What Is Data Governance? The Ultimate Guide

What Is Data Governance? The Ultimate Guide

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.

If you don’t, you really should.

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.

Seriously. 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 how to do it.

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:

Q:What is data governance?
A: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— the people in your organization that have the permission to access it.
  • In short— verified users.

Why is data governance important, and how does it benefit businesses?

Data is being 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.

Compliance is one of the critical drivers of data governance, but there are others.

Another significant catalyst is big data management. It’s easy to mix up data governance and data management, but the two terms are different.

Whereas governing big data refers to introducing company-wide policies and processes, management involves enacting them on a day-to-day basis.

Another important driver is customer satisfaction. Tedious link? Okay, it sounds like it, but it becomes a lot clearer when you drill a little deeper.

When you govern data efficiently, it’s much easier to share it. If a customer requests a data set—that could be anything from PII or performance data on a particular stock or asset—the quicker they can get access to it, the more satisfied they’ll likely be.

Even the fact that a business can share this information at all is a benefit.

The final key driver we'll cover in this section is decision-making. Critical business decision are better when you make them using quality, governed data.

And this translates directly into a business benefit because efficient decision-making practices lead to growth.

When an organization has access to governed data, it’s far easier to make better judgment calls. With qualified data, businesses can determine what has worked in the past, what hasn’t, and everything in between.

When a business user is aware of the data that exists within their company, regardless of whether it originates from their department, they can:
  • Ask better questions based on the data
  • Find better answers using the data
  • Develop more targeted solutions for growth

The Data Governance Framework

Traditional data governance model are in-depth and give data governance managers massive control over data sets. However, you have to be a data expert to get the most out of them. They aren't straightforward. They're suitably academic!

If you want to implement these models company-wide and manage them on an ongoing basis, you’re going to have a lot of work on your hands.

It’s necessary to implement traditional strategies over multiple systems and tools, and, by design, they focus on one primary driver: compliance. Consequently, these strategies don’t help much with data literacy, the most significant factor in widespread data use.

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 problem with their framework of governance. Not only is it prescriptive, but at times it’s intrusive too. Let’s go ahead and dissect the terminology described in the framework—piece by piece.

Data Architecture

In simple terms, data architecture is about identifying the data needs of an enterprise and designing and maintaining the master blueprints required to meet those needs.

These master blueprints enable users to manage data integration, control data assets and align data investments with business strategies.

You require a data architecture group to:

  • Classify data processing and storage demands
  • Meet business needs in the long and short-term using bespoke plans and structures
  • Ensure strategies are in place that enables organizations to adopt new technologies quickly
  • Confirm the state of data management within an organization
  • Implement an official business lexicon for a company’s data and data elements
  • Integrate data architecture with the existing enterprise architecture roadmap
  • Specify important data needs
  • Develop advanced unified designs to meet these needs

From a governance perspective, data architects are responsible for:

  • Maintaining and implementing standards
  • Managing architecture schemes
  • Overseeing data governance projects

Data Modeling and Design

Data modeling and design processes are directly comparable to data architecture. However, where data architecture processes provide an overview of a company’s data management requirements, data modeling and design are secondary.

Essentially, data modeling and design involve the production of graphs, diagrams, and other documentation - physical, logical, or conceptual - that demonstrate and communicate a company's data assets.

Data governance duties:

  • Devise data modeling criteria and enforce them company-wide
  • Preserve the integrity of database designs and data models
  • Keep control over versions
  • Guarantee data models are available to any user that might need to access them

Data Storage and Operation

Data storage and operation are about maximizing data's value through optimal design, implementation, and support

Most organizations have various databases (SQL, No-SQL, data lakes, etc.), maintenance systems, backups, encryption protocols, and other activities.

Teams responsible for data storage and operation must:

  • Make sure data is available throughout its lifecycle
  • Secure the integrity of data assets
  • Manage the performance of data transactions

From a data governance perspective, the following should be accessible:

  • Numerous performance metrics such as query performance and transaction frequency
  • Services metrics such as resolution times and KPIs
  • Various storage metrics including transactional statistics, capacity metrics, the number of requests, the number of databases, and improvement services
  • Information asset tracking to confirm organizations meet license requirements and recognize ownership costs

Appraising stored data using fixed acceptance standards to ascertain its quality is called data auditing and validation.

