What is Data Governance? The Ultimate Guide

This blog was last updated in September 2023

What is Data Governance?

Data governance defines how data should be gathered and used within an organization. It address core questions, such as:

• How does the business define data?

• Where does it live?

• How accurate must the data be for use?

• Who can use it?

• How can they use it?

Such questions inform the governance rules and principles, which seek to maintain the highest quality of data possible throughout its life cycle.

Data governance creates a foundation to improve data quality and accuracy. While data governance solutions once focused almost exclusively on compliance, the age of big data has broadened its applications.

Gartner defines information governance as, “the specification of decision rights and an accountability framework to ensure appropriate behavior in the valuation, creation, storage, use, archiving and deletion of information. It includes the processes, roles and policies, standards and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals.”

Simply put, data governance guides compliant human behavior around data. Modern approaches create a framework for data-driven decision-making. Setting clear expectations around how to work with data improves quality outcomes and builds trust between people. Therefore, organizations have a lot to gain from implementing a data governance framework.

Why is Data Governance Important?

Data governance is gaining importance as organizations use it for compliance and various use cases including collaboration and discovery. Forrester defines data governance as software and services that help manage data policies, quality, and compliance. Aspects include data definitions, policies, quality, stewardship, literacy, regulatory compliance, ethics, risk management, privacy, security, and end-to-end lifecycle management. It should integrate with an organization's data strategy.

In essence, data governance is about trust and ensuring data quality, security, and compliance. It ensures you can find data, improve definitions, enforce policies, and maintain quality – all in a collaborative, transparent environment. It also sets the groundwork for data security, compliance, and ethical use.

Who Is Responsible for Data Governance?

A successful data governance framework includes people from different parts of the organization. This inclusivity is important for creating a data-focused culture in the organization.

Key Data Roles that Benefit from Effective Data Governance

Some of the key data roles that benefit from effective data governance include:

Data Custodians

Data custodians protect and maintain data. Data governance offers rules and policies to help custodians do this, ensuring that data is used securely and compliantly.

Data Stewards

Data stewards are key in setting data quality standards and making sure available data meets those standards. Governance frameworks give data stewards the tools and policies to keep data accurate.

Analysts

Analysts need high-quality, well-governed data for their work. Data governance ensures they have access to reliable and accurate data for more reliable reports.

Data Governance Leaders

Leaders in charge of data governance benefit from frameworks that guide their decisions and support data-related projects.

Compliance Specialists

Compliance specialists make sure data governance follows rules and laws. Governance frameworks help them stay compliant and reduce risks.

Chief Data Officers (CDOs)

CDOs are key for data governance. They use governance frameworks to plan strategies, set priorities, and make sure data governance aligns with the organization's goals.

Chief Privacy Officers (CPOs)

CPOs focus on data privacy and protection. Data governance gives them the structure and rules they need to enforce privacy policies and keep sensitive data safe.

Data Protection Officers (DPOs)

A DPO is an enterprise security leadership role required by the General Data Protection Regulation (GDPR). DPOs, ensure organizations follow regulations like the GDPR. They oversee data governance to enforce data protection, evaluate risks, and ensure data security.

Effective data governance addresses the needs and responsibilities of each role as it relates to data. For example, data stewards set data quality rules, which guide data custodians. The governance approach chosen affects how custodians handle data. It's crucial to pick a governance style that fits with the organization's goals and meets the needs of these roles to make sure data remains valuable from start to finish.

What are the Benefits of Data Governance?

The integration of big data has expanded the applications of data governance; and as a result, numerous benefits from data governance have followed. Some of the direct business benefits include:

Better Analysis

Because data governance enhances data quality and findability, analysts can find, understand, and analyze data more quickly – and compliantly.

Easily Defined Goals

Data governance sets out a clear method to achieve business goals.

Consistent Compliance

Data governance helps data users stay compliant with regulations, reducing the risk of fees and reputational damage.

Improved Data Management

Governance minimizes duplicative efforts, which boosts operational efficiency.

Standardized Systems and Data Policies

Standardizing systems and policies across the organization instills users with ethics and awareness.

Improved Data Quality

As a business implements data governance, data quality will improve. This leads to more accurate business processes and higher customer trust.

Data governance ensures that data users follow the same guidelines to achieve higher quality and accuracy; it also provides a flexible framework that responds to new laws and regulations around data usage.

Chances are, if your organization is heavily focused on data, then regulations like GDPR, CCPA, and HIPAA have crossed your radar. But without a solution that classifies data assets and communicates policies to front-line users, organizations are exposed to higher data risks.

Data governance establishes similar data-use processes across an organization. This is valuable, as dissimilar processes lead to inconsistent data quality and accuracy. If implemented properly, a data governance framework not only synchronizes behavior but also helps data users understand the guidelines governing that behavior.

