Data Science

What is Data Governance, How Does it Work, Who Performs it, and Why is it Essential?

Written byJohn Terra
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Updated onAugust 14, 2024

Whether we realize it or not, data plays a significant part in our lives, affecting everything from education to finances, leisure time, jobs, shopping and entertainment choices, and much more. Data represents so much of who we are, and much of our lives has been reduced to digitized information, which, in the wrong hands, leaves us vulnerable to possible abuse and other catastrophic incidents.

That’s why we have data governance, and that’s why we’re discussing the topic today. This article defines data governance, its components, purposes, best practices, and implementation. It also shares a data science bootcamp professionals can take to gain job-ready knowledge and skills.

So, what is data governance?

What is Data Governance?

The Data Governance Institute (yes, there’s an actual entity dedicated to data governance) defines data governance as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”

Or, in plainer language, data governance is a set of policies, processes, and tools that promote reliable and consistent data and proper data usage across an organization. It allows users to easily find, prepare, use, and share trusted data sets without relying on IT, ensuring the data isn’t misused.

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Why is Data Governance Essential?

Data governance’s main benefit is providing high-quality data for data analytics and business intelligence tools. Insights gleaned from these tools create better business decisions and improved performance. Other reasons why data governance is essential include:

It increases trust in data used for analytics and decision-making
It improves data accuracy, completeness, and consistency, increasing overall data quality
It makes it easier to locate data, thereby facilitating data availability
It prevents misuse of data
It lowers data management costs
It fosters agreement on standard data definitions
It helps organizations better comply with data privacy laws and other government regulations, such as the California Consumer Privacy Act (CCPA), US Health Insurance Portability and Accountability Act (also known as HIPAA), or EU General Data Protection Regulation (GDPR)
It removes data silos between departments and systems

What is Data Governance, and Who is Responsible for it?

Data governance is a group effort that includes data management professionals, IT staff, and business executives. Here’s a breakdown of the responsible parties. Note that not all organizations will have all of the following: much depends on the organization’s size and other factors.

Chief data officer. If the organization has a chief data officer (CDO), they are typically the senior executive overseeing the data governance program and have high-level responsibility for its success. The CDO secures approval, funding, and staffing for the program, plays a leading role in its setup, monitors its progress, and acts as its advocate internally.
Data governance manager and team. Some companies appoint a data governance manager or lead to run the program. The program manager typically heads a team that works on data governance full-time. It coordinates the process, tracks metrics, runs meetings and training sessions, manages internal communications, and executes other management tasks.
Data governance committee or council. However, the data governance team mentioned above typically doesn’t make policy or standard decisions. That’s the job of the data governance committee or council, which consists primarily of business executives and other data owners. The committee typically approves foundational data governance policies and associated policies and rules on areas such as data access and usage and implementation procedures. The committee also resolves disputes.
Data stewards. Data stewards oversee data sets to maintain order. They’re also responsible for implementing policies and rules approved by a data governance committee and end-user compliance. Professionals with knowledge of specific data assets and domains are usually appointed to the data stewardship role. This position is a full-time job in some organizations and a part-time task in others.

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Typical Data Governance Framework Components

The three primary components of the typical data governance framework are people, processes, and technology.

People: An effective data governance program includes the following roles:
- Steering Committee. This role consists of the Chief Data Officer, the head of IT, and executives from each business unit. This group establishes usage policies and data standards. The committee also defines the program’s goals and mission statement and how success will be measured.
- Governance Team. A data governance manager leads this team and implements and maintains the appropriate systems and tools. It usually consists of data architects and other IT governance specialists.
- Data stewards. This team manages data sets and enforces the business’s rules and daily needs.

Processes. The organization needs formal processes or activities that ensure consistent execution and enforcement of all usage policies and data standards established by the steering committee. Flow charts typically describe these processes, clearly illustrating inputs and tasks for each use case.
Technology. This component covers the tools and techniques for efficiently maintaining and managing data’s integrity, lineage, security, usability, and availability. Today’s tools can automate most data governance aspects.

Data Governance vs. Data Management

Data management’s scope is more expansive than data governance. Data management is the process of ingesting, processing, securing, and storing the organization’s data, which is then used for strategic decision-making to improve business outcomes. While this includes data governance, it goes beyond that scope, featuring other data management lifecycle areas like data processing, storage, and security.

