Big Data: Ignore the Hype and Concentrate on the Reality

Stephen DeAngelis

August 18, 2014

“Across manufacturing, healthcare, agriculture, retail and beyond,” writes Bernard Marr (@BernardMarr), “the rate that data on every activity is collected – no matter how seemingly trivial – means more opportunities to fine-tune procedures and operations to squeeze out every last drop of efficiency.” [“Big Data Is Changing Every Industry, Even Yours!SmartData Collective, 1 July 2014] He adds, “Different industries have responded to the call in different ways of course. Retail and sales will rely on gleaning as much information about their customers’ lives as possible, while in manufacturing the emphasis is on streamlining operations. Equipment calibration settings can be recorded and refined, and product storage environments monitored to determine the optimum conditions that lead to minimum spoilage and waste. For global companies this can mean collecting and analyzing data from plants across the world, allowing minor variances to be studied and their results understood.” A number of pundits have asserted that Big Data is no more than a buzz word whose time will come and go. They claim that the hype about Big Data has far exceeded the results it has achieved. Some of their arguments are undoubtedly true. The hype over new concepts almost always promises faster and more spectacular results than are actually achieved. Big Data is no different.

 

There is, however, a growing number of serious analysts who have examined the reality of what Big Data has achieved and they continue to insist that Big Data era is here to stay (even if the buzz word eventually fades into history). For example, Marr reports, “Last year pharmaceutical giant Merck used analysis to dramatically cut the amount of waste caused by variance in manufacturing environment conditions. It took three months and involved 15 billion calculations on individual production data from 5.5 million vaccine batches. This allowed them to discover the optimum conditions during the fermentation process, and should greatly increase their yield, once the FDA has approved the proposed changes to the manufacturing process.” Mark van Rijmenam (@VanRijmenam) reports that Nordstrom is also a proponent of Big Data analysis. He writes, “For Nordstrom, providing customers a personalized and relevant experience is all about data, big data. With different experiments they learn what works and what does not work and as such have a competitive advantage.” [“How Fashion Retailer Nordstrom Drives Innovation With Big Data Experiments,” SmartData Collective, 26 August 2013]

 

Boston Consulting Group analysts, Robert Souza, Thomas Jensen, Cornelius Kaestner, and David Potere, insist that Big Data analytics are particularly suited to benefit the retailing sector. “Traditional retailers generate and capture a deluge of data,” they write, “most notably, customer transaction histories that can reveal detailed product affinities and promotional and marketing response rates. Now the emergence of big data and advanced analytical tools and techniques can connect data with a larger context. Big data can explain the who, what, when, where, why, and how of retailing.” [“Making Big Data Work: Retailing,” bcg.perspectives, 24 June 2014] Despite the fact that retailers collect mountains of data, the BCG analysts report, “Most retailers have not yet built the analytical capabilities and internal processes necessary to take advantage of the deep well of information they can access.” This perplexing inaction is often explained by noting that many executives still don’t understand Big Data and that they have been burned in the past when undertaking large IT projects involving new technologies. Souza, Jensen, Kaestner, and Potere, conclude:

“Many retailers have not figured out where and why they are winning and where and why they are losing. They struggle to discover which prices, promotions, and store locations are working best. They have a hard time taking advantage of all the contextual information around transactions that could make a difference in sales. In effect, they know the outcomes of millions of real-time experiments, but they are not able to look at and learn from them. All this leads to missed opportunities. Ultimately, it opens doors to online and direct sellers, which often have better data and more sophisticated analytics.”

They believe there are three “high-potential opportunities” for retailers. Those opportunities include: Boosting the effectiveness of promotions; targeting pricing precisely; and, understanding the value of a network. The latter opportunity requires some explanation. The analysts explain:

“Big data reveals another way to think about store networks. One specialty retailer was able to unlock value from its retail footprint with an advanced analysis of its network. Consumer research suggested that a well-known technology brand had not achieved its full potential for sales among a key customer segment, in part because its existing direct-sales network was spotty in many areas. The retailer believed it could help the brand provide the necessary coverage for those businesses, but it needed proof. The retailer decided to analyze how well 12 million businesses and 117 million households in the United States were covered by its network, by its competitors’ networks, and by additional retailers that could be partners with the technology brand. It used geoanalytical techniques to examine 3.4 billion point-to-point customer trips in order to identify gaps in the technology brand’s retail and partner network. The results were clear: The specialty retailer was 37 percent closer on average to U.S. households and businesses than its competitors were. In fact, the retailer had the best coverage of key customer segments when compared with competitors and other potential partners in almost all U.S. regions. The company determined the true value of its last mile by looking at it through the lens of a complementary player. As a result, the retailer was able to partner with the technology brand to generate $40 million in incremental revenues from its existing network at little additional cost.”

Big Data analytics was once thought to be useful and affordable only to large companies. Michelle Pluskota, Vice President of Business Services for Comcast’s Heartland Region, reports that small and medium-sized companies (SMBs) are now jumping on the Big Data analytics band wagon. “[Big Data] is catching their attention,” she writes, “because it offers a cost-effective way to mine every single bit of data they collect on a daily basis.” [“What big data can do for your business,” Crain’s Detroit Business, 2 July 2014] She continues:

“Many SMBs are starting to benefit from big data. Consider Mom and Pop grocery stores. Some are winning in their categories by mining and analyzing customer buying trends gathered through their loyalty programs and then boosting their offerings, special savings, and rewards. As a result, they are seeing more of their customers making purchases, and they are drawing in new customers attracted by the offerings. … It’s what the company does with this information that truly turns it into big data. It not only gathers and stores the information, but it also shares it with everyone in its business and supply chain who needs it. Those with access can excavate what they want and analyze the data to glean intelligence and insight that lead to better business practices and profits.”

Brian Taylor (@BrianB2BCopy) notes that a report published by the Economist Intelligence Unit (EIU), entitled Decisive action: How businesses make decisions and how they could do it better, “suggests that predictive analytics is driving better results for growing firms.” [“Big data drives better decision making, according to The Economist Intelligence Unit,” TechRepublic, 16 July 2014] The report was commissioned by Applied Predictive Technologies (APT) and Taylor interviewed APT’s Rupert Naylor “about the report’s big takeaways for tech decision makers.” Naylor stated:

“I think the big takeaway is that you should no longer be debating whether you have the data or not. You should be thinking about how to use it and analyze it. If you look at the companies that are growing faster than their peers [in the report], those are the companies that are analyzing their data and running business experiments, running tests to drive their decisions. 45% of these fast-growing companies were using tests to make decisions, and only 10% of those growing more slowly than their competition were using that approach. It is clear that we have an analytic tool with big data to drive better decision making, and the companies that are more advanced on that path are really driving superior performance. That to me was one of the major takeaways.”

Every business is unique and each business’ approach to using Big Data should be unique as well. Big Data analytics are part of the next big thing that is going to take businesses by storm. Hype scares off a lot of people because hype is often associated with bubbles that can burst. If business executives take a practical and pragmatic approach to using Big Data, they can ignore the hype and find the real opportunities buried in the data they already have.