Data governance is a broad term that refers to the strategy of managing availability, usability, standard compliance, consistency, data integrity, and data security in organizations and companies.

While the term is notorious for escaping definitions, data governance is often defined as the first essential branch of data management strategy.

Data governance serves as a decision-making process with the purpose of creating roles, policies, and metrics for the sole purpose of ensuring a company’s data is used efficiently.

As a general practice of organizing and implementing data policies, data governance mainly deals with the enforcement of how, where, why, by whom, and under what circumstances data assets and processes are collected and stored. It’s based on a company’s established models of decision rights, accountabilities, internal data standards, and external data policies.

The Growing Need for Data Governance

Twenty years ago, companies and organizations considered data merely as a strategic asset for decision-making and business experimentation. According to the Aureus Analytics report, the world’s data volume will grow at a staggering 40% per year, projecting growth trends from 2021 to 2026.

With digital transformation moving at breakneck speeds and the increasing need for strong cybersecurity capabilities, many enterprises began regarding data and data security as an absolute priority. This led to the birth of new data management concepts like data governance.

Today, many organizations are dependent on data analytics and data classification. They’re also trying to find ways to deal with the latest data privacy regulations and data leak prevention strategies, as well as improve their operations.

This is why a well-structured data governance framework is crucial — to ensure that an organization’s data is consistent, trustworthy, and well-protected from misuse.

Data Governance, Data Management, and Master Data Management

Data governance is often used interchangeably with closely related concepts like data management and master data management, but they are distinct.

  • Data governance can be regarded as a primary component of data management. While many types of data management exist, data governance focuses primarily on documentation, outlining ownership, delegating access authority, data sourcing methods, regulatory compliance, and the measures implemented to keep data secure.
  • As its name suggests, data management means the management of the full data lifecycle requirements within an enterprise. It’s focused on the action and execution of the policies, tasks, rules, and guidelines outlined in a company’s data governance strategy.
  • Finally, master data management (MDM) is a data discipline in which both IT staff and other business units collaborate to manage their organization’s main data assets. Their goal is to identify the key data entities like suppliers, stakeholders, customers, and other providers shared across the organization. MDA is focused on ensuring that a company has complete, uniform, accurate, and final data information with semantic consistency regarding these key entities. It goes beyond simple data governance because it attempts to reconcile fragmented views of the same entities into a single view.

Without proper data governance, there can be no efficient MDM. A data governance program may also define other requirements, such as master data management models (e.g., distinguishing a customer or product), appointing roles and responsibilities for data authoring, allocating data retention policies, data curation, and data access management. 

Other data-related disciplines include:

Why Is Data Governance Important?

An organization without an efficient data governance program may face unresolved data issues and inaccuracies within its system that might become a serious problem for the company’s operations.

These inconsistencies are commonly illustrated when a customer is registered and named differently across different departments in the company, such as sales, customer service, and logistics.

Such negligence may seriously hinder data integration efforts and cause problems in the organization’s data integrity, adversely affecting business intelligence (BI), reporting, analytics accuracy, and other crucial operations, including data security.

Proper Use of Data

One of the main tasks of data governance is to ensure that data is used and managed appropriately. Proper use of data prevents potential data errors and the misuse of sensitive information within an organization’s systems.

An organization must establish data use policies and crucial monitoring procedures to properly and constantly maintain given policies.

Additionally, data governance seeks to establish a very strict balance between data privacy compliance and data collection methods that are well within the confines of an organization’s internal and external policies.

Harmonizing Data Silos

Another important goal of data governance is fractioning an organization’s data silos. Data silos are data repositories organized and segmented into separate departments, sectors, or business units.

These silos need to remain incompatible and isolated from the rest of the data sets. Otherwise, they become saturated when business units create new transaction processing systems without centralized coordination or orderly data architecture.

Without a proper data governance management system and collaboration with stakeholders, data silos can’t be harmonized and kept at acceptable saturation standards.

Least Amount of Data for the Least Amount of Employees

Data governance is used for establishing data-related processes to ensure effective data management and use of the organization’s information assets, namely, accountability for how poor data quality may affect the organization.

That being said, organizations and enterprises commonly use data governance for its security benefits.

It has the purpose of grouping and categorizing data based on priority, sensitivity, and legal requirements, while also allocating data use among employees.

