By Doug Stephens
I defy you to find a marketing conference today that doesn’t have at least one track focused on big data analytics. Big data is a very big deal. Yet, despite all the talk and attention, a 2014 study suggests that almost half of retail executives across categories still don’t fully understand how big data can help them solve business problems.
This understanding-gap is a huge problem if you happen to be the one in the organization attempting to garner consensus on a big data strategy. After all, it’s difficult to build a case for investment and trial when such a high percentage of leadership may not even be able to see the potential payoff.
By the same token, there’s also little sense in spending more time and energy selling your executives on the technical aspects of big data analytics. Does anyone really care about the technicalities of petabytes, distributed processing and Hadoop databases? Probably not.
However, I do think there’s a way to present the big data opportunity to your business leaders that cuts through the jargon and technicality and brings big data to a level every executive can appreciate and connect to in a very clear and intuitive sense.
It involves sharing two very simple stories.
The first story
I came across this article by Fast Company contributor Farhad Manjoo and thought it was brilliant. Manjoo wrote:
A few years ago, at the Washington Hospital Center, a sprawling urban health-care facility near D.C., the emergency-room doctors noticed a troubling medical mystery common to many hospitals: A large number of patients were returning to the ER, sick again, within just a few weeks of being discharged. Doctors believed that many of these readmissions could have been prevented with better follow-up care, but they had no way to predict which patients were most likely to become ill again. The issue became even more pressing when Medicare announced that it would start penalizing hospitals for patients readmitted in less than 30 days after discharge.
At a loss for answers, the hospital began working with Eric Horvitz a computer scientist (and physician) who works at Microsoft Research. Instead of a team of slavish residents, Horvitz’s main investigative tool is data. He and his colleagues built a system to analyze more than 300,000 ER visits. They looked for correlations among 25,000 variables, including the patients’ medications, vital signs, and doctors.
In the thicket of information, Horvitz stumbled upon a few intriguing surprises. One was the length of a patient’s stay in the hospital. Horvitz’s data showed a tipping point of 14 hours–if you’re in the ER for longer than that, there’s a good chance you’ll be back. Another red flag was fluid. The data showed that any mention of that word on a patient’s medical chart significantly increased the likelihood of readmission. “You look at that and you say, ‘Wow,’ ” Horvitz says. “We don’t know why fluid is so important, but it seems to matter–and it can help doctors take necessary preventative action.”
Horvitz built his findings into a program called the Readmissions Manager, which Microsoft sells as part of its health-care IT suite. It analyzes hospital data to produce a readmissions forecast for each patient, and doctors can tailor follow-up appointments based on the forecast. How much will this reduce costs? Horvitz is formulating clinical trials now to assess the savings. Studies show that 20% of patients released from American hospitals need to be readmitted within 30 days, an inefficiency that costs Medicare $17 billion a year. “There’s just so much data dropped on the floor in health care that we can learn from,” Horvitz says.
Discovering Invisible Truths
Without even knowing your business, I guarantee it’s awash in questions that are every bit as mysterious and inexplicable as Washington Hospital’s readmission problem. Questions like; why some of your customers are more profitable than others? Why certain products cause a disproportionate percentage of returns? Which external factors have the greatest impact on demand for your products? And which skills and traits your best employees have in common – to name just a few.
What would the value be to your business of understanding the root causes, and deep correlations lying behind these sorts of questions? I suspect it could be enormous!
The second story
You may be surprised to learn that each year in the United States there are 1,638,910 new cases of cancer diagnosed. In turn, 577,190 Americans succumb to cancer-related illnesses each year .
So, with those statistics in mind, would it not follow that if we could eradicate all forms of cancer, life expectancy in America would dramatically increase? It seems logical to think so, doesn’t it?
However, according to Google CEO Larry Page, “…if you solve cancer, you’d add about three years to people’s average life expectancy”. Not thirty or even thirteen but just three!
If this is indeed the case, the surprisingly low impact from curing cancer prompts one to wonder what might happen if the money, effort and intellectual horsepower currently being dedicated to its cure were redeployed against other potent problems in America like mental health, poverty, addiction etc. Could greater effort and resources in these areas have a more profound impact on the longevity of Americans? It’s entirely possible.
Disproving Erroneous Assumptions
So, what kinds of errant assumptions does your business operate against every day? What tasks are you focusing resources on, under the false assumption that the payoff justifies the effort? What products, programs and channels are you dedicating the most organizational attention to and what’s the true return? Just imagine if you could apply the majority of your organizational energy against only those activities that truly reap the highest return and cease all together the activities of little or no value. How could that shift in focus transform your business? I imagine it could cause a quantum change in performance!
Sell the questions – not the answers
Stories like these demonstrate the true power and promise of big data, not only to help us find hidden correlations within our business but also to shake us from erroneous beliefs that might be holding us back. 
In other words, don’t waste time trying to sell big data as the answer until you’ve fostered ironclad consensus on the perplexing questions that plague the business, and the financial upside of solving those questions. If you achieve buy in on the questions, you’ll be getting a built-in nod to big data – as it is, quite likely, the only practical means of parsing the enormous datasets involved.
In essence, your big data strategy doesn’t begin with agreeing on big data. It begins with agreeing on the big questions.