This is essential: If people are tasked with doing things a certain way, it’s natural they should ask why. A transparent governance framework clarifies the why behind the how of data processes. This transparency secures buy-in, builds trust, and empowers people to understand the system — enabling them to improve within that system and educate others on how it works.

What are the Challenges of Data Governance?

Challenges within data governance are inevitable. Issues from data responsibility, quality, and security, make it hard to manage data governance. Top challenges of data governance include:

Limited Resources

Organizations have a hard time establishing who handles what data, when, and how. This creates a lot of pressure within the IT department to manage data access, which can cost the business months of lost labor.

Data in Silos

Siloed data only allows one group to manage data. This creates a series of cultural, structural, and technological disadvantages.

No Data Leadership

Lack of strong data leadership results in mismanagement and a misunderstanding of data. Leaders need to educate themselves around data governance to understand the risks it mitigates and the value it brings to their business.

Negative Experiences

Data governance is tough to get right. In fact, most data governance initiatives fail on the first attempt (as many as 80%, according to Gartner, The State of Analytics is Worse Than You Think). Those who have witnessed governance failures first-hand may be tough to convince it can work.

Data Quality

Data quality is relative, but today, many catalogs will crowdsource feedback to gauge quality (think Yelp or Amazon, but instead of restaurant or gadget reviews, a data catalog may invite users to review and rate data!). Without an emphasis on data quality, the use, age, and accuracy of the data diminish.

Lack of Control

A lack of control over data governance within a business can result in noncompliance. When data consumers process data unlawfully, the entities that employ them can endure serious fines and consequences.

Each of these challenges within data governance are preventable. But it’s important to understand that most of these challenges stem from a lack of understanding of data and misinformed leadership. Not only is this a disadvantage to the business, but to the roles within the organization that benefit from successful data governance.

What Roles Benefit from Data Governance?

A governance platform must be built with all data users in mind, from leaders to end-users, to ensure organizational buy-in and trust. This inclusivity is vital for creating a data culture. Some of the data roles that benefit from a successful data governance framework include:

• Data Custodians

• Data Stewards

• Analysts

• Data Governance Leaders

• Compliance Specialists

• Chief Data Officers (CDOs)

• Chief Privacy Officers (CPOs)

• Data Protection Officers (DPOs)

Data governance, done properly, benefits everyone responsible for data at different points of its lifecycle. For example, data stewards may define what constitutes “quality data.” This definition, in turn, informs the actions and responsibilities of the data custodian. Governance solutions can either make the custodian’s life more efficient or derail their process completely. It all comes down to the philosophy behind the data governance framework or even the type of data governance you choose to implement.

What are the Types of Data Governance?

The philosophy and implementation of data governance depend on the form. A successful data governance implementation will take cues from your organization’s culture. Here are three approaches to consider:

1. Command & Control Data Governance

• People must be assigned to data stewardship roles

• Forced governance of data

• Very invasive

• Demanding tone

• Early purchase of tools

2. Traditional Data Governance

• Path is set and users are expected to take it

• Passive governance

• One-size fits all approach

• Innovation through governance is not a priority

• Formal decisions are not fully understood

3. Active Data Governance

• Non-invasive

• Embeds governance in users’ day-to-day activities

• Assumes people are already governing data informally

• Distributes decision rights

• Believes people can be more efficient and effective if they are recognized and rewarded

• Establishes common trust model

• Achieves business outcomes while managing risks

A people-first approach to governance has gained traction in recent years. Bob Seiner, an expert in data governance, explains that, “the best approach is the approach that will activate your program, engage the data stewards, and lead to the demonstration of business value.” The benefits will spring from the data governance approach an organization takes — and how it involves the people responsible in that journey.

What Are the 4 Pillars of Data Governance?

Data governance has four main pillars that create a strong base for managing an organization's data. These pillars give an organized way to make sure data is handled, maintained, and used correctly. Here are the four data governance pillars:

1. Data Quality

This pillar emphasizes the importance of data accuracy, consistency, and reliability over its entire lifespan. It includes setting data quality standards, evaluating data quality, and establishing procedures to clean, validate, and enhance data to meet these standards. Maintaining high data quality is crucial for informed decision-making and preventing errors resulting from unreliable or inconsistent data.

2. Data Privacy and Security

Data governance encompasses actions to safeguard sensitive information and comply with data privacy laws like GDPR, HIPAA, or CCPA. This includes setting up controls for data access, encryption, authentication, and authorization to protect data from unauthorized use or breaches. Additionally, it covers aspects like data categorization, risk evaluation, and how to respond to incidents.

3. Master Data Management (MDM)

MDM is an integral part of data governance, aiming to establish a unified and consistent dataset for critical business entities like customers and products across an organization's systems. Its primary purpose is to ensure data accuracy and consistency, aligning with data governance's goals. However, MDM implementations can sometimes spark internal debates due to differing preferences on data formatting. Despite its complexity limiting broad adoption, smaller MDM projects are growing. These are driven by overarching data governance objectives, and demonstrate the growing synergy between the two disciplines in achieving data quality and consistency.

