A practical guide to capitalizing on big data
By Dominic Barton and David Court.
Big data and analytics have rocketed to the top of the corporate agenda. Executives look with admiration at how Google, Amazon, and others have eclipsed competitors with powerful new business models that derive from an ability to exploit data. They also see that big data is attracting serious investment from technology leaders such as IBM and Hewlett-Packard. Meanwhile, the tide of private- equity and venture-capital investments in big data continues to swell.
The trend is generating plenty of hype, but we believe that senior leaders are right to pay attention. Big data could transform the way companies do business, delivering the kind of performance gains last seen in the 1990s, when organizations redesigned their
core processes. As data-driven strategies take hold, they will become an increasingly important point of competitive differentiation. According to research by Andrew McAfee and Erik Brynjolfsson, of MIT, companies that inject big data and analytics into their operations show productivity rates and profitability that are 5% to 6% higher than those of their peers.
Even so, our experience reveals that most companies are unsure how to proceed. Leaders are understandably leery of making substantial investments in big data and advanced analytics. They’re convinced that their organizations simply aren’t ready. After all, companies may not fully understand the data they already have, or perhaps they’ve lost piles of money on data-warehousing programs that never meshed with business processes, or maybe their current analytics programs are too complicated or don’t yield insights that can be put to use. Or all of the above. No wonder skepticism abounds.
Many CEOs, too, recall their experiences with customer relationship management in the mid-1990s when new CRM software products often prompted great enthusiasm. Experts descended on boardrooms promising impressive results if new IT systems were built to collect massive amounts of customer data.
It didn’t turn out that way. Too many C-suites were blind to the practical implications of new CRM technologies-namely, that to capitalize on them, organizations would have to make complex process changes and build employees’ skills. The promised gains in performance were often slow in coming, because the systems remained stubbornly disconnected from how companies and frontline managers actually made decisions and new demands for data management added complexity to operations. To be fair, most companies eventually managed to get their CMR programs on track, but not before some had suffered sizable losses and several CRM champions had lost career momentum.
Given this history, we empathize with executives who are cautious about big data. Nevertheless, we believe that the time has come to define a pragmatic approach to big data and advanced analytics-one tightly focused on how to use the data to make better decisions.
In our work with dozens of companies in six data-rich industries, we have found that fully exploiting data and analytics requires three mutually supportive capabilities. First, companies must be able to identify, combine, and manage multiple sources of data. Second, they need the capability to build advances analytics models for predicting and optimizing outcomes. Third, and most critical, management must possess the muscle to transform the organization so that the data and models actually yield better decisions. Two important features underpin those companies: a clear strategy for how to use data and analytics to compete, and deployment of the right technology architecture and capabilities.
Equally important, the desired business impact must drive an integrated approach to data sourcing, model building, and organizational transformation. That’s how you avoid the common trap of starting with the data and simply asking what it can do for you. Leaders should invest sufficient time and energy in aligning managers across the organization in support of the mission.
1. Choose the right data
The universe of data and modeling has changed vastly over the past few years. The sheer of volume of information, particularly from new sources such as social media and machine sensors, is growing rapidly. The opportunity to expand insights by combing data is also accelerating, as more-powerful, less costly software abounds and information can be accessed from almost anywhere at any time. Bigger and better data give companies both more-panoramic and more-granular views of their business environment. The ability to see what was previously invisible improves operations, customer experiences, and strategy. But mastering that environment means upping your game, finding deliberate and creative ways to identify usable data you already have, and exploring surprising sources of information.
Source data creatively. Often companies already have the data they need to tackle business problems, but managers simply don’t know how the information can be used for key decisions. Operations executives, for instance, might not grasp the potential value of the daily or hourly factory and customer-service data they possess. Companies can impel a more comprehensive look at information sources by being specific about business problems they want to solve or opportunities they hope to exploit. For example, a banking team that needed to improve the efficiency of its customer-service operations from ATM transactions, online queries, customer complaints, and so on. That allowed duplicative interactions to be identified, thereby reducing costs and streamlining the customer experience.
Managers also need to get creative about the potential of external and new sources of data. Social media are generating terabytes of nontraditional, unstructured data in the form of conversations, photos, and video. Add to that the streams of data flowing in from sensors, monitoring processes, and external sources that range from local demographics to weather forecasts. One way to prompt broader thinking about potential data is to ask, “What decisions could we make if we had all the information we need?” Using that logic, one shipping company improved the on-time performance of its fleet by tapping specialized weather forecast data and live information about port availability that it hadn’t realized were available.
Senior executives can take the lead here. The CEO of one major packaged-goods company told us that he views data as a strategic asset whose value he takes into account when assessing potential acquisitions. But leaders at all levels must also be attuned to novel approaches to gathering and husbanding information. As business practices in the internet era continue to evolve, inspiration can often arise from a scan of the external environment.
Get the necessary IT support. Legacy IT structures may hinder new types of data sourcing, storage, and analysis. Existing IT architecture may prevent the integration of siloed information, and managing unstructured data often remains beyond traditional IT capabilities. Many legacy systems were built to deliver data in batches, so they can’t furnish continuous flows of information for real-time decisions.
Fully resolving these issues often takes years. However, business leaders can address short-term big data needs by working with CIOs to prioritize requirements. This means quickly identifying and connecting the most important data for use in analytics, followed by a cleanup operation to synchronize and merge overlapping data and then to work around missing information. Such short-term tactics may lead companies to vendors that focus on analytics services or emerging software. New cloud-based technologies may also offer ways to scale computing power up or down to meet big data demands cost-effectively. Together those approaches establish an IT infrastructure that propels innovation by facilitating collaboration, rapid analysis, and experimentation.
This blog originally appeared at Harvard Business Review