How to Create a Data Governance Strategy

As data continues to grow in volume and frequency, it’s vital that organizations have well-documented, scalable digital strategies. That’s because a comprehensive plan ensures that specific efforts by data teams and stakeholders have a consistent approach and purpose. In this blog post, we’ll discuss the elements of a smart data governance strategy and how you can create and maintain one across your organization.

What is a data governance strategy and how do I create one?

A data strategy is an articulated, holistic approach to data governance and management for an organization. It uses standards, processes, and documentation for collecting and managing data to ensure integrity (i.e. data is clean, accurate, and usable). And much like a business strategy, a data strategy should ultimately be in service of delivering value.

When creating your own strategy, consider these core questions that will anchor the purpose for your plan:

  • What sort of data is accessible by the organization?
  • Why is this data important and in what way could it be leveraged?
  • Who are the core audiences for our data and why?
  • Where will data be stored and for how long?
  • When is data collected, processed, and retired?
  • How should data be delivered and used in our processes?

These questions will help form a framework that can be extended with more detail, and further informed by cross-cutting concerns—such as guiding principles and alignment with organizational values and practices.

With these inputs gathered, the strategy itself will often take the form of a single document, or set of documents, which pull together these various aspects into a cohesive structure. One example sequence that could be used to structure your data plan is as follows:

  1. Core purpose and long-term vision
  2. Guiding principles
  3. Scope of strategy
  4. Current goals
  5. Architecture
  6. Key capabilities
  7. Data management concerns
  8. Policies and practices

This can then serve as an explicit agreement between data teams and stakeholders for how data efforts should be prioritized and executed. As in, the effort should clearly align with the purpose, make progress toward the vision, and be executed according to the guiding principles and policies.

What kind of data is usually covered?

A data strategy is meant to be wide-reaching in scope—ideally encompassing the purpose, principles, and policies for all data being processed at an organization. The specific data will depend of course on the domain, but could apply to everything from sales results to production metrics through to customer service scores and skill assessments.

A typical split of a data strategy is between the data used internally at the organization and the data presented externally to customers or investors. These two categories may entail different enough approaches to necessitate separate treatment.

Why is it important to have a data governance strategy?

Consider why organizations put considerable effort into articulating their business strategies—complete with core values, mission statements, vision, guiding principles, as well as specific near-term and long-term goals.

Defining an organization’s identity in the form of vision, principles, and values promotes a common approach to improve decision making and coordinated activity. Articulating core competencies and goals helps create an overarching influence on how work is selected and prioritized. As such, it encourages those in the organization to push forward in the same direction, and with a common approach. Ultimately, that saves the organization a lot of time and money that would otherwise be wasted on disparate goals or lengthy disagreements about the purpose of those efforts.

And these same ideas apply to a data strategy. A thorough guide helps guarantee that efforts by data teams and stakeholders have a consistent approach and purpose.

Are there any broadly adopted or standard data strategy models?

There is far less precedent for data governance strategies, as there are other types of strategy documents. However, as an active problem, there are a number of resources online to help you sort out your own structure.

Who manages data strategies?

In a large organization with dedicated data teams, the strategy will likely be addressed by a Chief Data Officer or VP of Data. In smaller organizations where data efforts are primarily owned by analysts or developers, the strategy will be in the purview of the associated lead, such as the Principal Data Analyst, Chief Information Officer, or Chief Technology Officer.

Because the point of a data governance strategy is to promote consistency across teams, it is crucial that the strategy is developed and promoted via these types of high-level positions.

Do different departments or regions need different strategies?

While the guiding principles and vision for the data strategy should apply on an organization-wide basis, specific policies and practices need to fit the unique constraints present in certain domains or regions.

For example, regulatory requirements such as GDPR may apply specifically to certain teams or offices. Certain domains of data handled by the organization might have an increased level of sensitivity or risk associated with them, such as personally identifiable customer data.

Up Next: Designing a Data Strategy Based on the DoD Model

In the next blog post we’ll take a closer look at how the Department of Defense recently updated their data strategy and how you can apply their techniques to your own strategy.

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