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Data Governance Implementation

Here’s how to implement data governance in five easy steps:

Choose your project. Selecting the right project is critical. Make it something that will interest senior management. This means providing metrics that demonstrate tactical success and long-term goal progress.
Set your goals. What are you trying to achieve? Many governance programs fail because of vague goals or differing expectations.
Recruit the right people and organize them appropriately. Data governance programs involve many people. Even if the data governance team is small, the project will impact anyone who depends on the data. Many people will have opinions, and some may get vocal about them. Don’t worry; embrace their enthusiasm, but direct it into constructive channels.
Create your processes. Data governance teams must have clearly defined, repeatable processes to handle the task. Four core processes support every data governance program:
- Discovery. Identifying and understanding the governed data.
- Definition. Documenting data definitions, standards, policies, and processes. Assign ownership and define your key performance indicators and metrics.
- Application. Operationalizing data governance policies, business rules, and data stewardship.
- Measurements and monitoring. Measuring the value of the data governance efforts and monitoring compliance with established policies.

Choose your technology. Data governance initiatives are constantly evolving. New internal data projects and regulations (and new risks) constantly appear. You need a technological platform that delivers value today but can adapt and evolve as your requirements change.

Data Governance Best Practices

The Data Governance Institute lists the following best practices of good data governance:

Define responsibilities. Define the data governance team with clearly stated job descriptions, responsibilities, and duties. This process includes determining who’s accountable for cross-functional data-related processes, decisions, and controls.
Define accountabilities. Define accountabilities in a manner that applies checks and balances between the business and technology teams, ensuring everyone is effectively working towards a common goal.
Ensure audibility. Data-related controls, decisions, and processes must be auditable and accompanied by documentation that supports compliance requirements. Additionally, the framework must support enterprise data governance standardization.
Ensure integrity. Every organization member must work with integrity when dealing with data and each other, practicing honesty in discussions and feedback regarding data-related decisions.
Ensure transparency. Data stewardship requires transparency so every participant and auditor knows when and how data-related controls and decisions are introduced into processes.
Support change. Finally, an effective data governance program must support proactive and reactive changes initiated by management to ensure proper handling of data processes.

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What is Data Governance? Exploring the Pillars

Data governance programs are supported by several other parts of the data management process, especially:

Data Quality

One of the major driving forces behind data governance activities is developing high-quality data. Data accuracy, completeness, and consistency across systems are essential to any successful governance initiative. Data cleansing, also called data scrubbing, fixes data errors and inconsistencies, correlates and removes duplicate information, and harmonizes how customers and products are listed in various systems.

Data Stewardship

Data stewards are responsible for the organization’s data and help implement and enforce data governance policies. Typically, these stewards are data-savvy business users who are experts in their domains’ subject matter.

Master Data Management

MDM is another data management discipline closely associated with the data governance process. MDM initiatives establish a master data set regarding customers, products, and other business-related entities that ensure data consistency across the organization’s various systems. Thus, MDM naturally dovetails with data governance.

Data Governance Challenges

Although data governance is a great concept that benefits the organization and its customers, the process poses a few challenges.

Justifying its value. Without documentation to back up the process’s value, securing funding, approval, and support can be challenging. Stakeholders often must be convinced that data governance is worth it.
Securing enough human resources. This challenge includes recruiting people with the right skills, experience, and leadership qualities.
Managing cloud-based data. Migrating data and applications to the cloud poses new challenges associated with cloud usage (e.g., security and regulatory compliance).
Dealing with big data. Data governance emphasizes structured data in relational databases; big data includes structured, unstructured, and semi-structured data.
Expectations and changes. Data governance moves slowly, so leaders must temper management’s expectations while dealing with ongoing cultural and operational changes.

Also Read: What is A/B Testing in Data Science?

Do You Want to Improve Your Data Science Skills?

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FAQs

Q: What is data governance?

A: Data governance is a set of policies, processes, and tools promoting reliable and consistent data and proper data usage across an organization, letting users quickly find, prepare, use, and share trusted data sets without relying on IT.

Q: What are the pillars of data governance?

A: The pillars that support data governance are:

Data quality
Data stewardship
Master data management

Q: Is data governance a framework?

A: Yes, data governance is a framework for ensuring that organizations are leveraging high-quality data to improve processes, increase efficiency, and make better data-based business decisions.

Q: What are the main components of data governance?

A: The three primary components are: