What Is Data Governance? Definition & Best Practices

What Is Data Governance? Definition & Best Practices

In this comprehensive blog, we’ll tell you everything you need to know about data governance so you can start making the most of your data—today.

I will throw caution to the wind and presume you know just how crucial data is right now.

It’s the key driver of growth for modern businesses, but you can forget about outsmarting your competitors if you don’t manage it well.

It’s that important.

But don’t take my word for it—let’s take a look at the numbers. Thirty years ago, we were still in what I like to refer to as the ‘filing cabinet phase.’

Those days are gone. Since the advent of the internet, the amount of stored data has exploded. In fact, by 2013, 90% of the world’s data had been created in just two years prior.

By 2025, analysts predict that users will create 463 exabytes of data every day —that’s the same amount of information stored on 212,765,957 DVDs.

The data age is here, but what does that mean for businesses?

In a nutshell, you need to up your data governance game.

And here’s all you need to know.

What is Data Governance?

Don’t worry. I won’t make the next part too painful. Data governance is a lot easier to define than you might think.

I’ve done my best to keep this as straightforward as possible, so here we go:

Data governance is the process of organizing, securing, managing, and presenting data using methods and technologies that ensure it remains correct, consistent, and accessible to verified users.

Let’s break that down. Data governance is the process of:

Organizing — identifying all your data sources and getting all your data in one place.

Securing — making sure all your data is compliant with data privacy regulations and internal company policies.

Managing and presenting data — after you’ve nailed down your organization’s data, you need to decide how you present this data to your team.

Using methods and technologies — like modern data governance platforms.

That ensures it remains correct, consistent, and accessible to — like modern data governance platforms. the people in your organization that have the permission to access it, in short — verified users.

To simplify it even further:

Data governance is about improving the quality and trustworthiness of data and easing access to data in line with regulatory compliance.

Data Governance can be described in three core objectives of access, literacy, and quality.

Access includes all of the company’s data so that data is easily discoverable and protected for compliance.

Since everyone has access to the data, they need to understand it, so data literacy is a high priority.

Data quality can be monitored and users report data quality issues which can then be fixed, increasing the data’s trustworthiness in the data’s lifecycle.

At the top-end of the scale, the most cutting-edge modern data governance programs allow for progressive implementation, enabling users to develop data governance programs at their own pace.

There is a fine line between the drivers and benefits of data governance. Drivers are the pain point or trends which trigger an action whereas benefits are some things that produce good and helpful results.

Why is Data Governance important?

“When something is important enough, you do it even if the odds are not in your favor” - Elon Musk.

What makes data governance a do-or-die situation, what are those drivers? Before we look at that, I want to point out the difference between drivers and benefits. It is a fine line. Drivers are the pain points or trends which trigger an action whereas benefits are some things that produce good or helpful results.

Losses due to operational inefficiency

Inefficiencies can cause real problems for business owners. It has a direct negative impact on a company's balance sheet, that is why it is a trigger. A company suffers losses in terms of money, time, work quality, and more.

Growing need for analytics

Analytics is trending. The most successful companies treat data as an asset for analytics. Massive corporations such as Amazon and Google have leveraged data to their advantage and profit. In order to emulate the success of these large companies, data-driven analytics is a high priority.

Compliance with data regulations

Data is created and stored at lightning speeds, and with this stockpile of data comes responsibility. It’s pretty simple. If you’re responsible for any third-party data, you are obligated by law to govern it correctly.

Data Security

While closely related to compliance, data security is ensuring that all data is secure and that there is no unauthorized data access. 

Merger and Acquisition

Merging two company’s data assets can be daunting. Where is all the data stored? How does each define terms? What applications are used, and how can they be consolidated without duplicating data? Data must be consistent and standardized for a smooth transition which is one of the outcomes of data governance. 

Expediting Digital Strategies

Data governance aids an organization by ensuring that the data used to develop digital strategies is high quality, easily accessible, relevant, and timely. These tasks typically fall to IT departments.

How Does Data Governance Benefit Businesses?

In the above section, we saw the pain points which trigger a data governance initiative. But governance also creates immense value rather than just risk reduction. The holistic value comes from data-driven decision-making. Be it finance, sales, marketing, production, procurement, all teams get hugely benefited from data insights.

