Enterprise Data Governance Guide Process Benefits Best Practices

Enterprise Data Governance Guide: Process, Benefits, & Best Practices


Table Of Contents

The accuracy of the data that is served to an organization’s internal and external stakeholders is paramount. Otherwise, the insights gleaned from said data are useless to help make data-driven business decisions. Ensuring data correctness is the main function of enterprise data governance⁠—effective handling of data while it’s being stored, accessed, processed, and finally deleted.

On the back end, enterprise data governance responsibilities include setting up and maintaining  the data governance infrastructure and technology, policies and processes for secure data handling, and ensuring access permissions and other security measures are adhered to by the data governance framework and users.

In a nutshell, data governance includes:

  • Creating the technical infrastructure to support data governance
  • Determining the rules behind how data is handled, managed, used, and transformed within an organization
  • Delegating responsibility to various stakeholders within an organization (e.g., data scientists, data stewards, data analysts, etc.) to maintain the integrity of the system

While there is a lot of technology that goes into effective enterprise data governance, but it’s really the professionals behind data governance initiatives that are essential for keeping them running smoothly and effectively.

Data Governance Benefits

Implementing a data governance strategy in your organization has a myriad of benefits, including:

Better Decision Making

As well-governed data is easily accessible by anyone within the organization that has the credentials to access it, and it’s organized in a standardized structure, it’s easier for stakeholders to make data-driven decisions based on correct information.

A Common Understanding of Data

A well-implemented data governance strategy helps those that access and use the data better understand who is responsible for what data and why ensuring the security of data as it moves through an organization matters. Permissions and access privileges provide transparency and accountability, and data lineage allows data governance stakeholders to see how data moves through the organization.

Improved Data Quality

The goals of data governance and maintaining data quality often overlap because they are both interested in the integrity of data. Data governance, for instance, aims to govern how data is stored, who has access to it, and who is responsible for it. Data quality, on the other hand, is focused on the completeness of data and its usefulness. At the end of the day, both initiatives are aimed at the common goal of making data better.

Improved Data Management

Data management and data governance go hand-in-hand. While data governance outlines how data should be handled, data management is the framework for actioning the policies and procedures surrounding data governance, ensuring that they are followed. Data remains secure while still being accessible to those who need it.

Empowers Experts

Data governance has another benefit that may be overlooked: empowering experts. When experts have access to the data they need, they can make better decisions and recommendations regarding how to move forward with a project, client, workflow, process, or some other business functionality. In other words, data governance empowers experts to make positive changes in an organization that can have a positive cascading effect.

Data Governance Best Practices

Data Governance Best Practices

A well-managed data governance system affirms an organization’s whole digital transformation since it touches every department and directly affects how data is treated.

Effective data governance requires an understanding of typical best practices:

Set Standards for Your Data

Data standards are necessary for reducing silos and encouraging interoperability, and maintaining data integrity. Governance with regards to how data is represented, formatted, defined, structured, and tagged is defined in predefined technical specifications and agreements to ensure that the integrity of data remains as it moves through, and is used in, different systems.

Developing effective data standards requires creating a centrally managed data language to create consistency with datasets across an organization. This will likely emerge as:

  • Data identifier taxonomy, referenceable fields, patterns, and powered lists
  • Data science models that determine how these elements should work together

Data standards should remain flexible, however, so that as your data strategy evolves over time, your data standards can be easily updated and changed as needed to support it.

Next, you need to apply your data standards to make it easy for stakeholders to access and use them. This means delegating who has access and ownership of the data standards (likely the same individuals that are in charge of your organization’s overall data governance), how those stakeholders can access them (e.g., through an interface), and how they will be managed over time.

From there, you’ll need to connect the various tools within your organization’s tech stack so that they adhere to your data standards. Dedicated data standards platforms are available to do this via integrations, APIs, and the ability to import/export files.

Establish Governance Team Roles

An effective enterprise data governance team is typically comprised of several roles, including a chief data officer, data owner, data steward, administrator, custodian, data governance committee members, and of course, the data user. It’s important that everyone understands their role and how it contributes to the success of the enterprise data governance strategy.

  • The Chief Data Officer is an emerging executive role that is responsible for achieving data governance buy-in from the rest of the executive team and acts as a leader for an organization’s overall data governance strategy.
  • Data owners have the responsibility for directing how the data they produce should be used. For instance, an organization’s customer service department manager would be a data owner, and should help ensure that sensitive customer data is utilized correctly and with adherence to regulatory privacy policies and organizational policies.
  • Data Stewards are the champions of an organization’s data governance. They handle the training and education and help enforce enterprise data governance policies.
  • Data Administrators are basically overseers; they act as a point of contact for resolving data-related issues, and they are also responsible for processing data into workable data models.
  • Data Custodians handle the technical aspects behind the movement of data, including the security, storage, and use. The difference between a data steward and a data custodian can be confusing, as their roles can overlap depending on the organization. The clear difference between the two is that data stewards deal with the data directly, while the data custodian is responsible for the tech behind the data’s infrastructure and environment.
  • Data Users, as consumers of data inside and outside of an organization, are also an integral part of data governance. Data users are responsible for educating themselves on the organization’s data governance, access, and use policies (and ensuring that they follow them), using any data governance tools effectively (e.g., data dictionaries and data catalog tools), and bringing any issues with data quality or integrity to the attention of the data governance team so action can be taken.

