How to Create a Successful data strategy

By the MIT CISR Data Research Advisory Board

Data Strategy

The MIT CISR Data Board defines a data strategy as a central, integrated concept that articulates how data will enable and inspire business strategy.

In general, a data strategy answers five questions:

  1. What is the organization’s vision for data? A data strategy lays out how data will enable specific business goals.
  2. How will the organization move forward on its data journey? A data strategy clarifies how the organization will execute desired data activities.
  3. How will the organization drive data adoption and use? A data strategy describes how the organization needs to change to maximize value from the desired data activities. It should include change management components (e.g., education, incentives, measurement, communication plans) that will inspire change at the individual, group, and organizational levels.
  4. When will the organization execute the proposed activities? A data strategy lists the sequence of steps that the organization will follow to move forward; it contains a roadmap with milestones and priorities.
  5. What is the organization’s economic logic? A data strategy explicitly describes how the company will monetize its data using some combination of improving, wrapping, and selling activities.

The MIT CISR Data Board, Member Data Strategies, and the Collaboration Process

The MIT CISR Data Board was established in 2015 to inform and influence data-specific research conducted at MIT CISR and in the broader academic community. In Q1 2018, the board included 89 data executives from 70 organizations around the world, representing 12 industries. At that time, the executives collaborated with MIT CISR researchers to produce a better understanding of contemporary challenges and opportunities associated with data strategy.

During Q1 2018, 52 of the organizations with participation on the board responded to a set of discussion questions on the maturity of their data strategies. The following breakdown describes data strategies in place at these organizations.

WHEN: Data strategies were new. On average, the existing iteration of an organization’s data strategy had been in place for 1.6 years.

WHERE: About half of the organizations’ data strategies were then owned either by IT (18%) or a formal data unit (34%). The remaining strategies fell under a diverse set of owners, such as an analytics group or digital team. (See figure 1.)

WHAT: For the most part, the organizations were creating data strategies that were much different from ones they had created in the past. Although about one-third of the organizations had possessed data strategies for years (with the existing iteration being evolutionary), two-thirds of the organizations possessed data strategies that were either dramatically refreshed or brand new (the organization had never had a data strategy before).

WHY: The organizations’ strategies were established to shift mindset, reengineer work practices, and advance architecture. As a result, the strategies proposed ways to attack hard problems such as breaking down data silos (e.g., to enable a 360-degree view of customers), defining the meaning of data as an asset, and prioritizing data investments (targeting needed improvements in data tech and data talent).

Figure 1. Data strategy owners in MIT CISR Data Board companies.
Source: MIT CISR Data Board responses to discussion questions on data strategy maturity in Q1 2018 Yammer forum conversations. Not all participants answered each question; N=50 responded to this question.

As MIT CISR researchers further examined the text of the responses, consistent patterns emerged regarding the process by which companies were crafting their data strategies. Researchers distilled these patterns into four key principles of developing a data strategy, then asked the executives on the data board to provide reactions to each of these four principles. Many data board members responded to the framework to agree or disagree with it or extend it; a subset of respondents have permitted publication of their comments. This article provides the synthesis of the discussion, the principles established by the researchers, and the feedback on the framework as provided by the data board.

Data Maturity Matters

A host of data maturity models propose that organizations mature in data over time across a host of dimensions such as architecture, governance, funding, organizational impact, users, roles and skills, and applications. Therefore, most organizations become great at managing and using data after years of practice.

As organizations mature, their focus moves from establishing data foundations, to learning how to best extract insight from the foundations, to formulating from these insights meaningful and effective actions that create value in new and exciting ways. The data strategy, therefore, needs to evolve to reflect the organization’s current state of maturity.

In Q1 2018, fifteen MIT CISR data board member organizations were just beginning their data journeys. These organizations were introducing data strategies focused on building an enterprise data vision, as well as investing in data foundations such as a central data lake and master data management. Thirty organizations had established data foundations previously—and were now developing strategies to exploit the foundations. These strategies proposed ways to extend data use—such as by talent development, self-service, and data services—and to change the nature of data use, such as by shifting from traditional business intelligence to analytics that enable improvements in customer experience. The seven most mature board organizations were developing data strategies to identify how data might enable new business models. These organizations were entering ecosystems, and driving revenue streams from information-based solutions.

Principles of Data Strategy
Read respondent bios and collected responses
© 2018 MIT Sloan Center for Information Systems Research
CITE THIS ARTICLE: The MIT CISR Data Board, "How to Create a Successful Data Strategy," November 2018.