For example, to avoid the risk of data exposure from leaked credentials, organizations can implement data governance strategies, such as the principle of least privilege, to limit the data employees have access to and allow them to only work with the least amount of data required to do their job.

Learn how to implement the principle of least privilege.

Regulatory Compliance

Most importantly, data governance helps organizations comply with data protection regulations and initiatives to avoid data-related violations.

Poor data governance indirectly affects regulatory compliance, which, in turn, can cause major security incidents, including high-profile data breaches.

Usually, it’s hard for organizations to control data flow because of rigorous data protection compliance regulations, and this often results in violating data protection regulations.

That’s why it’s important to have an enterprise data governance program with a mature data governance framework that defines and standardizes common data definitions and formats. As we discerned, achieving acceptable data consistency is crucial for both business and regulatory compliance.

A good data governance framework handles and manages every single data asset and maintains a platform for consistent compliance to satisfy the data protection mandates of many regulatory requirements, such as:

Data Governance Use Cases

Other use cases for data governance include:

  • Data management in operational systems, BI, and analytics applications that are given by data warehouses, data marts, and data lakes;
  • Digital transformation initiatives in which data governance helps with digital risk management, business process management, and other corporate decisions like mergers and acquisitions;
  • Newer technologies in which data governance is applied for machine learning algorithms and other AI tools.

The Benefits of Data Governance

Implementing a good data governance strategy yields many benefits, including:

  • Improving data management — Data governance establishes codes of conduct and best data governance practices. This is important for consistently addressing the needs in sectors beyond data like legal, financial, regulatory, and cybersecurity.
  • Driving important business decisions — Data governance grants decision-makers of the company access to reliable data that would positively affect how companies make business decisions. Marketing, sales, and IT teams can share data and cross-pollinate insights to save time and resources and, in turn, generate profits and revenue.
  • Lowering data management costs and data monetization — Data governance helps companies manage, standardize, filter out, and classify valuable data from the collected and generated data, which promises better revenue for the company.
  • Improving data comprehension — Data governance provides a better overview of data terminologies for employees, customers, and business units, creating valuable data maps and usable data assets for business outcomes. Additionally, it can improve how data access is granted to data scientists and analysts.
  • Better data quality — With data governance, organizations can make plans which ensure proper data accuracy, clarity, consistency, and data wholeness. Dealing with low data quality makes it impossible for organizations to make accurate decisions or take calculated risks. According to IBM, U.S. companies annually lose $3.1 trillion because of poor data quality. Data governance is required to optimize workflows and provide staff members with valuable data assets of an adequate standard.
  • Consistent overview of customer data, competitor data, and other businesses — Data governance creates a unified framework that helps organizations agree on “a single version of the truth” for better consistency, meaning employees won’t have to waste time managing inconsistent data and conflicting information.
  • Increasing workforce efficiency — Data governance can standardize data and make it available across the organization so that sectors will avoid wasting efforts in dealing with faulty or duplicated data.

Data Governance Challenges

Since different companies deal with different aspects of important data entities and frameworks, the initial steps in implementing a data governance initiative can be very demanding.

A company must assess these data governance challenges and resolve them as a critical objective of its data governance process. This process occurs via consensus on common grounds and accepting a unanimous definition for data formats and data definitions among their sectors.

Though it may be complex, agreeing on a middle ground for data definitions can be done with straightforward dispute-resolution methods.

The challenges of implementing and maintaining data governance that organizations are faced with include:

Demonstrating Business Value

For a company to consistently demonstrate business value to its associates, competitors, and target audience, it needs to develop and implement proper and standardized metrics for data quality. This includes measuring accuracy, database error rates, and the number of resolved data errors.

Self-Service Business Intelligence

Organizations must deal with maintaining self-service data analytics.

However, with newer implementations of self-service business intelligence and analytics, more and more data is being handled by different staff members within a company. This makes it very complicated for companies to implement proper data governance.

Data governance programs evaluate data accuracy to make it more accessible for business analysts, self-service accounts, and data experts. They must also make sure that the data doesn’t get mishandled, which, in turn, may result in serious data security and privacy issues.

Deploying Big Data

Deploying big data can be a very arduous task for organizations, especially new organizations and businesses.

Data governance programs commonly focus on structured data. However, with big data, they face a combination of structured data, datasets/qualitative data, and half-structured data, not to mention new dynamics in managing data platforms like cloud-based storage, NoSQL databases, etc.

