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Data Science: “Why?” Is Just the First Question

jasonfoster Analytics, Blog, Business Intelligence

Wheaties and beer may be the breakfast of ex-champions, but Pop-Tarts and beer appear to be the breakfast of hurricane evacuees.  Back in 2004, Hurricane Frances was on its way across the Caribbean heading toward Florida’s coast, in the wake of Hurricane Charley which had arrived several weeks earlier.  A week prior to the storm’s predicted landfall, Wal-Mart’s chief information officer, Linda M. Dillman, was working with her staff to use their enormous data resources, including what had recently happened when Hurricane Charley hit, to try to “predict what’s going to happen, instead of waiting for it to happen,” as she put it.

Her experts mined their data and found that they would in fact need to increase inventory of several items, and not just bottled water and flashlights.  “We didn’t know in the past that strawberry Pop-Tarts increase in sales, like seven times their normal sales rate, ahead of a hurricane,” Ms. Dillman said in an interview. “And the pre-hurricane top-selling item was beer.” (Hays, 2004)

As an anecdote, the story is interesting, and one may even ask why they should care…

In every industry, all over the world, businesses are attempting to harness the power of data: precision medicine initiatives promise to reduce trial-and-error treatment of patients and reduce expenditures; fraud detection algorithms rooted in predictive analytics are helping to protect consumers’ identities and deposits; the combination of behavioral and data science is helping to reduce energy consumption.  Opportunities are everywhere, if you know how to find them.

Consider the Pakistani start-up Cowlar, who began marketing what they term “A FitBit for Cows”, apparently taking a cue from the Israeli dairy industry who use similar sensors to track and analyze dairy cows.  Cowlar predicts that adopters of their technology can increase dairy production by as much as 15% (Huddleston Jr., 2016).  Many are probably also familiar with companies like Uber who have data science at the heart of their business model.  But both these companies entered the market with what are classically defined as sustaining innovations (making an existing product better) and incumbents must scramble and innovate to compete.

Many may also be aware of the demise of Blockbuster at the hands of Netflix.  Netflix was a truly disruptive innovation because when it entered the market with its mail-in subscription service, it wasn’t targeting the core customers of companies like Blockbuster, and only initially appealed to a small group of customers. However, as Netflix moved upmarket, it began offering streaming video and optimizing that service through data science.  Blockbuster collapsed.

According to recent studies, 91% of companies are increasing their data science expertise, but only 5% are prepared for industry disruption like that seen in the case of Blockbuster (Accenture, 2014).  Is your business shrugging off competitors whose market trajectories will eventually become disruptive to your core business? Are you prepared for this type of market disruption?

Whatever your answer to those questions, also consider this. The consulting firm McKinsey and Company estimates that “there will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions” (Manyika, 2011)  Businesses need not just talented data scientists, but analysis teams comprised of data scientists, software engineers, business analysts and company management to identify opportunities for their own sustaining and disruptive innovation.

Regardless of your investment in big data, will your company focus its resources on analyses that truly make a difference? Will you target the right customers or provide the right recommendations for cross-selling? How does your supply chain and your reverse logistics affect your customer retention?  Are you allocating your IT expenditures on the features of your software that improve customer and employee retention and give you competitive advantage?

The question “Why data science” is no longer the relevant question.  The relevant question is “How to do data science.. now?”

Bibliography

Accenture. (2014). Big Success with Big Data. Accenture.

Hays, C. L. (2004, 11 14). What Wal-Mart Knows About Customers’ Habits. Retrieved from www.nytimes.com: http://www.nytimes.com/2004/11/14/business/yourmoney/what-walmart-knows-about-customers-habits.html?_r=0

Huddleston Jr., T. (2016, 04 06). This ‘Fitbit for Cows’ Could Help Farmers Improve Milk Production. Retrieved from www.fortune.com: http://fortune.com/2016/04/06/fitbit-cows-cowlar-wearable/

Locke, S. (2015, 06 22). Hi-tech dairies help Israeli cows produce twice as much milk as Australian cows. Retrieved from www.abc.net.au: http://www.abc.net.au/news/2015-06-22/israeli-dairy-industry-pushes-boundaries-to-lead-world/6563694

Manyika, J. (2011, 05). Big data: The next frontier for innovation, competition, and productivity. Retrieved from www.mckinsey.com: http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation

About Jason Foster

Jason is a Technical Architect at Statera with a passion for data science, integration and engineering culture. When not working, he plays his Martin acoustic guitar, works through a cookbook (currently Thomas Keller’s Bouchon) and enjoys hiking and fishing in the beautiful Rocky Mountains.