Creating a Data Governance Strategy Mapped to Outcomes

Updated September 14th, 2023
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A solid data governance strategy - one that you can implement successfully across your organization - is critical for increasing compliance and improving organizational data quality. Sadly, not every data governance strategy is created equal.

This article will outline creating a data governance strategy that’s value-driven, measurable, and directly linked to tangible business outcomes.

Want to make data governance a business priority? We can help you craft a plan that’s too good to ignore! 👉 Talk to us

Table of Contents

  1. Data governance strategy: 7 steps to get started
  2. Data governance strategy: What should be your guiding principle?
  3. Data governance strategy in practice: Examples
  4. Conclusion
  5. Data governance strategy: Related reads

Data governance strategy: 7 steps to get started

Here are the steps to defining a data governance strategy that achieves results:

  1. Schedule a meeting with key stakeholders and identify pain points.
  2. Create value stream maps and craft a vision statement.
  3. Create a data maturity model specific to your organization.
  4. Divide your challenges into smaller steps.
  5. Select and prepare deployment of a data catalog.
  6. Get buy-in from decision-makers, secure a budget, and identify the teams involved.
  7. Launch and track progress towards your first KPI.

Let’s look at each of these in detail.

Schedule a meeting with key stakeholders and identify pain points


A data governance strategy can have numerous goals. These include improving business agility, sharing knowledge, improving trust in data, and improving security & compliance. You and your stakeholders need to agree on:

  • Which of these goals are the highest priority?
  • How will you achieve them?
  • How do you measure that we’ve obtained them?

To get started, convene a meeting with everyone who needs to be involved in the data governance strategy creation process. This includes executive leaders, operational teams, data owners, and business & data analytics practitioners.

Create value stream maps and craft a vision statement


There should be two outcomes from this work. The first is a set of value stream mappings. These describe, in detail, what processes you will use to deliver value to your customers. Use these mappings to identify your data value streams, how you’ll implement them, and whether there’s waste you can eliminate.

The second is a vision statement. The vision statement is a succinct summary of your organization’s data governance strategy and goals in a single, short sentence.

Use the value stream mappings and vision statements as your North Stars to keep everyone aligned and working towards the same goals.

Create a data maturity model specific to your organization


To know where you’re going, you need to know where you are and to have a map of your destination.

A data maturity model provides that roadmap by defining milestones, action plans, and expected results for each subsequent step in your organization’s data governance strategy. By breaking your data governance strategy into stages, you improve organizational focus and the program’s chances of success.

To create a data maturity model, start at your current level of data maturity and declare it Level 1. Then, create a Level 5 that contains your end goals. This will include long-term goals such as “everyone in the organization uses our data catalog as a single source of truth,” or “decision makers trust and can easily discover our data.”

From there, fill in the intermediate steps you’ll take from Level 1 to Level 5. For example, initial steps at Level 2 might include defining KPIs and implementing tools such as a data catalog.

Data governance strategy: maturity model template


For each step of your data maturity model, define the following five items:

Data governance strategy maturity model template

Data governance strategy maturity model template - Image by Atlan.

You can lay out your maturity model template as shown in this image, capturing the milestones, action plan, scope, risks, and expected results for each stage.

  • Milestones. The concrete goals for this step.
  • Action Plan. A list of tasks for putting these goals into action. For example, this might involve setting up a data catalog, or implementing data quality validation automation tools.
  • Scope. The departments and areas of the company covered by the current change. Remember, start small! Aim to onboard one team or group in your earlier phases. You can increase the rollout radius in later phases by vetting your earlier decisions and learning valuable lessons.
  • Risks. What could go wrong along the way. For example, if you’re installing a data catalog, incompatibilities with the tools on your data stack could hold up implementation. Identify all possible risks and develop action plans to address them should they arise.
  • Expected Results. The concrete metrics you’re aiming to achieve. This should be stated as a measurable goal - “Finance team’s data sources fully onboarded to data catalog,” “50% of corporate data properly classified with sensitivity tagging,” etc. These serve as gates to validate that you’re ready to enter the next phase.

Divide your challenges into smaller steps


According to data from McKinsey, 70% of all data transformation projects fail.

There are many reasons for this. But one key reason is that organizations try and do too much at once.

