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Data Governance

Data governance is a framework that establishes the rules, policies, and procedures for organizations to ensure the quality, integrity, security, and usability of their data assets.

What is Data Governance?

Data governance is a framework that establishes the rules, policies, and procedures for organizations to ensure the quality, integrity, security, and usability of their data assets. It involves data management practices including data architecture, data lineage, and data privacy, along with data stewardship roles.

The goal is to provide consistent, reliable, and accurate data across the organization, enhancing decision-making and regulatory compliance. Data governance also includes the establishment of data standards and principles, and the execution of these policies within the organization. Key aspects include data quality management, data protection, data lifecycle management, and regulatory compliance, such as GDPR or CCPA.

Data governance benefits

Improved decision making

High-quality data supports better business decisions, leading to enhanced performance and competitive advantage.

Regulatory compliance

Data governance helps ensure compliance with data related regulations, like GDPR, CCPA, or HIPAA. This minimizes legal risks and potential penalties.


Standardized procedures and guidelines around data management can increase operational efficiency, reducing errors and time spent on data-related tasks.

Data governance challenges

Data silos

Data stored in disparate systems or departments can hinder the establishment of a unified governance framework.


As organizations grow and their data expands, ensuring that data governance systems can scale to meet these demands can be challenging. Quality documentation is essential for scaling.

Cultural change

If buy-in isn’t gained from employees who will be implementing and working with the data governance system every day, implementation can be a challenge. Employees may be used to handling data in certain ways and need to be convinced to follow the new system.

5 data governance best practices

Have a defined strategy before starting

Before getting started with implementing a data governance program you need to define your goals and objectives. This should align with the higher level business strategy as well. The reason to do this is so you will know if you are moving in the right direction and to keep yourself honest throughout the process.

Create standard procedures

Data management tasks should have defined processes and procedures. This will help ensure consistency and lower the risk of errors.

Invest in proper tools

Where it makes sense you should invest in tools that will make your team more efficient. This can be tools that help to automate routine processes, managed database services, or tools that help with general data management.

Iterate over time

While your goals and objectives should stay the same, how you go about achieving them should be flexible. Learn from mistakes during the process and refine your processes based on real world experience.

Data security

A key part of any data governance program should be to make sure data is secure from breaches or any other unauthorized access.

Data governance key concepts

Below are some key concepts related to data governance that are worth knowing about:

  • Data lineage - Data lineage is about having the ability to track the entire lifecycle of data from when it’s created, how it’s transformed, and more. This is important for gaining insights as well as potential auditing purposes.
  • Data quality - This is the measure of how reliable and accurate data in your systems is. If data isn’t high quality it can’t be trusted and can’t be used for decision making.
  • Data stewardship - This is the practice of managing and owning an organization’s data assets. Data stewards are a key part of a data governance strategy.
  • Data cataloging - A data catalog is a full inventory of an organization’s data assets that contains their format, quality, and uses.

Data governance vs data management

Data governance and data management are interrelated but distinct concepts in the realm of data science. Data governance effectively sets the rules and principles for how a business will use data while data management is about the tools and processes utilized while following the data governance guidelines.

Data governance is the strategic framework outlining the rules, policies, standards, and roles that ensure the quality, consistency, and security of data across an organization. It is focused on the people, processes, and policies that ensure effective data usage and compliance with regulations. Governance also determines who can take what actions, with which data, in what situations.

Data management is the tactical execution of those policies and rules set by data governance. It involves the actual handling of data like how it’s collected, stored, classified, accessed, and deleted. Data management involves practical tasks like data integration, data quality management, and database operations.

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