Big Data: Too Big to Ignore
Short Takes...on Analytics
|Posted by Jim Guszcza, Deloitte Consulting LLP|
Whatever else one might say about the business press, it is consistent. With a notable regularity, good ideas are in turn noticed, branded, given a bullet point-friendly vocabulary, championed, boosted, and then suffer the inevitable backlash. This cycle is playing itself out once again, as discussions of business analytics are increasingly framed in terms of the newly ubiquitous theme of “big data”. The result is a breeding ground for confusion.
While much of language surrounding analytics – “big data”, “data exhaust”, “data products”, “data science” – is new, what it describes is not. In fact, a classic example dates back to the midcentury era of Mad Men: credit scoring. Here raw credit information (in raw form, a type of “big data” emanating from “data exhaust”) is first synthesized, then analyzed by “data scientists”, and ultimately implemented as a “data product” that enables dramatically better decisions in an ever-widening variety of applications. In the past decade, this pattern has been repeated in domains ranging from baseball scouting to commercial insurance underwriting to patient safety to predictive policing and child support enforcement.
Business analytics is therefore hardly new, and anything but a passing fad. So why the backlash? For better or worse, the topic has recently come to be enmeshed in the language of “big data”. This language tends to play up the technological inputs into the analytic process (data sources, data warehouses, analysis tools, and so on) and play down the domain-specific, strategic, and judgmental aspects of the process. The resulting muddle breeds skepticism and strategic errors.
To be clear, big data in the true sense of the term empowers important scientific breakthroughs, innovations, and even new business models. On-line translation tools, social networking products, and recommendation engines for items ranging from books to romantic partners have changed not only the business world, but our culture as a whole. In such applications, the rapidly flowing volumes of data “in the wild”, the tools and methods needed to work with it, and the resulting data products and business models are all new, valuable, and worthy of further adoption.
The problem is that the language of big data has been stretched to cover the entirety of business analytics. But the bone fide big data examples listed above are not representative of many of the business analytics challenges one regularly encounters. Compared with the above examples, the challenges of many business analytics applications have somewhat less to do with data engineering and more to do with strategy, reflecting domain knowledge in rigorous data analysis processes, blending expert judgment with ambiguous data indications, culture change, communication, and often downright persuasion. There is more to business analytics than analytics.
That pop culture classic Moneyball is a case in point: Billy Beane’s data-enabled raid on an inefficient market for baseball talent involved neither terabytes of data nor MapReduce technology. What it did require was a guiding strategy, a commitment to insightfully analyzing the right data (as distinct from big data), and an ability to lead the organization to use the resulting insights to make better decisions under uncertainty.
Most analytics projects rely on some combination of data, tools, technology, and data science skills. But it is important to remember that data is not the same thing as information, and size is only one of many considerations. Furthermore, an effective mix of technology capital, information capital, and human capital is application-specific and can be intelligently determined when a clear strategy has been articulated.
To borrow another slogan from the book of big data, “data is the new oil.” Fair enough. But organizations that let the data tail wag the strategic dog risk experiencing data as the new turmoil.
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