Data management vs Data Governance: Is there a difference?

Data is an asset to organizations. However, it should be replaced as good quality data is an asset. There are only 3% of companies data meets basic quality standards. That makes it easier for us to dive into what causes these issues, right?


In order to do so, we need to understand data and determine how this could help both the users and stakeholders in the organization. How is the data originated? Who are the data producers? Is it accurate? How can we solve the data if we cannot trust it?


Who answers all these questions? Well, it’s the data management and data governance leaders in the organization who help solve these issues. There is a confusion between data governance and data management to start with and in this article, I will help you understand how data governance and data management are used interchangeably.


Difference between Data Governance and Data Management

Data Governance consists of setting policies and procedures on how data is accessed and treated within the data management strategy in the organization.

Example: An analogy – Bake a cake from a recipe book.

Data Governance is the recipe book to bake the cake. The right temperatures and measurements (policies and procedures) with respect to the ingredients (strategy) is data governance.

 

Data management consists of the practices, tools, and architecture to achieve data governance objectives.

Example: An analogy – Bake a cake from a recipe book.

Data management consists of the necessary tools to bake and the right amount of ingredients (data quality) to bake the cake.


Data management

According to DAMA international, the organization for data management professionals, data management is defined as: “the development and execution of architectures, policies and procedures that properly manage the full data life cycle needs of an enterprise.”


Over the years, this definition has changed and since we are producing so much data that it needs to be pieced apart and broken down into narrow functions. The concept of data management has evolved over the years and can be redefined as “Data management consists of the practices, tools and architecture to achieve data governance objectives.”


Data management started way back during 1960s and it has branched out into various sub branches like

  1. Data Governance

  2. Data architecture

  3. Data modeling and design

  4. Database and storage management

  5. Data security

  6. Reference and master data

  7. Data integration and interoperability

  8. Documents and content

  9. Data warehousing and business intelligence

  10. Metadata

  11. Data quality

Data Governance

Data Governance is the key to make data management work. It helps set policies and procedures around these systems in order to ensure the formal management of data assets within an organization.


As the data in an organization increases, there should be a structure and framework in order to manage these systems. Data Governance helps create consistency and standards by using these policies, people, processes, and technology.


There are various benefits to having data governance as the company grows to generate large amounts of data:

  • Organizations in the United States and EU having Data Governance will benefit as it helps set standards on data that can help comply with CCPA, GDPR, etc.

  • Data governance helps identify roles and accountability for data management

  • Data Governance helps identify and improve data access across the organization

  • Data Governance can bridge the gap between the business and technical Metadata to gain better understanding by promoting transparency.

  • Data governance helps provide consistent data quality standards

Do you need Data Management for Data Governance?


Data Management cannot work without data governance. To have a set standard on metadata you need to have people, processes, and technology in place.


There are some organizations when one person would have started some work on data management as the business glossary but if fails since there is no governing body to guide and bring the business glossary for everyone’s use.


When an organization has data classification, architecture, and metadata management methods can be the beginning of data governance. But there is a lot of work that can go into making a data governance program successful.


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