Single source of reference for all your data needs

The productivity of data engineers and data scientists increases dramatically. The work of weeks for retrieving data can be done in minutes. Data is stored in disparate sources whether in different types of databases or siloed in specific departments. Under data governance, the organized data is searchable from a single source through a tool such as a data catalog.

Improved data access

Granting data access is now in the hands of data owners and stewards and not IT (who are very busy) which eliminates one layer of processing. What took weeks earlier would only take a minute now. All this is done in accordance with all data regulations and policies about confidential data.

Consistent data definitions for a shared understanding of data

Definitions for terms are standardized through a business glossary, decreasing confusion and increasing the accuracy of the analysis. It improves business decision-making.

This is a crucial point. Let's delve into it with an example. A manager at a cheese factory orders milk based on the demand factor calculated from a specific set of data. Now he moves to another plant and orders milk based on the demand factor. It falls short of production needs. Why? because the demand factor for the first plant was calculated from this year’s projected sales, and the other plant calculated it based on the previous year’s sales.

Trustworthy data

Users can use analytics with confidence that the data is trustworthy. Trust comes from transparency and defined processes that come from data governance.

Better data quality through Data Quality Improvement Lifecycle

Data quality issues are reported and fixed through defined policies and procedures. Data quality rules are formed to prevent further data quality issues.

Related: Data Governance and Data Quality: Working Together

Faster analytics with increased accuracy

Since data is searchable, the data necessary for analytics can be found and used faster. Accuracy increases because all relevant data is cataloged and searchable through classification and tagging.

Users can use analytics with confidence that the data is trustworthy. Trust comes from transparency and defined processes that come from data governance.

Quick impact analysis

When a new feature is added, to any application, it is necessary to check how the new feature impacts the performance of the rest of the downstream systems. Using smart tools, IT teams can quickly assess how introducing specific changes will impact KPIs, metrics, workflows, and more. Decision-making improves as business users make important decisions based on these KPIs and metrics.

Data literacy

Now business users can access data, they need the ability to understand it. Data literacy is the process by which an organization puts in place measures to ensure all data users are educated to consume data confidently. A comprehensive data literacy strategy enables companies to avoid mixed messaging and cross-department confusion.

See it in action! Watch this video about implementing data governance from scratch with industry experts.

Best Practices to Ace Your Data Governance Game

While every organization is different, there are some basic best practices to help guide you.

  1. Start with a progressive approach.

    Start small and scale the scope of the data in a governance program gradually. Learn important lessons along the way. As the organization grows in confidence, widen its data scope and source it from other departments and locations.

  2. Build a business case.

    Getting buy-in and sponsorship from leaders who will be part of the process is key when building a data governance practice. For that, build a strong business case by identifying the value drivers. Show the improvements that can be gained, like an increase in revenue, better customer experience, and productivity.

  3. Set clear, measurable goals.

    You cannot control what you cannot measure. When making any change, you should measure the baseline for comparison after.

  4. Make data literacy a priority.

    Educate stakeholders. Wherever possible use business terms and translate the academic parts of the data governance discipline into meaningful content in the business context.

  5. Don’t expect an instant ROI.

    A lot of times, the ROI of a data governance program is not immediately noticeable using metrics such as profit. Even though results take a long time, don’t be discouraged. Governed data is worth it and pays off down the line.

  6. Map infrastructure, architecture, and tools.

    Your data governance framework must be a sensible part of your enterprise architecture, the IT landscape, and the tools needed.

  7. Start with one or two data sources.

    Focus on the most critical data elements first. Allow data governance to grow and spread through the rest as the initiative progresses. Start with the data sources that benefit the business objectives the most.

  8. Always focus on the quality of your data.

    Maintaining high-quality data yields trustworthy analytics. Through tracking lineage and overseeing data through the Data Quality Improvement Lifecycle, data can confidently be used by business users.

  9. Don’t overcomplicate it.

    Most importantly, do not overcomplicate everything. There is no need to create every single data policy and procedure at one. Create contingencies and exceptions as they come. Data governance is supposed to solve your business problems, so don’t make new ones for yourself.

The Progressive Data Governance Framework

A data governance framework is a blueprint that defines the roles, responsibilities, policies, and procedures of the data governance initiative, so everyone in the organization knows the plan and is in agreement.

