Moneyball for business managers: The 7 components of a successful analytics program | MIT Sloan Executive Education

In 1977, Bill James published a landmark manual that codified the empirical analysis of baseball (Sabermetrics). During the Oakland A's 2002 season, General Manager Billy Beane famously used some of these concepts to field a team that, as a result, over-delivered on their value relative to their salaries. Beane’s innovative approach was chronicled by author Michael Lewis in Moneyball: The Art of Winning an Unfair Game. The sports data revolution was born. In 2008, five NBA teams had an analytics team; by 2016, all 30 teams had data scientists on board. From football to tennis, golf, and even little league baseball, new statistical tools are changing the game.

MIT Sloan Senior Lecturer Ben Shields says that organizations in every industry can learn a lot from the sports data revolution when launching, implementing, or refining their own analytics program. In his MIT Sloan Executive Education course, Analytics Management: Business Lessons from the Sports Data Revolution, Shield provides executives insight into the sports industry’s “secret sauce” and helps them apply it immediately to the development of their own analytics program.

Becoming an organization that makes data-fueled decisions requires more than sophisticated analytical methods and tools. Fulfilling the promise of analytics requires a sustained commitment to data-driven decision making—for most companies, this means implementing a formal analytics strategy. “One of the barriers to unlocking the full potential of analytics is organizational,” says Shields. “If a company’s analytics team operates in its own silo, it can be difficult for the company to truly become data-driven in its decision making. You drive change by integrating and using analytics on a daily basis.”

Auditing your analytics program: seven key components

How well have you incorporated analytics in your decision making? Is your company using data to discover new and innovative ways to create business value? Do you have a strategic plan for analytics in place?

In his recent MIT Sloan Executive Education webinar, and in his upcoming two-day program, Shields shares a framework for evaluating an existing or immanent analytics program. These seven components, and the questions they pose, helps business managers assess the strengths of their analytics strategy, identify areas for improvement, and prioritize next steps:

1. GOAL: Start with the problems you are trying to solve, and define clear and specific goals. What business value are you seeking to create?
2. STRATEGY: Align your analytics strategy with your organization’s strategy. What actions are you taking to create value?
3. DATA: Integrate multiple data streams, both quantitative and qualitative, into the decision-making process. Do you have enough data already to solve this problem and meet your goals? If not, what kind of data do you need to capture?
4. PROCESS: Think critically about the decision-making process, the types of decision you need to make, and the data you will use. Who is involved? What is the structure of the decision-making process? How are you going to track and iterate on the decision?
5. TECHNOLOGY: What technologies are you using to capture, manage, analyze, and visualize data? Do you have the right technology to obtain the data you need to make more effective decisions?
6. PEOPLE: What skills do the people on your analytics team have? Do you have a cross-functional team that includes people who can manage and analyze data, but also people who can translate that data to drive business impact.
7. COMMUNICATION: Develop a language for the output of your analytics program that key stakeholders can understand and act on. Who is going to communicate your findings and recommendations, in what format, through which channels, and with what frequency?

We invite you to learn more about Ben Shield’s course, Analytics Management: Business Lessons from the Sports Data Revolution.