Data Security

Once an organization decides on its data storage methods, the challenge is to ensure regardingt remains secure. When data is stored on-prem, it’s down to dedicated IT professionals to develop security systems that prevent third-party access or alteration.

But this challenge doesn’t end with external threats. Data security protocols should also prevent unauthorized users within an organization from accessing or manipulating prohibited data sets.

Data security goals:

  • Enable appropriate access to enterprise data assets
  • Prevent inappropriate access to enterprise data assets
  • Understand and comply with all relevant regulations and policies for privacy, protection, and confidentiality
  • Ensure that the privacy and confidentiality needs of all stakeholders are enforced and audited

IT security teams use various tools and techniques like encryption, antivirus software, malware attack prevention, and more to achieve these goals.

Data Integration and Interoperability

Data integration is the process of funneling data between data stores, applications, and organizations. Data integration is the most common process a business needs to initiate to build any data solution. Data engineers are the most sought role.

Data engineers are usually responsible for creating and managing these data pipelines.

Data integration goals:

  • Present data securely, with regulatory compliance, in the suitable format, and within the time frame required
  • Lower the cost and reduce the complexity of managing solutions by developing shared models and interfaces
  • Identify meaningful events and automatically trigger alerts and actions
  • Support BI, analytics, master data management, and operational efficiency efforts

Data integration requirements:

  • Data sharing agreements: A data-sharing agreement—or memorandum of understanding (MOU)—specifies the responsibilities and acceptable use of data to be exchanged
  • Data lineage: Data lineage—tracking data flow from one system to another—is vital for data governance. Without it, you can’t conduct any impact analysis after making changes
  • Metrics of data integration: Metrics, including availability, usage, volume, cost, and speed, are required to measure the scale and benefits of a data integration solution

Document and Content Management

Data exists in many formats. It could be a PDF, text file, JPG, or one of many other document types. Several steps must be followed. They include organizing and categorizing data, developing storage solutions, implementing workflow protocols, editing the data, publishing, and archiving.

Unstructured data requires governance, and here’s why:

  • Legal and regulatory compliance
  • Defensible disposition of records
  • Maintaining the security of sensitive information

Reference and Master Data

Although similar, reference and master data are two separate things.

Master data is the core data within an organization and could be customer data, data referring to investory or stock, primary analytical data, or something similar. Master data is characterized by how it is stored (on multiple systems) and shared (by numerous organization members).
On the other hand, refrence data is the set of values used to structure this master data with a focus on shared or common indecators.

For example, a trader may well be aware of the tickers representing each stock in the global stock market even if they don’t possess any other detailed information about the stock itself.

All of the following are the Master Data Management (MDM) activities:

  • Scrutinize your company to determine MDM drivers and demands
  • Appraise and evaluate all of your data sources
  • Determine and devise a data architecture strategy
  • Design master data properties and establish company-wide definitions and subject fields
  • Develop maintenance protocols
  • Institute governance procedures

Data Warehousing and Business Intelligence

Business intelligence (BI) refers to organization's strategies and technologis to analyze business-critical data, while data wherehousing is pivotal component of BI.
A data warehouse can contain all of company data, current and historica, from numerous sources inputted by multiple users. From here, data analysts are theoritically able to access any data they need to make vital business decision.

Traditionally, IT teams used an Extract, Transform, and Load process (ETL) to upload and store data in a data warehouse. This way, data is moved in batches and on daily schedules.

But there’s a limit to how much data you can move at once, so in a traditional data governance model, data warehouses often require updating. This method also requires a lot of resources, including CPU, memory, and bandwidth.

BI groups can enable business acceptance by:

  • Scrutinize your company to determine MDM drivers and demands
  • Documenting data models
  • Determine and devise a data architecture strategy
  • Ensuring a data quality feedback loop
  • Completing end-to-end metadata
  • Providing verifiable data lineage

Key objectives include:

  • Customer user satisfaction
  • Documenting data models
  • Defined Service Level Agreements (SLAs)
  • A reporting strategy for the entire data landscape


Metadata is data in the fine print—the information used to find and categorize information. As well as making data discoverable, you can use metadata to find common relationships between data sets too.

