What Is a Data Governance Strategy?

A data governance strategy is the operating model for data governance for the business. It defines how an organization plans to achieve specific business goals through the strategic use and governance of its data assets. It supports the overall business strategy by mapping data governance to business processes, particularly with regard to governing operations and analytics, as well as the people and teams accountable for governing data.

This is important because in most organizations, data is the biggest source of untapped value. Treating data as an asset helps ensure that data delivers more value to your business and allows you to:

  • Accelerate digital transformation
  • Improve business agility
  • Become more customer-centric
  • Capitalize on new opportunities
  • Focus resources on value creation

The Risk of Doing Nothing

These are exciting, challenging times for everyone in data governance. You’re responsible for making your business more agile, adaptive and intelligent. And that means you’ll need to challenge the status quo, implement new technologies and reimagine critical business processes.

That’s why more and more enterprises of all sizes are embracing the challenge of making data governance an ingrained part of their culture, as with any other business function. You follow best practices when you manage your financial and human resource assets — so why not your data assets?

The trouble is the threat of failure looms large — it’s the constant reminder of the corporate (and career) risk inherent in the undertaking. The data governance procedures you set up will change the way people work across departments and, ultimately, they will underpin your entire data management capability.

But this risk is tiny compared to what your company risks by doing nothing. Neglect to govern your data and you tempt any number of less-than-inviting fates:

  • Legal penalties or fines
  • Brand degradation
  • Inaccuracies in analytics
  • Customer churn
  • Faltering financial performance
  • Supply chain issues
  • Loss of market share

Without the appropriate care, your data can go from being a valuable asset to an expensive liability. Data governance is the tough but necessary job of keeping your data on your side. And the upside is as exciting as the downside is frightening. Get data governance right and you’ll go live with new apps and services faster. You’ll empower lines of business to do great things, such as make faster, better decisions and unify your customer data. In short, when your data is properly governed, the sky’s the limit.

Digital Transformation Is the New Normal

Far more than a buzzword, digital transformation is redefining how we work. Critical capabilities — like data science and analytics, automation and machine learning (ML), data democratization and regulatory compliance — all rely on accurate, reliable, trusted data. And digital transformation must be able to handle today’s challenges: ever-growing volumes of data, new kinds of data users, an evolving regulatory landscape and some extremely complex application & data environments.

This calls for a data governance strategy that is more collaborative, transparent and agile — and it has to do everything at scale. Here are three reasons why your data governance strategy needs to scale:

  1. Digital transformation affects different lines of business. A successful data governance strategy will serve hundreds (or even thousands) of individuals, inside and outside of your business.
  2. The most effective initiatives draw on data from all over the organization. Meaning that all your data needs to be accessible and trusted.
  3. Regulations like the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR) can affect everyone in your organization.

How to Create a Realistic Data Governance Strategy

Choosing the right project is crucial. You’ll need to find a challenge that’s both achievable and capable of generating results that executives care about. If you already have a project in mind, use this section to validate your decision. If you’re still deciding where to start, then this section will help you scope your effort. The right project will have the following three characteristics:

  1. Demonstrable value
  2. If your first project is going to evolve into a long-term program, then it needs to deliver a return on investment in a reasonable timeframe. Part of the challenge here is identifying a project that will yield impressive results, but the bigger challenge is proving value. You want to help the business unit you’re working with, but you’ll also want to capture the imagination of senior management. This means you’ll need to align around metrics that are tied to both tactical objectives and broader strategic goals. For example, if your first project is focused on financial reporting, use metrics that demonstrate the productivity benefits of making analysts more efficient, as well as the strategic value of delivering the trusted finance-related analytics they’re working on.

  3. Ready-made sponsors
  4. Sponsorship is crucially important to the success of a data governance initiative. Meaning you’ll need an executive on your side from the outset. Look for a project with ready-made supporters. For example, if your project’s goal is to enable cross-selling and upselling through improved customer data, then it would make sense to evangelize the benefits of a data governance program with someone like the head of sales or the CMO who will have the most to gain from more reliable customer data. Alternatively, if the goal is to better protect and secure customer data, or support compliance with GDPR, then it might make sense to approach the chief risk officer or chief information security officer.

    The important thing is to have the backing of an executive with a personal interest in seeing you succeed. And don’t just stop at one. The broader the executive support you secure now, the more chance your initiative has of going the distance.

  5. A bowling-pin effect
  6. Ideally, your first project will open doors and create new opportunities for better data governance. For example, by delivering trusted customer data for the finance team, you stand a good chance of expanding this project to serve many shared customer data requirements across marketing, sales and customer service functions.

What Is a Data Governance Roadmap?

Your first data governance project should lead to another, or several others. For example, you might start with GDPR compliance which could pave the way for bigger, enterprise-wide initiatives focused on reporting, analytics, customer experience and security.

Ten elements of a data governance roadmap
Ten elements of a data governance roadmap

Here are a few other factors to consider when you’re mapping out your long-term data governance roadmap.

  • Skills

Are there opportunities for your team to apply the techniques they’ll learn while executing your first project?

  • Funding

Is there an existing, high-profile or big-budget project that you can support? This might be the easiest way to find budget for your initiative. • Compliance Regulations often touch data and processes impacting multiple departments. So, if you’ve already solved a compliance challenge for one line of business, you’re likely helping another. Hint: Often, compliance initiatives are already funded, so they’re a great starting point for data governance programs.

  • Business priorities

Are there any critical challenges or time-sensitive opportunities that will influence the direction of your program? For example, if your chief competitor pivots to become more customer-centric, can you help your company do the same?

  • Toolkit

If you’ve already built technology or models that can be applied to certain types of data, then it might make sense to focus on that data in the short term. For example, if you’ve built a tool that catalogs product data, then perhaps target product, marketing, support and sales teams — the heaviest users of product data — first.

Getting Started with a Data Governance Program

Setting Your Goals

When a business chooses to implement a data governance program it’s usually to support one or more of the following common goals:

  • To improve the efficiency of critical processes that have been hindered by low-quality data
  • To comply with one or more regulations or adapt existing data practices to comply more efficiently and reliably
  • To use accurate and trusted data to inform every decision within or across business units or processes.
  • To improve data security and reduce risk by gaining a greater understanding of where sensitive data lives, how it moves and who has access to it.
  • To capitalize on analytics to capture, verify and certify the data that’s scattered across the business, ensuring that it’s reliable and available for improved analytical insights.

These are all valid reasons to launch a data governance program, but when you’re selling your project within the business, you’ll need to be more specific about what you intend to achieve.

The 3 Best Ways to Change a Data Governance Culture: People, Process and Technology

Every data governance program is made up of a multitude of projects. And every significant business project involves three core elements:

1. People

Data governance programs involve a lot of people. Even when your team is small, the outcome of your project can have an outsize impact on a large number of employees, customers, partners and others who support or depend upon your business.

Ideally, you want to give every one of these people a voice in the data governance conversation. To find the right balance, you can use a framework — like Driver/Approver/Contributor/Informed (DACI) or Responsible/Accountable/Consulted/Informed (RACI) — to assign and communicate one of four crucial roles to every individual who needs to be involved in each step of the initiative.

This role assignment framework ensures the right people are providing input at the right time and that everyone understands their position and responsibilities within your project

The Driver

This is the person who pushes the project forward. They’re responsible for managing stakeholders, assigning resources, building the case, measuring and communicating results and ensuring key decisions are made at the right time. For the purposes of your project, you may fill this role, or you may choose to identify an experienced project manager. While many data governance roles may be “part-time” jobs for those involved, as a best practice the driver should be a full-time, dedicated resource.

The Approver

This person is the one who is ultimately accountable for the outcome of the initiative and will therefore be accountable for all the key decisions and provide the necessary resources for the effort. They’ll also have the power to veto the decisions of other team members. This is likely to be your executive sponsor, who also plays an important role in evangelizing the initiative across the leadership team. As a best practice, aim for a single approver, although certain exceptions may require a second approver.


These are the business and IT subject matter experts who’ll help you provide the necessary context to achieve your goals. They may occupy a minor consultative role or a full-time position during different phases of a project, but either way they’ll provide valuable knowledge and insight that will help you and your team deliver the right recommendations and solutions. For example, if your project is designed to deliver high-quality, trusted finance data, your contributors may include finance managers who can explain how they’re using data to achieve critical outcomes. Your contributors should include dedicated IT and data management experts who support your finance team and will have first-line visibility into quality and reliability issues. You should also include business leaders, process owners and data stewards who run the upstream and downstream processes impacted by your initiative. IT architects, analysts and systems experts should also be on your list.

The Informed

These are the people who’ll be affected by your data governance effort. This group includes the broader community of data consumers who’ll benefit from the improved quality and reliability of data resulting from your initiative. The informed also include stakeholders who may not directly benefit but will be required to change behaviors and processes as a result of your initiative.

This group doesn’t have a say in the direction of your project. You’ll want to make this clear, as many people will want to be involved in key decisions. You’ll likely hear “This impacts me, so I should have a say.” This won’t be a problem so long as your “Approver” signs off on the DACI and you keep everybody in the loop. Then you can focus on a smaller team of contributors. That being said, you will have to brief this group on your progress, decisions made, expected impacts and any changes to policies and processes that may impact their responsibilities. Giving informed stakeholders sufficient time to react to these changes is critical for successful adoption.

This framework works only if everyone is committed to the project and the decisions being made. This means that fostering an open and collaborative culture is crucial for your project to succeed. The DACI is one of the first deliverables for any project. Before moving forward, make sure you’ve secured buy-in and agreement on the DACI roles and responsibilities.

2. Process

In order to scale anything, you need clearly defined, repeatable processes that are designed for the reality of the task ahead.

There are four core processes that support every data governance program. Here we explain what they are and the common challenges that organizations face when they implement them.


This is the process of identifying and understanding the data being governed. It includes mapping the business processes that touch the data, determining the state of the data and identifying the organizational and technical capabilities associated with it. One of the most time-consuming stages of data governance is discovering and profiling data, as it’s often a manual process. Technical specialists must trawl countless data sources in search of relevant data and metadata. If you automate these tasks, you’ll empower data stewards to spend more time on the valuable task of operationalizing policies and processes, and less time poring over spreadsheets.


This is the process of documenting data definitions, policies, standards and processes. It’s also when you assign ownership (a critical, often-overlooked step) and define your key metrics and KPIs. Establishing definitions is important, but so is implementation. Make sure your stewards are equipped to both document definitions, policies and standards and create the processes that will operationalize these rules. For more relevant definitions and rules, consider software that allows subject matter experts on the business side to contribute directly to the process.


This is the process of operationalizing data governance policies, business rules and stewardship. Operationalizing a data governance initiative is a team effort. Almost everyone who handles data has a role to play in its governance and is responsible for following agreed-upon policies. Having the ability to publish these policies can help you communicate these roles and responsibilities and hold people accountable.

Automation also plays a critical role in how data governance policies are operationalized, particularly at enterprise or big-data scale. It’s simple — being able to define and assign ownership of policies ensures accountability. And when these policies can be broken into rules, you can automate data quality measurement and reporting processes.

Measure and monitor

This is the process of measuring the value of your data governance efforts and monitoring compliance with your policies. Monitoring and measuring results can be difficult if your data is scattered across your business. For example, some of your data might be stored in an Excel file while the rest is spread across your data warehouse and a range of applications. Before you start your initiative, ensure that you’ve identified and sourced the data necessary to effectively measure your data governance efforts. Bonus points if you and your sponsor can monitor the success of your program — including business value and outcome measures — in real time.

3. Technology

Data governance initiatives are always evolving. Today, you might be serving 100 people and supporting a single process. Two months from now, your program may impact thousands of stakeholders across a dozen core systems and processes.

Your need to change and evolve means that you need technology that scales. A solution that can deliver value today but adapt and evolve as your requirements change. If the focus of your program shifts from improving data quality for one business unit to complying with GDPR across your organization, then your solution can incorporate a significant amount of new data and users without compromising on speed or effectiveness.

It’s also likely that the core capabilities of your software will need to evolve over time.

