Harvard Business Review - HBR Insights Future of Business Boxed Set (8 Books)

WHAT MAKES ANALYTICS STRATEGIC

by Thomas H. Davenport

Organizations are eager to reap the rewards of data and analyticsto learn about their customers by gathering vast amounts of data, to use that information to make better products and rise above the competition, and to enlist machine-learning algorithms to create new opportunities and improve performance. Yet, despite their efforts, many companies find that progress toward these goals is painfully slow. The successful use of analytics requires not only high-quality data and powerful hardware and software, but also a culture that encourages data-driven decisions and a set of skills to make them. Relatively few organizations have all those capabilities at scale.

Throughout most of the 60 or so years of business analytics, analytics have largely been tactical. They have described common and repetitive business transactions, they were largely backward-looking, and they werent highly visible to (or desired by) senior executives. Smart managers certainly paid attention to the numbers showing how much money they made on specific products or in particular quarters, but that kind of routine reporting could hardly be described as strategic. Companies spent far more money and effort putting transactional information systems in place than they did on analyzing the data that emerged from them.

Decades later, were using analytics in a much more dedicated manner. Around the turn of the century, companies started pursuing what might be called strategic analyticsanalytics that were used to predict what customers might buy; to close less profitable stores, branches, and product lines; or even to develop new service offerings. Capital One, for example, was spun out of a third-tier bank, but its credit card business was based on information-based strategy. It employed data and analytics to make virtually every decision in the companyfrom what interest rate to charge to which customers to target. In doing so, Capital One returned more value to shareholders during its first 10 years as a public company than any other firm in the United States.

Analytics are reaching new heights with the addition of new technologies, much more data, machine learning, and artificial intelligence (AI). Companies are using external data to train algorithms to help with decision making. Theyre using internal data to improve employee performance. Leaders are hiring new talentdata scientists, statisticians, and analyststo draw conclusions about their data and use these findings to inform leaders strategic choices.

Strategic analytics are those that make a companys strategy or business model possible. Consider companies today whose business model would not be able to survive without analytics and AI, like Googlethe employer of Cassie Kozyrkov, a contributor to this bookor the e-commerce operations of Vineyard Vines, described in chapter 10. These companies realize that by using analytics more strategically, theyre seeing better results, faster.

Despite this realization, many organizations still struggle to implement analytics effectively. In a survey of nearly 65 Fortune 1000 or industry-leading firms, my colleague Randy Bean and I discovered that 72% of large, sophisticated companies have not achieved data-driven cultures. Additionally, among those respondents:

• 69% reported that they have not created a data-driven organization
• 53% stated that they are not yet treating data as a business asset
• 52% admit that they are not competing on data and analytics

To truly leverage the value of strategic analytics, companies need to have some common elements in place.

Data and technology. Firms that succeed in strategic analytics either already possess large volumes of high-quality data and the technologies to manage them, or they do what is necessary to acquire them. That may involve sourcing external data, building digital infrastructures themselves, or turning to cloud computing.