4. Data Governance Framework and Policies

The governance framework serves as a blueprint for how people within the organization handle data. It clarifies who is responsible for what, and creates consistent data management protocols. Putting a governance framework into practice typically requires forming data committees, picking data stewards, and making rules for data. A clear framework ensures that everyone in the organization follows the same data rules and works toward the same big goals.

These four pillars work in tandem to provide a comprehensive approach to managing data effectively, ensuring its quality, protecting it from security threats, and optimizing its lifecycle. A robust data governance strategy helps organizations maximize the value of their data assets while maintaining compliance and minimizing risks.

What Are the Components of a Data Governance Framework?

A data governance framework is a structure that lays out processes, policies, roles, and responsibilities for managing data, serving as a guiding blueprint for how data is handled, protected, and used.

Key components may be sorted by people, process, and technology. 

• People. Clear roles clarify to people their governance responsibilities. Governance committees define key processes.

• Process. What governance principles, policies, procedures and metrics should guide usage? 

• Technology. Seek out a platform that unifies processes with people, supporting communication, monitoring, and lifecycle considerations.

This comprehensive plan helps people manage data assets well, guarantees quality and compliance, and gets the most value from data while aligning with organizational goals, making data a strategic business asset.

Now that we’ve identified the components of data governance, let’s explore the common misconceptions of data governance.

What Are the Misconceptions of Data Governance?

The term data governance can be combined with many other data definitions. In most cases, one data function cannot live without the other, but it’s important to understand their individualized capabilities and how they work together. Let’s clear this up:

Data Stewardship

Data stewardship is a facet of data governance. Data stewardship makes sure that data is accurate, controlled, easy to find and processed by authorized parties. Data governance supports this by making sure the right people are assigned to the right data responsibilities.

Master Data Management

Master data management (MDM) reconciles data from key entities so that data is useful across the business. Key entities may include customers, buyers and product data — or data types that are universally collected by all business departments, but rarely in uniform ways. MDM addresses this problem by building a unified view of that shared data.

In order for MDM to be successful, it must have data governance. Data governance helps define the findings of master data so that data can be understood — defining roles, responsibilities for data authorization, data curation, and data access.

Data Management

Data management is an umbrella term under which governance sits. It describes the management of data’s entire lifecycle.. Data management is responsible for connecting other disciplines — data quality, data security, reference and master data management, metadata management, data warehousing, and database operations. Data governance is the foundation for how to collect, store, and use data.

While these data concepts can often get confused with one another, it’s important to note that they have to work together in order to both be successful. This is especially true for cloud migration.

Cloud Migration and Data Governance

Migrating data to the cloud is becoming more popular as businesses notice the benefits of cloud data governance and storage. A cloud data environment gives users flexible access to data by removing data silos and centralizing compliance and data security. But the secret to successful data cloud migration is a strong data governance foundation.

Governance is crucial for migrating data to the cloud. Moving data processes to the cloud is innately complex due to data security and accessibility concerns. But a strong data governance foundation eliminates that risk by clearly labeling sensitive and legacy data. In this way, governance supports an informed, careful migration, in which effective data storage and security is maintained.

With proper security and efficient storage, the next question to address is how to make sure the right people have access to specific data at any time. This is where data democratization comes into play.

Why is Data Democratization Important in Data Governance?

Quality data is only powerful if people can use it. In the past, many companies adopted a “bunker mentality” around data, locking it away for fear of misuse. Nothing could be worse for creating a data-driven culture! In an enterprise, many people make business decisions each day. Those decisions need the support of good data. However, democratizing data means making it available to those who need it.

A data catalog is pivotal for implementing data governance and ensuring data democratization.

Data catalogs implement data governance in various ways. A catalog that takes an active, non-invasive approach will track user behavior to spot governance patterns at the source. By spotting patterns in user behavior with machine-learning and AI, a data catalog can formalize those patterns into a framework everyone shares and sees. Minds align around shared processes, and trust between people grows.

How Alation Activates Data Governance

The magic behind Alation’s data governance framework is the people-first approach. This approach uses an iterative process of continuous management and improvement. Not only does this improve internal communication, but it also allows the governance strategy to evolve over time into its most adoptable form, while being as minimally invasive as possible.

When implementing a governance strategy, organizations need a way to track progress. Alation’s Stewardship Dashboard helps stewards communicate as they track, measure, and report impact related to their efforts. This dashboard simplifies data governance through automation and behavioral insights. To get a better idea of how non-invasive data governance works, learn how Alation helped implement a governance solution for Riot Games.

When implemented intelligently, data governance improves data quality and promotes business efficiency. With a proper framework in place, any organization can start leveraging data governance.