Metadata is intrinsically linked to data quality because the information contained within it gives data provenance. But without a system in place that automatically analyzes metadata and uses it to categorize and qualify this provenance, it’s impossible to get the most from metadata on a large scale.

Metadata management objectives:

  • Establish standard business terms and develop a common business glossary
  • Collect metadata from all available data sources
  • Provide a standard way for business users to access metadata
  • Ensure the quality of metadata

The governance team must establish metadata standards and guidelines.

Data Governance Business Glossary

Employees in every organization will inevitably use different terms to describe the same thing. From a data governance perspective this can be very challenging.

That’s why a business glossary is so important. Using metadata, as mentioned above, it presents users with clear definitions and standardizes internal vocabulary.

Business glossary objectives:

  • Enable users to understand critical business terms
  • Align business users with technical users
  • Provide a method for companies to establish internal vocabulary

Data Quality

Good data quality improves the overall usage of data and makes data-driven decisions a reality. Consequently, quality data is one of the primary objectives of a data governance program.

Data quality team objectives:

    • Develop a governed approach to make data fit for consumer’s needs
    • Define standards and requirements so to achieve data quality
    • Define and implement processes and procedures to measure data quality

Identify and champion data quality improvement practices through various process improvements

Data Governance Maturity Model

Traditionally, data governance programs were so expensive that stakeholders needed a clear justification for the investment.

The return on investment (ROI) of a traditional data governance program is pretty hard to calculate (we’ll get to calculating the ROI on modern examples later), so the maturity model was developed to better communicate the process with sponsors and stakeholders.

So what does the maturity model look like? Here’s IBM’s version:


Level 1: Initial
There is no awareness, lots of silos, and no governance program in place.

Level 2: Managed
An organization begins to realize the importance of data and how it can benefit from it. Companies start seeing data as an asset.

Level 3: Defined
Data regulation and management guidelines are better defined and more widely implemented. Integration with existing company processes has started, while regulatory rules are refined and made less ambiguous. Technology is used in a more efficient way to manage data.

Level 4: Quantitatively Managed
At this stage, all projects follow the data governance guidelines and principles, while data models are documented and made available throughout the organization. Assessable quality goals are set for each project, data process, and maintenance task.

Level 5: Optimizing
There is a reduction in the cost of data management, and data becomes easier to administer. Operations are streamlined and easier to navigate. Data governance becomes an enterprise-wide effort that improves productivity and efficiency.

Data Governance Best Practices

As we covered (in great detail) earlier on in this blog post, traditional data governance deals with multiple departments and various functions.

Trying to align departments with data sources, not to mention one another, is a massive headache. Traditional data governance approaches don’t provide an easy way to measure the success of a data governance program. So, it’s often difficult to justify the investment.

Although incredibly complex, traditional data governance is outdated. Today, it cannot achieve the efficiency, cost-effectiveness, and simplicity of modern data governance tools.

What’s required is a centralized, value-driven platform that’s easy to implement and manage.

But what does that look like?

Today, data governance is defined by the level of value it can bring to an organization—especially when quality data is the foundation of this value.

In the early days of data governance, there was a great deal of focus on developing specific data architecture. Now, using modern data governance technologies, architecture diagrams are automatically built using raw data.

Modern data governance has three core objectives:



To advance data-driven decision making in an organization through trusted insights.


To ensure data compliance across various data privacy laws and internal data policies.


To improve the efficiency and productivity of IT and data teams.

Data Governance Roadmap

Data-Driven Decision Making

It’s pretty simple. You can encourage data-driven innovation in a company and make better business decisions when data is:

  1. presented and managed correctly
  2. converted to trusted insights

But don’t get ahead of yourself. Before an organization can innovate with its data, there are a few extra steps to take first.


Data literacy is the process by which an organization puts in place measures to ensure all data users within that organization receive education that enables them to consume data confidently.

A comprehensive data literacy strategy enables companies to avoid mixed messaging and cross-department confusion.


To build a culture where users can utilize data effectively, the way custodians distribute, store, and manage the data must be transparent.

And transparency leads to trust.

