“There’s a ton of information out there,” Steven Rosenbush and Michael Totty write. “And businesses are figuring out how to put it to work.” [“How Big Data Is Changing the Whole Equation for Business,” Wall Street Journal, 8 March 2013] They note that the definition of big data is “squishy” but believe that the only thing businesses need to know is that they have “more information than they used to, it comes from many more different sources than before, and they can get it almost as soon as it’s generated.” Regardless of how you define or label this data, “businesses in a slew of industries are putting it front and center in more and more parts of their operations.” Rosenbush and Totty continue:
“They’re gathering huge amounts of information, often meshing traditional measures like sales with things like comments on social-media sites and location information from mobile devices. And they’re scrutinizing it to figure out how to improve their products, cut costs and keep customers coming back. Shippers are using sensors on trucks to find ways to speed up deliveries. Manufacturers can trawl through thousands of forum posts to figure out if customers will like a new feature on their product. Hiring managers study how candidates answer questions to see if they’d be a good match.”
In an opinion piece published in the Financial Times, Doug Laney, Hung LeHong, and Anne Lapkin, research analysts at Gartner, write, “Big data is one of the most hyped terms on the market today”; but, they also agree that big data “means big money for some.” [“What Big Data Means for Business,” 6 May 2013] Daniel Burrus, a technology futurist, believes, “We’re starting to see that any company’s competitive advantage is increasingly determined by the quality of the data they have and how they’re using that data to make real-time decisions.” [“Competitive Advantage Is Increasingly Determined By Your Data,” Huffington Post, 8 May 2013]
The term “big data” has become so ubiquitous it won’t be going away anytime soon — despite protestations that its definition is unclear. One such protest comes from Ted Underwood, an Associate Professor of English at the University of Illinois, Urbana-Champaign, insists, “[The] conversation about ‘big data’ has become a hilarious game of buzzword bingo.” [“Against (talking about) ‘big data’,” The Stone and the Shell, 10 May 2013] He continues, “The discussion is incoherent, but human beings like discussion, and are reluctant to abandon a lively one just because it makes no sense. One popular way to save this conversation is to propose that the ‘big’ in ‘big data’ may be a purely relative term. It’s ‘whatever is big for you’.” He’s correct that “big” is a relative term and that it has little meaning for businesses beyond the fact that, whatever the size of the databases they must work with, it’s the value of the insights gained from the data that is really important. Laney, LeHong, and Lapkin offer Gartner’s definition of “big data”: “Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.” That definition might not advance the discussion too much further down the road, but it does make the point that advanced information processing (i.e., new technologies) are required to make sense of it.
They then discuss “three categories of business opportunities” in which “big data can unlock new business value in a wide variety of ways.” Those areas of opportunity are: “Making better-informed decisions, discovering hidden insights, and automating business processes.”
The types of decisions that can be enhanced by data analysis are endless. Laney, LeHong, and Lapkin mention, for example, “prices, promotions, staffing levels, or investments.” Tim Kastelle, a senior lecturer at the University of Queensland Business School, asserts that the ubiquity of data and analysis means, “We’re all in the knowledge business now.” [Innovation for Growth, 19 February 2013] If he’s correct (and he probably is), then businesses must master the basics of how to leverage the data they use. Rosenbush and Totty relate how Caesars Entertainment Corporation uses big data analysis to help its employees make better decisions about healthcare treatments and how Catalyst IT Services, a Baltimore-based technology outsourcing company, uses big data analysis to make hiring decisions. Laney, LeHong, and Lapkin remind us that it is not just humans who are making better decisions through data analysis. They explain:
“[When Wal-Mart] wanted to help its website shoppers find what they were looking for more quickly. It developed a machine learning semantic search capability using clickstream data from its 45m monthly online shoppers combined with product and category-related popularity scores generated from text mining social media streams. Wal-Mart’s resultant ‘Polaris’ search engine yielded a 10 per cent to 15 per cent increase in online shoppers completing a purchase (or around a billion dollars in incremental sales).”
Taylor Provost predicts, “Using the totality of an enterprise’s data to make forward-looking business decisions, develop new products, and improve marketing efficiency will be so common that there won’t be a name for it.” [“Prepping for the Big Data Future,” CFO, 3 December 2012] There are hurdles, however, that are likely to be encountered along the road to this business-as-usual dream. Provost explains:
“Using Big Data to make better business decisions requires that all the data a company collects and manages be integrated, not locked away in silos. Part of that problem is technological — the integration is poor because the tools are outdated — and part is political. Data is power. It can sometimes determine compensation; it’s frequently used to lobby for resources and position. People are not always willing to share.”
Even if the cultural datasharing challenge can be overcome, integrating disparate data is not a trivial task.
Laney, LeHong, and Lapkin write:
“Big data analysis can also be used to discover opportunities that are obvious only by looking at large sets of detailed data. Many organisations are mining vast pools of data to discover hidden insights that were previously unavailable to them – often in the development of new or enhanced products.”
Obviously discovering hidden insights is closely linked to making business decisions. They discuss how Climate Corp uses big data analytics to determine weather-related crop risks. Rosenbush and Totty describe how UPS combined “GPS information and data from fuel-efficiency sensors installed on more than 46,000 vehicles” to gain insights. The results were impressive. They report, “UPS in 2011 reduced fuel consumption by 8.4 million gallons and cut 85 million miles off its routes.”
Automating Business Processes
Laney, LeHong, and Lapkin write, “Finally, new technology can be used to leverage big data in real time, allowing analysis to be built into processes so that automated decision making can occur.” The example they discuss is how McDonald’s now uses highspeed image analytics to ensure that its buns are baked uniformly. My company, Enterra Solutions, provides an automated business solution for a problem that has plagued manufacturers for year: retailer compliance. Enterra’s Retailer Compliance Module enables an organization to proactively sense and adapt to changing retail requirements, reducing potential future penalties and improving deduction recovery. The system monitors and imports retailer compliance requirements, it applies analytics to identify gaps between the retailer’s requirements and an organization’s capability to comply. Not only does this address potential compliance issues long before orders are received, it provides a central communications hub and automates the collection of information to speed the research of penalties/shortages to help expedite disputes and exemptions. And by eliminating the time-consuming task of having to manually monitor retailer and carrier sites for requirement changes, it allows manufacturers to reduce their compliance department costs overall.
“Going forward,” writes Burrus, “the type, quality, and relevance of the data will become far more important than the quantity of data, so being very good at managing these will create new ways to differentiate as well as find innovative approaches to creating and maintaining competitive advantage.” He continues:
“So with all of this data coming in, it’s clear that competitive advantage is going to be created by your use of data and by your ability to make sure you’re getting good data. After all, bad data yields bad decisions. You want to be able to draw the right conclusions from your data, as that’s what provides new opportunities, better solutions to problems, and new competitive advantage. … If you want to solve seemingly impossible problems and find new competitive advantage, you have to look at the type and quality of the data you’re generating and how you’re using it. When your data can empower your people and your machines to make better decisions faster, you’ll have increased competitive advantage.”
No matter how you define big data, the analysis that is now being done on very large datasets is changing the business landscape forever.