Big data sets are commonly stored in data lakes. Since they’re in raw form, they must be filtered in order to be properly used for analysis, which can pose a challenge for data governance teams.

Key Persons in Data Governance

Depending on the type of company, there can be many people that oversee, manage, and work with data governance.

From IT experts who are acquainted with the organization's data domains to corporate staff with basic data understanding, all of them play important roles in data governance.

Here are the key persons involved:

Chief Data Officer (CDO)

The CDO is the senior executive solely responsible for monitoring and overseeing the data governance within an organization. Their role is concerned with enabling funding, choosing the staff, and approving key decisions in the data governance program.

Some organizations may also use a C-suite executive instead of a CDO.

Data Governance Manager

The manager runs the data governance program and is responsible for implementing strategies for effective data governance.

The program manager leads a data governance team, coordinates the data governance process, schedules meetings and maintenance sessions, tracks metrics, and manages communication between key sectors.

A CDO or another executive with higher power can also act as a data governance program manager.

Data Governance Committee

The Data governance committee (or council) is responsible for making decisions concerning policies, guidelines, and standards.

The committee consists of data owners and business executives. Their role is to settle disputes between business units regarding data formats and definitions and to approve, modify, or deny data governance policies regarding data usage and data access.

Additionally, the committee may collaborate with data experts, analysts, and architects to trace and examine important data metrics.

Data Stewards

Data stewards have extensive knowledge of a company’s data assets, and their job is to oversee an organization’s data sets. Stewards ensure that the committee-approved policies and rules are properly implemented and are responsible for keeping them organized.

Some employees may be a combination of IT experts and business data stewards. They’re often subject matter experts with the appropriate data expertise and capabilities. Stewards can also collaborate with analysts, database admins, and business units to resolve data issues.

Data Governance Framework and Components

A data governance framework or template defines the main tasks for the program, its decision-making obligations, accountability for functions, as well as its overall resulting success, and how it will affect the business.

In businesses and enterprises, it’s important to show the staff and employees how a data governance program may work within the framework of the organization. The data governance framework should be documented internally for transparency.

A typical data governance framework consists of:

  • data policies;
  • rules and methodology of data handling;
  • data governance processes;
  • organizational structures;
  • and software, tools, and technologies used to automate the management and internal tasks of a data governance program.

Although data governance tools are not a crucial component, they play important roles in managing workflow, handling documentation, making data catalogs, and the like. Developing and improving data management policies for better data quality and improved metadata and master data management also benefit from proper data governance.

Data Governance Vendors and Tools

For an efficient data governance strategy, there are many data governance vendors and tools available, including cloud tech giants Google and Microsoft.

Before considering purchasing a full, cloud-based data governance platform, it’s a good idea to start out with free, open-source tools and software that can be easily integrated into a working environment.

A cloud-based data governance platform will offer a very robust and efficient data governance suite that may implement metadata management components and data lineage functionality, as well as allow you to avoid overhead requirements for on-premise servers.

These tools can help an organization by:

  • offering a better understanding of an organization’s data through profiling and benchmarking personal data. Some tools can identify and single out contact information or other pieces of sensitive data in a new data set and inform the data team;
  • helping an organization’s data team and offering self-service tools for aiding in completing the company’s data stewardship objectives;
  • Improving data management by using metadata-driven ETL (extract, transform, and load), as well as ELT (extract, load, and transform) data integration apps. This method helps with identifying data pipelines, as well as accurately tracking them with end-to-end data lineage;
  • Improving data control with consistent monitoring and reviewing;
  • Documenting data so that it can be augmented by metadata. This method improves data relevance and compliance and makes it easier to be searched, found, accessed, and linked.
  • Achieving high-quality data through data enrichment, cleansing, and validation.

While data governance tools help improve internal data protection practices, external threats still remain. Cybercriminals hunt for exploitable vulnerabilities in an organization’s network security, such as data leaks, to compromise valuable sensitive data.

Even if an organization has effective data protection strategies in place, hackers will search further down the supply chain and target its third-party vendors — who also have access to this data and often have weaker security measures. 

Implementing an attack surface management (ASM) solution that instantly detects internal and third-party vulnerabilities and data leaks allows organizations to remediate these issues before they progress into costly data breaches. Used in combination with data governance tools, ASM software enhances data protection capabilities.

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