You May Not Need Big Data After All
Artwork: Chad Hagen, Graphic Composition No. 2, 2009, digital
What’s the problem? To begin with, big data has been hyped so heavily that companies are expecting it to deliver more value than it actually can. In addition, analytics-generated insights can be easy to replicate: A financial services company we studied built a model based on an analysis of big data that identified the best place to locate an ATM, only to learn that consultants had already built similar models for several other banks. Moreover, turning insights from data analytics into competitive advantage requires changes that businesses may be incapable of making. One retailer, for example, learned that it could increase profits substantially by extending the time items were on the floor before and after discounting. But implementing that change would have required a complete redesign of the supply chain, which the retailer was reluctant to undertake.
The biggest reason that investments in big data fail to pay off, though, is that most companies don’t do a good job with the information they already have. They don’t know how to manage it, analyze it in ways that enhance their understanding, and then make changes in response to new insights. Companies don’t magically develop those competencies just because they’ve invested in high-end analytics tools. They first need to learn how to use the data already embedded in their core operating systems, much the way people must master arithmetic before they tackle algebra. Until a company learns how to use data and analysis to support its operating decisions, it will not be in a position to benefit from big data. (See the sidebar “Who Benefits from Big Data?”)
Big data is big business. The IT research firm Gartner estimates that total software, social media, and IT services spending related to big data and analytics topped $28 billion worldwide in 2012. All estimates predict rapid growth. In addition to vendors, at least three types of organizations are harvesting value from big data.
Companies with a tradition of fact-based decision making. Procter & Gamble and UPS are exemplars. In the 1920s P&G became the first company to make significant product and advertising decisions on the basis of detailed market research data laboriously gathered during door-to-door conversations with consumers. Today P&G uses computer modeling and simulation to analyze multiple data sources—comments collected from social media, consumer sales data, RFID data, and information from the company’s highly digitized processes—and makes fact-based decisions on a daily basis.
UPS started tracking the movements of its vehicles and packages in the 1980s. More recently, the company began using big data from telematics sensors installed in its vehicles together with mapping data and other real-time reports of drop-offs and pickups from its drivers. Using these data, UPS designs routes that, for example, minimize the number of left turns a driver must make to deliver a load. Such changes can generate big payoffs, because they are deployed with more than 100,000 drivers around the world. In 2011, guided by analysis of big data, UPS avoided adding more than 11,000 metric tons of CO2 to the atmosphere and saved $30 million in fuel costs.
Engineering and research functions. Many engineering-based companies rely on analysis of big data to make critical operating decisions. For example, as long ago as the 1960s ExxonMobil invented 3-D seismic technology, which revolutionized how the oil and gas industry decided where to drill. Collecting and processing 3-D images of geologic formations beneath the earth’s surface provided more and better data for those decisions. Today the company’s scientists and engineers use 4-D analysis (which shows changes in a field over time) to further reduce the costs and risks of exploration. Researchers at pharmaceutical and biotech companies are also using big data and powerful processing to help drive business decisions.
The best web-native companies. Companies that connect with customers solely via the internet can capture enormous amounts of data about customer behavior. This is the perfect big-data opportunity for making fact-based decisions. One technique, which has become almost a governing ethos for Google, Amazon, Netflix, and eBay, is A/B testing, in which some users are diverted to a slightly different version of a web page, which is presenting a new idea or product. The behavior of those users (B) is then compared with that of users on the existing page (A), and the results are often subjected to sophisticated statistical analysis. This technique transforms much product-development decision making from a subjective to an objective exercise. Product designers are often shocked to learn how bad their instincts and rules of thumb are. In a neat twist, Google and Amazon are now providing tools that will help other companies follow the same approach.
Over the past three years, we’ve conducted seven case studies and interviewed executives at 51 companies to understand how companies generate business value from data. We have found that those that consistently use data to guide their decision making are few and far between. The exceptions, companies that have what we call a culture of evidence-based decision making, have all seen improvements in their business performance—and they tend to be more profitable than companies that don’t have that kind of culture.
The digital economy is all about capturing, analyzing, and using information to serve customers. Most companies can significantly improve their business performance simply by focusing on how operating data can inform day-to-day decision making. So why don’t more companies make better use of data and analysis? One reason may be that their management practices haven’t caught up with their technology platforms. Companies that installed digital platforms—ERP and CRM systems, real-time data warehouses, and homegrown core information systems—over the past 10 to 15 years have not yet cashed in on the information those platforms make available. In addition, adopting evidence-based decision making is a difficult cultural shift: Work processes must be redefined, data must be scrubbed, and business rules must be established to guide people in their work. The good news is that once companies have made the cultural change, they usually don’t go back, and their operating improvements are not easily replicated by competitors.
Our research suggests that companies with a culture of evidence-based decision making ensure that all decision makers have performance data at their fingertips every day. They also follow four practices: They establish one undisputed source of performance data; they give decision makers at all levels near-real-time feedback; they consciously articulate their business rules and regularly update them in response to facts; and they provide high-quality coaching to employees who make decisions on a regular basis.
Before we explore those practices, let’s look at a company that has had a culture of evidence-based decision making since its founding.
Empowering Employees to Make Good Decisions
In the 1970s Southland Corporation, known for pioneering the concept of the convenience store chain with its 7-Eleven shops, divested its Japanese stores, and Seven-Eleven Japan was born. Toshifumi Suzuki, the first CEO, decided early on that the key to profitability for the company’s tiny stores would be rapid inventory turnover. So he placed responsibility for ordering—the single most important decision in the business—in the hands of the stores’ 200,000 mostly part-time salesclerks. Those employees, Suzuki believed, understood their customers and, with good information, could make the best decisions about what would sell quickly.