Introduction. What Makes Analytics Strategic – Strategic Analytics: The Insights You Need from Harvard Business Review

Introduction

WHAT MAKES ANALYTICS STRATEGIC

by Thomas H. Davenport

Organizations are eager to reap the rewards of data and analytics—to 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 weren’t 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, we’re using analytics in a much more dedicated manner. Around the turn of the century, companies started pursuing what might be called “strategic analytics”—analytics 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.1 With the substantial performance improvements these efforts enabled, senior executives began to pay attention to the potential of analytics. In a few short years, analytics became the basis of their business strategy and their approach to the marketplace.2 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 company—from 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. They’re using internal data to improve employee performance. Leaders are hiring new talent—data scientists, statisticians, and analysts—to draw conclusions about their data and use these findings to inform leaders’ strategic choices.

Strategic analytics are those that make a company’s strategy or business model possible. Consider companies today whose business model would not be able to survive without analytics and AI, like Google—the employer of Cassie Kozyrkov, a contributor to this book—or the e-commerce operations of Vineyard Vines, described in chapter 10. These companies realize that by using analytics more strategically, they’re 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.3 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.

The right talent and skill sets. Data-driven companies hire talented data scientists and quantitative analysts—often in large numbers. They build data teams around key talents and leverage those skills to reach their data goals. And they ensure their people have basic data analysis skills, no matter their role in the organization, so they can understand what data is telling them and make decisions based on it.

A data-driven culture. Perhaps most importantly, firms that use analytics strategically have cultures that emphasize data- and analytics-driven decisions. This is perhaps the most difficult aspect of strategic analytics to achieve. Such a culture typically requires that senior executives set the tone for their organizations, sponsoring analytical projects and insisting on data-driven decisions when feasible.

This book aims to help anyone learn the basics of data and analytics, assist their companies in becoming more data-driven, and encourage the application of analytics to strategic issues and problems. It is divided into three parts. Section 1 introduces you to key concepts in the data space and helps you increase your data literacy, so you can be part of the conversation. Section 2 helps you learn how your organization can become data-driven—what needs to be in place and what to think about as you consider applying strategic analytics. Finally, section 3 helps you see the opportunities that data affords and gives you examples of ways to use your analytics for maximum benefit.

As you make your way through this book, consider whether you and your organization are prepared for strategic analytics by asking these questions:

  • Do you personally have the skills you need either to analyze data or to consume it effectively?
  • Do your people understand the basics of data and analytics? Do you have the right skills and talent on your team?
  • Is your organization putting modern analytical technologies in place?
  • Is this analytical horsepower being applied to problems and issues that will make the company more successful?
  • Are the decisions your company makes using data to inform them? If not, are there ways to collect more information to make this shift?
  • Does your organizational culture encourage analytical thinking from every member of your team?

If you’re unsure about your answers to these questions—or you realize that your company isn’t doing enough to make the most of its analytical capabilities—it’s time to engage with others in your company to address these issues and get started.

We may think of analytics as a field involving data and technology, but as many of the chapters in this book suggest, it is primarily about people. Their skills, priorities, and attitudes determine whether decisions and actions are taken on the basis of data and analysis, or intuition and guesswork. If your organization has enough people who care about strategic analytics—including yourself—the rest of the journey is relatively simple.

NOTES

  1. 1. Thomas H. Davenport, Jeanne G. Harris, David W. De Long, and Alvin L. Jacobson, “Data to Knowledge to Results: Building an Analytic Capability,” California Management Review 43/2 (Winter 2001): 117–138.

  2. 2. Thomas H. Davenport, “Competing on Analytics,” Harvard Business Review, January 2006, https://hbr.org/2006/01/competing-on-analytics; Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics: The New Science of Winning (Boston: Harvard Business School Press, 2007).

  3. 3. Randy Bean and Thomas H. Davenport, “Companies are Failing in Their Efforts to Become Data-Driven,” hbr.org, February 5, 2019, https://hbr.org/2019/02/companies-are-failing-in-their-efforts-to-become-data-driven.