Traditional governance follows the DAMA framework.

DAMA International has been in the data governance game for over three decades, and they have done some incredible things during that time.

But there is a limitation with their framework of governance. Not only is it prescriptive, but at times it’s intrusive too. That is why we came up with progressive data governance framework.

At the top-end of the scale, the most cutting-edge modern data governance programs allow for progressive implementation, enabling users to develop data governance programs at their own pace.

Three initiatives

There are three initiatives that will help you to set up a successful data governance strategy in your organization. These initiatives are the responsibility of the data governance group.

  1. Create an inventory: The first step is to identify where all of your data is stored. Following the identification process, you must create an inventory of all the data sources including applications like SAP and Salesforce, data warehouses, and other databases too.
  2. Confirm data ownership: The next step is to identify the owners of the data in your organization. However, you need to specify that the owners identified are the business owners and have full control politically & territorially to make decisions on how the data can be and will be used. For example, a sales data stakeholder from France should not own the U.S sales data.
  3. Set up a data governance committee: A data governance committee, made up of data owners, will help oversee data governance company-wide and act as an authority. Ultimately, the data governance committee sets the rules and policies for the data governance initiative. They will receive and review reports regarding new procedures, policies, protocols.
Related: Building an Effective Data Governance Framework

Three core programs

Managed access: Define policies for confidential data and personally identifiable information (PII) then work towards implementing these policies across the organization. These policies will govern who has access to what data, how they access it, and how others can be prevented from doing so.

Data literacy: One of the cornerstones of successful data analytics is data literacy. When your staff is data-literate they can use data to innovate. Granting easy access is the first step towards this goal, the other steps include building awareness of your data governance objectives and standardizing terms using a business glossary.

Collaborating on data assets enables data users to make the best business decisions. To make this possible, users need to be on the same page in regards to data terms and definitions.

Data quality improvement: There is no point in providing users with access to data if it is not of high quality. Bad data can only lead to bad business decisions. The aim of a data quality improvement program is to document data lineage, carry out root cause analysis, and fix any errors that may have occurred in the past.

Data management by IT

One of the core purposes of end-to-end data governance is to ensure that data is secure. Although most of the work conducted in this area is performed by IT staff, a company's data governance initiative must ensure that security standards are followed.

Data security is largely the responsibility of the IT team, which should conduct regular audits to ensure the data is secure and implement encryption methods to defend the data while it travels from location to location.

The three major aspects of IT data management are:

  • Security: Generally, a data access management program covers data security in an organization. However, a data governance program should incorporate measures that ensure that the latest patches are installed and there are no software vulnerabilities present.
  • Encryption: A data governance program should include an encryption policy for data at rest and during transfer. Data at rest must be encrypted so that if a hard drive is stolen, it can't be read by anyone. Data in transfer must be secured at every stage of its journey.
  • Auditing: Data governance groups must ensure that every access management activity is adequately audited. It is essential to create an audit policy for PII and other confidential data too.

Data Governance Maturity Model

Evaluating your organization using a data governance maturity model can be a very useful element in making the roadmap (discussed later) and communicating the next steps of your governance initiative. It also gives context for deploying the data governance framework.

A data governance maturity model is a tool and methodology used to measure your organization's data governance initiatives and communicate them simply to your entire organization.

Some of the known maturity models are from IBM and Gartner but they do not provide an action plan to move towards the next step. With a progressive approach, we enable companies to track the progress of their data governance initiatives by providing a timeline for the process and a definitive framework for measuring maturity levels.

Level 1: Unaware

At this level generally, the data value is not known to organizations, they do not focus at all on data and all the decisions are mostly executive and middle management led.

  • Unaware of the importance of data
  • No action taken
  • Processes are reactive and generally chaotic

Level 2: Aware

At this level, Organizations are aware of the importance of the data and they have started taking some action to manage it.

  • There is awareness of the importance of data.
  • Existing data practices are understood and well documented
  • An inventory of data sources is available.

Level 3: Defined

At this level, most of the Organization's data policies and procedures are well defined.

  • Data governance rules and policies are defined
  • Data owners and data stewards are identified
  • A governance committee is set up
  • A data catalog is installed

Level 4: Implemented

At this level, most of the Organization's data policies and procedures are well defined and implemented.

  • Data governance policies and implementing rules are enforced
  • There is training conducted
  • Data is collected and measured
  • Alerts are set up to monitor data quality issues raised by users

Level 5: Optimized

At this level, most of the Organization's data policies and procedures are well defined and implemented. Organizations collect the data of processes and optimize the process.

  • Rules and policies for better efficiency are optimized
  • Redundancies are reduced with redesigned workflows
  • Data is tagged by users to increase discoverability
Related: Data Governance Maturity Models and How to Measure It?

Data Governance Roadmap

  1. Define your strategy

    The first and most important step is to define your governance strategy in alignment with your business strategy. What is the business problem you are trying to solve and how can data help you get there? It happens very often that companies collect heaps of data and later try to figure out what to do with it.

    Here is an example. Your business strategy is to upsell your existing customers by giving them excellent customer support. So you will need to devise your data strategy - capturing, storage, usage of customer data accordingly.

    But, if your business strategy is to attract new customers through extensive marketing, you will need to define your data strategy - to buy data for prospecting, use it effectively and integrate your entire digital marketing effort for it to run smoothly.

  2. Build a business case

    To get the board or management to approve a project, it is necessary to build a business case that demonstrates why the project is needed and what the benefits of the project will be when it is finalized.

    Download the Business Case Builder here. This Business Case Builder provided by OvalEdge lists in detail the steps to build your own business case and also critical questions you will ask the stakeholders.  Here is a walkthrough video of the builder.  

  3. Assign roles and responsibilities

    The next step is to define the roles and responsibilities for data governance.

  4. Make policies and processes

    Next, it’s important to outline the policies and processes for managing data, and map them to the roles and responsibilities. Clear processes will also be required for data storage, formatting, metadata, security, and compliance.

  5. Monitor and measure

    How will you track your progress? - define your metrics. The data governance maturity model is also one of the yardsticks.

  6. Select a tool

    Once you have a framework in place for measuring and monitoring data governance, you will be able to select the right tools for the job. Not only should the tool solve your business problem, but have room for implementing more data governance as your organization is ready to progress the data governance initiative.

  7. Work with the tool

    Implement the data governance tool and follow the previously decided data governance procedures and policies.

  8. Close the loop

    Check that the data governance initiative continues optimization and stays in line with business objectives.

Related: Roadmap For Successful Data Governance

Common Roles and Responsibilities

Data governance introduces new independent roles or assigns them to the existing data team. The following table goes over the most common roles we see based on our practical experience in many companies and data initiatives:

Role and Responsibility Organizational Assignment
Chief Data Officer will oversee the development and organization of a data governance strategy. They are the key point of contact for both the data team and business executives. A C-Level executive is given this full-time role
Data Governance Manager will oversee the development and organization of various data governance programs. They are the key point of contact for both the data team and business executives. Someone who has previous managerial experience on a data team is given this full-time role
Data Owners are responsible for maintaining specific data assets and making them accessible to other users. A data owner is not just responsible for data, but accountable for it. This part-time role is typically assigned to a senior manager
Business data stewards are responsible for measuring and reporting the data quality, defining guidelines for creating and maintaining the data, and documenting the data in a data catalog/glossary. The data steward's role is organized by data domains (e.g. customer data, vendor data, material/product data). Business analysts and subject matter experts are typically assigned this part-time role
Technical data stewards are responsible for the data model and data lifecycle across IT systems. They provide standardized data element definitions and formats and profiles source system details and data flows between systems. They typically work across domains. Data architects or Data engineers are typically assigned this part-time role

The Data Governance Committee, also known as the steering committee, meets monthly or quarterly, depending on your company's requirements.

The aim of the committee is to decide on data policies and standards, manage budgets, determine business terms, and more.

These are business and IT stakeholders, often also data owners


Related: How Chief Data Officers overcome three key challenges they face

Use of a data governance tool

Data governance tools are essential to ensure effective data governance, while also navigating evolving compliance standards and requirements. As per the roadmap you have identified your needs and found a tool to meet them. This tool should support the majority of your data sources and your top data governance initiatives. A tool comes in very handy to do certain foundational tasks.