Not every organization may require someone to fulfill all of these roles, and in some cases, overlap in responsibilities may occur. The important thing is that each member of a data governance team understands their roles and responsibilities and has the tools and resources they need to ensure that the organization’s enterprise data governance runs smoothly.

Map Your Business Goals for Governance

Having an effective data governance strategy requires consideration of business goals and how they relate to your overall strategy. The goals you map to your data governance should start with objectives that are important to the organization’s leadership. In other words, start with mapping the most important (top-level) objectives to data governance and go from there.

For instance, one top-level goal for your organization could be to discover your BI assets. In this case, the roadmap to get to that point should include a milestone that describes how BI assets are collected, curated, and defined.

Focus on Simplicity in Most Areas

Because data governance isn’t a primary job focus for the majority of employees within an organization, it’s important to focus on simplicity and intuitiveness when developing data governance standards and rules. In other words, making rules easy to understand and follow will result in better buy-in and adherence from employees across the organization.

Account for Unmanaged Data

Your organization’s data governance process should include how to account for unstructured (unmanaged) data⁠—data that doesn’t fit anywhere in particular and can’t be used in applications or programs without processing. Because this data doesn’t have a structure, it can be at more risk than your managed data. While raw, unstructured data has typically been stored in data lakes, a lakehouse is one solution that your data governance team could consider to alleviate the security risks that are often associated with data lakes.

Classify and Tag all of Your Data

Establishing metadata standards is extremely important for ensuring that data works to meet your organization’s goals. It also allows data to be reused appropriately. Without effective data classification and tagging, it would be extremely difficult to achieve standardization with regard to your organization’s data governance strategy.

Measure Progress With Multiple Metrics

Metrics helps organizations measure the effectiveness of their data governance strategy. The specific data-related metrics that you may want to focus on include:

  • Data quality, which includes data accuracy and completeness, consistency, integrity, and data issues found and resolved.
  • Data literacy rates, which should focus on the effectiveness of enterprise data governance training programs, general organizational knowledge of data governance policies and procedures, and overall adherence to the data governance strategy.
  • Data ownership and accountability, in terms of how successful data owners have been with regards to maintaining data reliability, as well as following organizational and legal requirements with data governance.
  • Business value, which refers to how effective the data governance strategy has been in meeting organizational goals and objectives, such as cost savings, revenue growth, and compliance.

Automate as Much as Possible

To reduce the likelihood of human error and ensure that workflows, processes, data requests, and more operate as intended in terms of data governance. It’s important to implement as many data automations as possible. This will make data governance easier and save time and resources.

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Data Governance Principles

The thought process behind the data governance best practices listed above ties heavily with specific data governance principles. These principles describe the general framework that your data governance strategy should follow:

Data governance principle Description
Integrity Everyone involved in enterprise data governance needs to uphold a policy regarding honesty in terms of issues regarding security, roadblocks, inefficiencies, and more so they can be addressed as soon as possible.
Transparency All enterprise data governance processes should be clear, easy to understand and have a straightforward implementation process for all stakeholders.
Auditability Activities should be supported by documentation and records to ensure compliance with regulatory and organizational requirements.
Accountability Each stakeholder in data governance should understand their roles and responsibilities to ensure that accountability can be maintained.
Stewardship Data governance responsibilities in terms of stewardship should be clearly defined for both individuals and groups.
Checks & balances Data owners, users, and managers should undergo a series of standard checks and balances when data is accessed and used to ensure its adherence to organizational data governance.
Standardization The standardization of enterprise data, in terms of format, metadata, categorization, and other requirements, is the heart of an enterprise data governance strategy.
Change management Data governance is a living strategy. It should be flexible enough to accommodate changes across processes and procedures as needs, goals, and objectives evolve.

Data Governance Process

Data Governance Process

Understanding the methodology behind data governance best practices and data governance principles leads to the development of the overall process or roadmap that guides them. An effective data governance process ties best practices and principles into an actionable plan to achieve enterprise data governance goals.

Here are the steps required for a data governance process (or roadmap):

1. Assessment

A data governance assessment is an essential first step in developing an enterprise data governance roadmap because it allows you to analyze the organization’s current security and access processes and procedures, identifying weak points, inefficiencies, and archaic processes.

A maturity assessment or model is typically used as it provides the most efficient and effective method for understanding the current state of an organization’s data governance and how it does or does not meet the data governance goals and objectives that they want to achieve.

A high-level overview of what a maturity model helps an organization understand about their data governance includes:

  1. What is needed to support business intelligence. This could be the development of a data warehouse, database, or another repository.
  2. The current state of the organization’s metadata management.
  3. The overall view of the organization’s current data governance process, or lack thereof.
  4. What technical or organizational changes need to be made in order to support the data governance model the organization requires.
  5. What infrastructure, data architecture, and other technologies the organization needs as part of their data governance system.

