. Part of challenge today is bridging that communications gap so that data scientists better grasp the business strategy and business leaders better understand how and when to effectively use data.
“The big challenge now is that you have all this data you’re collecting, but just throwing data scientists at it and hoping they come up with insights that are commercially viable tends to be a very costly and unproductive exercise,” Wells say.
That’s why seeking data to support – or dispute – a decision makes more sense, he says.
“If you think about what enables manages and their day-to-day operations, it’s really about helping them make better decisions. It’s not ‘give me a tool that helps me mine data,’” Wells says.
See if this sounds familiar: Someone in your organization has an amazing idea or insight that he or she claims is going to produce loads of revenue. But senior leaders won’t give it the green light because the idea would require billions of dollars to execute and doesn’t tie into business objectives. As Wells says, the business won’t give “legs” to the idea.
Makes sense, right? Senior leaders must ensure that business initiatives tie into the business objectives, so why doesn’t data get the same scrutiny? That’s a mistake that needs to be corrected if leaders want to ensure that the data they have now and collect in the future has a direct payoff, he says.
“By mapping your decisions to key business drivers that achieve corporate objectives, you are charting a clear path to actionable analytics,” write Wells and Chiang in their book.
The authors also advocate the use of decision theory, which can help guide companies toward the most feasible decision that will have the greatest impact. By laying out expected acts, events, outcomes and payoffs, managers can better see the scope of proposed actions and make more objective choices.
“It’s all about how we can better leverage the data to make better decisions, Wells explains. “We’re shifting from just going on a ‘gut’ feel to what the data tells us. So, your goal is to have the data support what your gut is telling you, or showing you that your gut is wrong.”
Another approach that can help companies better monetize their data is doing an economic value analysis that can give decisionmakers a clearer picture of the economic tradeoff of the decisions that are available to them.
“I think using data to support decisions is a new era for analytics,” Wells says. “I think more and more people are getting it. I meet with Fortune 500 leaders, and once you explain the mental shift to them, it’s hard to go back.”