For example, if your first project is focused on ensuring the privacy of customer data, then you’ll probably need some kind of data governance console to visualize data lineage, as well as a data security solution to ensure security controls are aligned with governance policies.

If you then broaden your remit to accelerating analytics for sales and marketing, you’ll need a more robust solution that perhaps incorporates data quality tools, data management, data democratization and data cataloging.

Your solution will need to be scalable but also modular and adaptable — capable of supporting a host of data management systems and tools. Here are a few tips on building such a solution.

Consider the cloud

When you’re designing the architecture of your solution you have two choices — monolithic or microservices-based. We recommend that you choose the latter.

Microservices components are built to be modular and integrated, so minimal coding is required to connect them to other applications. This means you can extend the functionality of your solution more quickly and cost-effectively than you could if you were using a traditional, monolithic architecture.

A cloud-based tool with elastic storage and compute power will find it easier to tackle a sudden influx of data and users. You’ll almost certainly need the capability to govern data across hybrid, cloud and applications all from one location.

Connectivity is another factor to bear in mind. If all of your essential apps are connected on day one, then data will flow through your solution more easily. Managing your APIs — understanding the role of each and standardizing how they’re used — will also make it easier for your team to connect new systems in the future.

Automate to accelerate

Your data governance program needs to be efficient and agile if it’s going to adapt to rapidly changing business needs. Keeping pace with the business is relatively easy when your project is small, but it may become an issue as you grow, especially if you’re using manual processes to manage data discovery, cataloging and reporting. Use artificial intelligence (AI) to automate these tasks and ensure your team spends more time on the things that really matter. AI is already helping teams accomplish tasks that used to take months in just days.

Build a metadata repository

It’s far easier to manage, categorize, segment and secure your data if you can access and govern your metadata. For example, let’s say you want to apply access controls to all your customer data. Once you have your metadata in one place, you can segment data bearing “customer” tags and apply specific protections to it. You can even automate this process to avoid the time-consuming task of vetting each of the data entities within the scope of your project. This makes it much easier to scale your project. Once you’ve taught your security system what certain metadata tags mean, then it can protect new data entities automatically as and when they flow into your system.

Nurture collaboration

Data governance isn’t a one-person, or even a one-team job. Everyone in the business needs to take responsibility for the data they use and own. Although technology alone can’t bring your people together, you’ll need to provide stakeholders with a platform to share their knowledge of data lineage, business processes and policies.

Crucially, you need a system that can provide role-relevant experiences for both the business and IT. If a subject matter expert or line of business owner can’t easily use your tool and understand how data governance relates to business processes, then they’re unlikely to adopt it, or evangelize it to the rest of their team. At the same time, IT must be able to connect the dots between what the business wants and their role in implementing the systems and rules to automate and scale data governance policies and processes.

Email or spreadsheet-based systems simply can’t support this type of teamwork. Critical data, documents and files will end up stranded on individual computers or buried on disconnected applications. A centralized data governance console, on the other hand, can help you get everyone on the same page. These tools connect data lineage to business processes, allow you to document policies and align workflows across your business so that everyone is aware of their role in your strategy and how their use of data is aligned with the data governance standards and norms of the business.

Improve data literacy

Democratizing data drives business value. Sharing data internally helps companies deal with existing issues — solving problems, executing transactions or complying with regulations. Building this capability into your organization drives trust and understanding and empowers employees for data-driven results.

How Can I Make Data Governance Scale?

All data governance programs are different, so there’s no such thing as a one-size-fits-all template. However, there are certain actions you can take to build a robust, yet adaptable, technical foundation for effective data governance.

Catalog your data

Every initiative begins with a discovery process. If you’re a data governance specialist, you need to identify the data you’ll be managing and governing. If you’re an analyst, you need to find the data that’ll help you generate new insights. Your data catalog needs to meet both these needs and provide visibility into data wherever it resides — in applications, infrastructure systems, on-premises or in the cloud. It also needs to put data in context. When users can see where data has come from, who’s been using it and how it’s been used in the past, they can make better decisions when they use it. This means you need a system that uses multiple types of metadata to categorize data. Only then can you present users with a holistic, contextual view of data.

