Data Governance – An Informative Guide In 6 Easy Points


In the present times, it is necessary to know where one’s data goes. In today’s highly competitive, technologically advanced and rapid-paced market, data governance is no more a choice. Organizations have control over huge quantities of diverse data that has to be used in a disciplinary manner due to non-exploitation of the data subject and maximising value and minimising associated risks and costs.

  1. Definition
  2. Data governance vs Data management
  3. Framework
  4. Goals
  5. Benefits
  6. Principles
  7. What is the role of data governance?
  8. What is a data governance tool?

1) Definition

Data governance is the systematic method whereby an authority in the organization has control, rights and responsibility for data assets and its use. Such control is over the people who use the data, the processes involved in collecting data and the technologies/ technical tools involved in managing said data assets. 

This system of decision-making accountabilities is based on pre-agreed models that state what actions must be done, under what circumstances and using what methods.

2) Data governance vs Data management

One may often confuse terms associated with data and its handling. The layman might find it strenuous to differentiate between the closely related terms of data management and data governance.

At the outset, data governance is not the same as data management. Data governance is essentially a crucial element to data management. Data management process looks into the protection and operations of an organisation’s data for a full lifecycle of the data. On the other hand, Data governance is complementary to data management and ties together other disciplines such as that of data quality, security, reference, base operations and warehousing, etc.

Hence, data governance is a strategy that directs the use of technology and solutions. In contrast, data management is practices used to protect data and its value and uses solutions to achieve tasks.

3) Framework

A well-drafted strategy for data governance includes the benefits that can arise from the efforts put into consistent, secure, and common data handling processes. It should clearly outline how the organizational data will be managed and control. There are certain domains that a government policy should cover such as

  • The quality of data is to be correct and consistent, retaining the same qualities even if it is stored and transferred for viewability on other platforms.
  • The data should be available in a manner that makes it easy for consumption if business functions need it by giving appropriate access.
  • The data should be structured, documented and labelled and categorizing metadata in a comprehensive manner as tools for business users.
  • The security of data must be done based on its sensitivity. It should be aimed at preventing data loss and leakage

Data governance framework can be majorly divided into 3 types. 

1.     Framework should define command and control by designating data handling responsibilities to a few employees like data stewards.

2.     It should be traditional in the designation of responsibility, giving responsibilities to data stewards voluntarily while giving a few crucial data stewards with additional responsibilities.

3.     It should recognize data stewards’ current work, allowing all who rectify or modify data to become stewards of said data.

The framework should address the funding and management aspect, the engagement of the final user of data as well as the council that will help define the framework and execute the same in an organization.

4) Goals

Data governance’s key goal is to regulate the fixed data that is isolated and is under the control of an organization. It aims at establishing various methods and responsibilities to process, equalize, protect and store said data. It should aim to achieve the organisation’s main goals, including risk minimization, laying down rules of data usage, implementing and meeting compliance requirements, enhancing communication within and outside the organization, increasing the value of data, etc.

5) Benefits

  • Access to consistent and uniform data helps make more informed and comprehensive decisions. 
  • Increases the business’ agility and scalability due to the clear structure made in data regulation due to dynamic processes and data.
  • Cost reduction due to central management of data.
  • Increased efficiency of business due to the reusability of data.
  • Increased confidence in the quality of data
  • Improved data regulation compliance.

6) Principles

  • Data must be true and open regarding what impacts data-related decisions.
  • Transparency in dealing with data
  • Data must be auditable.
  • Accountability regarding decisions, processes and controls of data must be defined. This must be using check and balance method.
  • Data Governance should support the standardization of data.
  •  It should support change in management activities.

7) What is the role of data governance?

  • As steering committee: comprises senior management that set overall governance strategy to achieve specific results, supervises work of data stewards, and look into the governing organization’s accountability.
  • As data owner:  They are individuals responsible for specific data domains and have duties such as approving data glossary, ensuring the accuracy of the information, reviewing data management approaches, resolve data issues in cooperation with other data owners, provide inputs for future solutions regarding data handling, etc.
  • As data steward: They are individuals in the team responsible for overseeing the implementation and regulation of policies regarding data. They are accountable for the day-to-day management of data regarding specific subject matters that they specialize in.

8) What is a data governance tool?

Data governance does not just entail a technological solution but uses tools to support a continuing program. The right tool for a specific organization will depend on the need, volume and budget.


Certain tools used in data governance are SAS Data Management, Collibra Governance, Alation, Informatica Axon, Varonis Data Governance Suite, SAP Data Hub, Unifi Data Platform, IBM Data Governance, etc. These tools are provided via various big-time IT vendors such as IBM, SAP, Informatica, Oracle, etc. Most solutions come as a package with data governance and management platforms. However, a product like Alation is available as standalone products.


One must understand that data governance is not a choice but a necessity to understand the market better and build the organization to be more successful. Trusted, well-kept data us the answer to maximize an organization’s capability in today’s date and time.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 


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