Standardizing data is a crucial step to ensuring compliance. When you standardize data, it is easier to track and compare. This can be done using a business glossary. A business glossary is a list of data-related terms and definitions and displays them clearly and logically so everyone in an organization can access them.


Once standardized, data is easier to identify, enabling organizations to classify and tag it. Understanding data is vital if you want to ensure compliance.


Data lineage refers to the lifecycle of data—where it comes from and where it has been.

From a compliance perspective, documenting data lineage enables organizations to achieve many objectives, including more efficient regulatory reporting, improved data governance through access to historical data, and the ability to expose any discrepancies or potential security threats.


Metadata provides context to information, enabling users to work with it more effectively.

You must fully understand the data you are using to get the most out of it. That’s why in modern data governance, one of the most important drivers of efficient data analysis is managing this metadata.

When managed correctly, metadata makes every aspect of modern data governance more effective. It provides accurate information to calculate impact analysis.


Data discovery processes ensure that an organization’s data is easy to find, access, and understand regardless of where it is stored. The best way to achieve this is through a data discovery platform, but how do provisions like these improve efficiency?

Generally, data is stored in multiple locations with countless different admittance measures in place in an organization. With data discovery systems in operation, data is easy to locate because it is searchable.

This process slashes the time it takes to find and understand a particular data set. And, when data is discoverable, it can be collaborated on. Data and IT teams can work together to use the data available to them to develop data-driven growth strategies.


Impact analysis, in this context, concerns the processes IT and data teams undertake to determine the impact of data management decisions downstream.

Impact analysis enables these teams to work more efficiently before rolling out a significant data management protocol because they can systematically weigh up the pros and cons of any imminent decision.

The first step of impact analysis is to do a business assessment. Using smart tools, both data and IT teams can quickly assess how introducing specific changes will impact profits, workflow, and more.

Calculating the ROI of a Data Governance Program

The ROI of a data governance program is value-driven, always use-case specific, and not intrinsically tied up with tangible profits—at least not in every circumstance. So, to calculate the ROI, you have to look at the governance program as a whole.

We will explore three areas that benefited from data governance to calculate ROI: efficiency, compliance, and data-driven decisions.

Operational Efficiency

It’s straightforward to calculate the ROI in regards to improved efficiency because you’ll quickly learn how much time you’re saving your data teams, and, of course—time is money.

Self-service has a significant impact on ROI. When users gain access to platforms that make it easy for them to find and use data independently, the economic impact can be huge.

One report by Forrester included analysis from seven companies that had used a modern data governance tool. Over three years, it found that:The total ROI was 364%

  • ROI in regards to time saved was $2.7m
  • Business user productivity gains totaled $584,182
  • Savings from faster analyst onboarding totaled $268,085


It is challenging to calculate an exact ROI from a compliance perspective, but it’s easy to work out the savings you could make by not falling foul of regulatory guidelines. Let’s look at a few examples.

There are lots of data protection laws but let’s look at the ones with enormous fines attached:

  • GDPR: Maximum fine of €20 million ($24 million) or 4% of annual global turnover
  • California Consumer Privacy Act: Civil penalties of up to $7,500 per violation
  • DIFC Data Protection Law: $20,000 to $100,000

And these aren’t just empty threats. The worst rule-breakers of the EU’s GDPR got hit with the following penalties:

  • British Airways – 204.6m Euros
  • Marriott International Hotels – 110.3m Euros
  • Google Inc. – 50m Euros
Related: Data Privacy Compliance: How to Ensure it and How it Can Benefit Your Business

Data-Driven Decisions

The most challenging ROI to calculate surrounds data-driven innovation because it is both company-specific and slow to mature.

However, you can split this ROI into two—benefits to business leaders and business users.

As we mentioned earlier in this blog when business leaders make decisions backed by trusted insight, it leads to better outcomes. and this directly affects a company's top or bottom lines.

With business users, even when there is a trusted data delivery platform in place and all the data required to innovate is at a user’s fingertips, it’s difficult to predict when and how innovation will happen.

Over time, teams will build more use cases with the technology available to them. Eventually, there will come a pivot point where this new use case, say a recommendation engine, for example, is rolled out.

Even then, you need to have people use the technology first to find out how popular it is and what the ROI will be. The winning tool should support most, if not all of your data sources and enable you to realize your key goals—within budget!

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