But transparency doesn’t mean making all data in an organization available to everyone—that would make your data almost impossible to govern correctly!

Instead, it’s vital that a company clearly states where and what its data is, where it’s coming from, who is using it, who owns it, and whom to contact if you need access to it.


Once you have a data-literate staff, transparent data sets, and a culture of trust in that data, the next step is to make it accessible. Without access to the data they need to develop new concepts and approaches, users can’t innovate.

Ideally, all users will have equal access to the data they require—as long as no restrictions are in place to protect PII or other information. You can achieve this through smart cataloging and classification.


Self-service analysis happens when business users develop into business analysts. After giving users access to data, they can train, experiment, and innovate. Eventually, these users will transform into analysts using the data available to make better business decisions.



The more methods you put in place to streamline the data governance process, and track KPIs, the better the data’s quality becomes.

Organizations need to make a concerted effort to improve their data quality to get the best from it. Once a data-literate staff can access and analyze this data, they can determine the specific KPIs required to track its performance.


Data Compliance

Compliance is a driving force behind data governance practices today. There are three key areas to consider if you wish to address compliance issues in modern data governance.


Standardizing data is a crucial step to ensuring compliance. When you standardize data, it is easier to track and compare.



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’s 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.

More Efficient IT and Data Teams

Chief Information Officers (CIO) and Chief Data Officers (CDO) are expected to do more with fewer resources. There is a powerful drive to transform existing data models into modern systems, but several fundamental processes are required to achieve this.


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.


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.

Roles and responsibilities

Now you know what a modern data governance model is, it’s a good time to talk about who uses it.


Chief Data Officer:
In charge of the entire data governance initiative.
Data Owner:
Owns a section of an organization’s data and is responsible for maintaining its quality and accessibility.
Data Steward:
Builds new applications and enhances existing ones.
Chief Compliance Officer:
Maintains data compliance with an organization.


Business User:
Produces and consumes data through well-defined products.
Business Executive:
Develops new business ideas and makes various business decisions based on the data available to them.
Business Analyst:
Helps executives produce data in their desired format.


Database Administrator:
Maintains databases and database security.
System Administrator:
Maintains various applications and technical infrastructure.
Builds new applications and enhance existing ones.


Data Analyst:
Analyzes data so that business users easily consume it.
Data Engineer:
Moves and transforms data. Migrate it over from one application to a data warehouse or data lake.
Data Scientist:
Builds predictive models using various statistical and machine learning techniques.
VP Analytics:
Leads the BI group to provide strategy, budget, and execution.

The progressive approach

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.

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.

using the three pillars of modern data governance—data-driven decisions, compliance, and efficiency—I can explain the ROI of a modern data governance strategy.


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 regarding 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.

There are lots of data protection laws but let’s look at the ones with the biggest 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 for 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.

DataI-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 mega-blog when business leaders make decisions backed by trusted insights, it leads to better outcomes. And THIS directly affects a company’s top or bottom line.

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.

Data Governance Tools

The first step in your data governance journey is finding the best governance tool for the job.

You’ll need to get a little introspective and figure out what you want to get out of your data governance program. Find out what you need and go with a tool that meets these expectations.

The winning tool should support most, if not all of your data sources and enable you to realize your key goals—within budget!

20 foolproof ways to ace your data governance game

  1. Follow the three Cs: Catalog, Collaborate, and Comply.
  2. Take it at your own pace.
  3. If you don’t have a data-literate staff, make data literacy your number one priority.
  4. Get a data governance manager.
  5. Nail down those definitions.
  6. Choose a modern data governance tool.
  7. Research what your competitors are doing.
  8. Talk to your team.
  9. Don’t be afraid to discuss failures.
  10. Allow your team to experiment with the data at their disposal.
  11. Set clear goals.
  12. Don’t over complicate things.
  13. Start with one or two carefully selected data sources.
  14. Don’t expect an instant ROI.
  15. Encourage collaborative practices.
  16. Always focus on the quality of your data.
  17. Trust your data governance team.
  18. Focus on the most critical data elements first.
  19. Ensure everyone in your organization is clear about the program’s direction and purpose.
  20. Use limited, quality KPIs.

Find your edge now. See how OvalEdge works.