2. Stakeholder Input

Once there’s an understanding of the organization’s current situation with data governance, work can start on determining what needs to be done to get the organization’s data governance strategy to a place where it meets organizational goals. This will involve input from multiple stakeholders, including those that will be using the data in their jobs.

Once input is gathered, a data governance team should be identified according to an organization’s needs and budget. These include data professionals such as data scientists and analysts, but also individuals who will be responsible for enforcing data governance in specific areas across the organization, like department managers and other team members.

Of course, data privacy and security should be considered, including how data movement adheres to regulatory requirements and other laws, such as GDPR and CCPA.

3. Documentation

Every stage of the data governance process should be documented for easy reference. Easily accessible documentation helps ensure that every stakeholder involved in enterprise data governance, regardless of their responsibilities, can refer to the process and carry out their duties consistently. Documentation should also be editable for the relevant stakeholders so that changes and updates can be made, with notifications being sent to all stakeholders so they can stay informed.

Data Governance Metrics to Keep an Eye On

Data governance metrics are detectable, measurable outcomes that tell the full story of how your organization’s data governance strategy is working in a real-world context. Significant data metrics to watch out for include:

Data Governance Metric How to implement it
Improvement in data quality scores Data quality refers to the completeness, accuracy, and timeliness of data. A single data quality score for each of these factors or a consolidated score can be applied. The goal is to measure the quality of data over time so improvements or adjustments can be made as needed.
Adherence to data management standards and practices A certification and training process should be in place to ensure that every stakeholder understands their data governance responsibilities. Data lineage and tracking can be used to determine adherence over time.
Reduction in risk events Risk events can include:

  • A penalty or fine imposed by a regulator
  • Inaccurate decision-making due to poor data
  • Client loss due to incorrect reporting

An effective data governance strategy should result in a significant reduction of risk events. If they are still occurring, then you’ll know that adjustments need to be made.

Reduction in rectification costs The time, resources, and costs involved to ensure that source data can be used for its intended purpose is referred to as data rectification. With an effective data governance policy, there should be a reduction in the costs associated with rectification, as data governance should ensure high-quality source data.

Big Data Governance vs. Data Governance – What is the Difference?

The principles of data governance apply whether an organization is dealing with regular or big data. But because big data is much more complex in terms of its variety, volume, and velocity, it’s much more difficult to process and use when relying on traditional methods, which means that traditional data governance technologies and processes won’t be able to handle it appropriately.

Big data characteristic Description
Variety Big data is comprised of many different types of datasets. These datasets are typically unstructured or semi-structured, so additional processing power is needed to extract meaning from them.
Volume Big data is high-volume, low-density, and unstructured, often in terabytes or petabytes.
Velocity Data velocity refers to how quickly data is received. Data streams allow a substantial amount of data to be transferred to an organization at a high rate of speed. This means collecting data from hundreds or thousands of data sources and sending large data clusters to be processed.

The value of big data to an organization falls within these three characteristics. If an organization can process big data effectively, they get more information to work from and are therefore able to make more educated and better business decisions.

Big Data Governance Framework

To govern big data sets in a way that works for an organization, a more in-depth look at the technologies that support data governance are required. For enterprise organizations, this means upgrading their traditional enterprise data warehouse(s) (EDW) to support enhanced architecture that can handle the complexities of big data.

This extended data warehouse architecture is comprised of the following elements:

  1. Data layer, which includes structured and unstructured data and real-time streaming data
  2. Integration and ingestion layer, which refines the data and assigns information to it so that it can be used for BI, data analysis, or other business processes.
  3. Processing layer, which structures data into suitable formats for querying SQL and data warehouse OLAP servers, and also allows it to be used by investigative technologies such as Hadoop or Spark.
  4. Analytics and BI layer, which provides the data to technologies for data visualization and business intelligence so that data scientists and analytics can explore the data further.

At every point in this extended data warehouse architecture, data governance policies and procedures need to be applied. This means revisiting the same governance principles that are traditionally used but remolding them to work within a framework that processes larger datasets faster.

How Revelate Handles Data Governance

How Revelate Handles Data Governance

Revelate’s data fulfillment platform offers a highly customizable, white-labeled data marketplace solution that organizations can use to distribute datasets internally and externally, whether that be through data monetization strategies or data sharing.

The data marketplace allows you to apply your own data governance framework in terms of how data is accessed and by whom automatically. In addition, your organization’s data is not stored on Revelate servers. Rather, Revelate facilitates the extraction of data from the source (e.g., EDW) when the customer requests it via the data marketplace and fulfills the order automatically with no additional input required from your teams.


Data equals opportunity. That’s why enterprise organizations are increasingly taking advantage of the insights that data has to offer, whether it be through data sharing or monetization strategies or through analysis of big data from thousands of different sources.

However, to ensure that everyone within your organization can access and use data effectively, it’s important to have a data governance solution in place that allows data access while still maintaining full control.

Revelate is a data fulfillment platform that helps organizations democratize access to data, giving them full control but offering a new and more efficient way to fulfill data orders. Want to learn more? Book a Demo today!

Simplify Data Fulfillment with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started