A data maturity model helps break down your data governance journey into smaller, achievable goals. You’ll further maximize your chances for success if you define your initiatives as smaller, obtainable projects. For example:

  • Work on onboarding a single team to the new governance strategy framework first instead of rolling it out to the entire company in one fell swoop.
  • Use Agile planning techniques, such as breaking work down into two-week sprints, to keep work cycles small and encourage “quick wins.”
  • Focus on creating MVPs (Minimum Viable Products) that you iterate on over several sprints instead of finishing in a single development cycle. You’ll gain fresh insights with each iteration - and will give yourself the flexibility to change direction as business requirements shift.

Select and prepare deployment of a data catalog


According to a study by Matillion and IDG, the average organization has 400 data sources. 20 percent are drawing from more than 1,000 sources. The democratization of data due to the cloud means that the number is only likely to increase heading into the future.

This makes a data catalog the indispensable centerpiece of any data governance strategy. A data catalog serves as an organization’s single source of truth, enabling users to register, discover, and analyze data no matter where it lives. It also serves as the centerpiece of other data governance efforts, such as data quality, security, and compliance.

Get buy-in from decision-makers, secure a budget, and identify the teams involved


Once you’ve laid the foundation, it’s time to put your game plan into practice. Convene your stakeholders again and review your roadmap. Explain your data maturity model, identify each step along the way, and be clear on how you plan to measure success at each juncture.

At this point, your goals should align fully with all stakeholders’ operational goals and business objectives. For example, suppose one business goal is to increase the velocity of product releases for the coming year. Your roadmap should show how improvements in data governance will decrease the time-to-market for data-driven products - e.g., by making data easier to find and reducing defects in data pipelines.

Launch and track progress towards your first KPI


Once you’ve secured a budget and identified the appropriate teams, it’s time to launch. Choose your first KPI and track progress towards completion.

Along the way, measure the adoption of your overall data governance strategy and related tools, particularly your data catalog. Track metrics such as data catalog Weekly and Monthly Active Users, number of data sources onboarded, changes in data quality, features used, and related metrics.

Data governance strategy: What should be your guiding principle?

A detailed plan is a necessary but not sufficient condition for enacting a successful data governance strategy. It also requires cultural change. Unfortunately, in the case of data governance, that’s often easier said than done.

“Data governance” has earned a bad reputation. Originally imposed by edict from the top down, employees saw it as red tape that slowed down their work.

It doesn’t have to be that way. A good data governance strategy can help employees do their work faster by providing a framework for finding data, trusting data, and ensuring compliance with all applicable regulations.

By contrast, an organization with poor data governance can’t move as quickly. It doesn’t know what data it has, who owns it, when it was last updated, or whether it’s even accurate. As Tristan Handy of dbt put it in an iconic post, “Without good governance, more data == more chaos == less trust.”

To enact a successful data governance strategy, you should adopt the principle that data governance is about collaboration and data democratization.

To achieve this, your organization has to shift the way it thinks about data governance and data governance strategy:

  • From “data governance” to “data and analytics” governance. Data governance should be about more than tables; it should cover every asset in your data estate.
  • From centralized governance to decentralized, community-led governance. There’s too much data to control everything from the top down. Building trust in data requires involvement from the entire community of data engineers, analytics engineers, data stewards, IT professionals, product & program managers, decision makers, and business analysts who all have a stake in possessing accurate, high-quality data.
  • From an afterthought to embedded in daily workflows.

These ideas are critical for the success of any data governance strategy. To dive deeper into them, read about how these three principles provide the foundation for a modern data culture.

Data governance strategy in practice: Examples

Nasdaq: A data-driven company creates a data-first culture


The complexity of Nasdaq’s data platform and its legacy data tools meant that users spent 75% of their time every week understanding the context of data. To solve this, it created a comprehensive data governance strategy using Atlan for data discovery and metadata management.

The company’s newly democratized data model made it easier to onboard new teams and freed up the data organization to focus on larger issues. And the introduction of Atlan eliminated data silos, giving Nasdaq what one user referred to as “Google for our data.”

Autodesk goes decentralized with data mesh


Engineering and design software firm Autodesk found itself overwhelmed with a growing volume of data. Unable to scale with its old, monolithic approach, it defined a data governance strategy that moved the company to a data mesh architecture.

Autodesk now has 60 teams that own their own decentralized data sets and data pipelines. Using Atlan, the company can enable users to find data no matter where it resides, while also validating and enforcing compliance company-wide.

Conclusion

A successful data governance strategy requires a well-thought-out plan. But it also requires changing how everyone in the organization thinks about data governance. By combining a detailed strategy with the three principles of modern data governance, you can put your company on a solid path toward building greater trust in its data.

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