Enable collaboration across the business

For every opportunity and challenge your data governance program must address — regarding policies, definitions, rules and data types — there’s someone, somewhere in your organization, who understands the problem and knows how it should be solved for the best possible outcome.

Finding that person and enabling them to share what they know, does more than make your data governance program more effective — it also helps you to secure buy-in. And the more people who understand the value of data governance, the easier it’ll be to scale your program.

A collaboration tool that brings together workflows, policies, definitions and rules will help you create a source of truth about the value, reliability and lineage of your enterprise’s data assets, one that everyone involved can agree on. Look for a solution that will help business stakeholders:

  • Define what success means for the program at a policy, rule and quantitative level
  • Align with their technical counterparts to instrument their data management operations accordingly
  • Assess and monitor attainment and business outcomes on an ongoing basis

Gain agility by aligning your people

When an opportunity arises, you need to react quickly. From a data governance perspective, this means providing the right people with the right information as quickly as possible, so that they can take the right actions. A best-practice decision-making framework, like the DACI approach we discussed above, will help your team work together more efficiently. But you’ll need to support this model by creating a workflow that orchestrates the various components of your data governance program. That way everyone will be working based on a shared understanding of business processes.

Automate with AI

Here are just a few examples of how AI-enabled data governance tools work in practice:

  • Data users working with one data set can be presented with similar data sets that provide context
  • New unstructured data can be onboarded, structured, and categorized automatically
  • Data can be tagged automatically based on the logic learned from previous tagging practices

The capabilities above may seem relatively minor but altogether they add up to significant savings in terms of time and resources, especially when they’re operated at the scale needed to process hundreds of millions of records — or more. And when your team doesn’t have to bother with routine data management tasks, they can spend more time on more meaningful activities.

How Do I Measure Data Governance Success?

Your goal with data governance is to transform your business and make the kind of meaningful impact that delivers measurable ROI. You want to start by identifying the strategic projects where your data governance efforts can achieve critical mass.

And in the short term, that’s what you should measure: you should be able to identify how your program is gaining traction and making progress toward your business goals. In the early stages of this journey, support, sponsorship and buy-in are crucial. You’ll measure your success in terms of adoption by key stakeholders and positive cooperation across departments.

What Does a Good Data Governance Program Look Like?

Your data governance solution will need to give you certain capabilities. It needs to be modular to support you in starting small and demonstrating value, and it needs to scale rapidly to add additional capabilities to serve your entire enterprise. But these are just the fundamentals.

The best solutions will also:

  • Deliver all the capabilities and functionality you need in one modular, yet fully integrated platform.
  • Meet the requirements of both business and technical users, enabling them to collaborate.
  • Utilize artificial intelligence so you can increase productivity even when you’re onboarding ever-increasing amounts of data and tackling new use cases.
  • Enable users to access and manage any type of data from any data source across the entire enterprise — in the cloud, on-premises or anywhere else.

How Bristol Myers Squibb Drives Data Literacy

Bristol Myers Squibb recognizes that data is the lifeblood of their business. They understand the need to keep data healthy and flowing freely around the organization, to accelerate innovation and development and to continue to make groundbreaking advancements in therapeutics.

The health of their data comes back to the ingestion of high-quality data and proactive and reliable data governance. To ensure their users are sufficiently data literate to trust the data and have confidence in its outcomes, Bristol Myers Squibb is combining data governance and data management with data literacy education.

This approach allows users to get more value from their data by removing friction for more agile decision making, taking a pragmatic approach to data risk management, and giving every employee the tools, skills and motivation to treat data as a valued asset.

How Genworth Financial Measures Success

When the data governance team at Genworth Financial began their program to improve the reliability and quality of their data, they quickly learned that if they did not measure, there was no movement. The team knew they would need to create a culture change: one that provided clarity of purpose, defined roles, collaborative policies, consistent communication, data literacy and tools to support their journey. Their current KPIs leverages metadata flags and physical participation in public forums.

The team’s achievements are showcased on the data governance portal, along with access to all their tools and documentation. They also continue to drive engagement and use of data governance tools through their data stewardship forums.

Additional Resources