offers data science lesson videos made simple!

Sign up or log in to Magoosh Data Science.

What Every Data Scientist Needs to Know about Data Governance

data governance -magoosh

The complete organization of the usability, integrity, security and availability of data employed in an enterprise is termed as Data Governance (DG). An efficient data governance program involves a pre-defined set of procedures, a ruling council or body and a roadmap to accomplish those goals and procedures.

Questions about Data Governance

Why governance?

This paragraph lays emphasis on the need for data governance. The Data Governance Institute defines this 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.” In simple terms, processes and framework involved in management of data assets is combined in data governance. As an example, need arises for organizations to be aware of customer demands within different departments to be able to define their data and manage it overall. Customers occupy multiple points in any organization because of which the management of such interactions persistently is of utmost importance.

Achieving insights from customers is just the beginning. Such concepts of data governance exist for all categories of data, be it analytical, transactional or operational. The method of information management has a great effect on data usage as well. Cases, when given untrustworthy information, gaining complete benefit from its use are almost impossible.

Who needs data governance?

Organizations these days must have a proper data governance system to secure their sensitive information from wrong hands. Huge companies, such as those of healthcare and banking along with other regulated industries, have the most confidential information at stake. The amount of risk with such data usually compensates the efforts and expense drained on data governance.

What properties of data require governance?

Data governance is mostly driven by legal and regulatory requirements; although a governance rule can also be any policy that the organization wants to practice. This governance states where specific categories of data will be stored and it codes methods of data protection majorly like password strength or encryption. It also dictates the backing-up of data and defines access rights and the destruction of archived data after a suitable time. Governance objectives can also be set up by organizations to monitor data quality and accessing silos containing certain isolated data.

Is data governance a technology?

Data governance in simple terms points to the plan used for accurately controlling and managing data. Various commercial products are also present to assist with tasks related to information management such as enforcement of business rules or validation of data quality.

What are the Business benefits?

In most analytics projects and business intelligence, an emphasis is laid on a specific data set in the organization and the various project stakeholders take this governance within the analytics framework as enough. However, data governance needs to encircle all data assets of the organization to be able to provide a compiled view of information and management of disparities along with data quality issues as they arise.

Data focused and technologically-advanced governance affects business largely. Deep insights into products, suppliers, customer lists, demographics, partners, etc. provide a clearer vision. Taking proper use of this advantage helps in identification of potential opportunities, effective data management, and high performance. Some business benefits practically encountered are:

  • Easier management of data quality resulting in quicker and deeper insights.
  • Better coordination within departments.
  • Generation of automated analytics, highly proficient data structures along with broader insights in business.

The main benefit organizations achieve from governance done right is that of highly efficient information visibility and access resulting in proficient analytics.

How to Implement a data governance strategy?

Considerations related to people, processes, and technologies are a must for a holistic approach towards data governance.


The foremost step for successful implementation of data governance programs is the creation of a team and the assigning of responsibilities for data assets to individuals in the organization. These data owners are then answerable for the resulting data quality and must support data quality processes and initiatives for the company.

It is the duty of the data team to make sure data governance initiatives are in accordance with business requirements and needs. Data governance gives an impression of being aligned with the IT department of the organization but in reality, it is required to be closely linked to the business itself to enable stakeholders secure access to all the information they need to make proper data-dependent decisions. Failing to do this, your organization will have an efficient data strategy as the end product in contrast to a big data governance strategy which was required.


Furthermore, development of data processes is required. This consists of definitions of data storage, its movement, changes, access, and security. Monitoring, audit, and control processes also need to be worked on mainly for compliance in highly regulated industries.

Laying emphasis on data governance on the needs of business and its processes is utmost important and the resulting processes must be able to reflect it.


Technology alone cannot complete data governance and so, organizations must implement solutions to help with better governance initiatives. Instances can be taken such as technology to enforce business rules, data quality processes and monitoring and reporting software.

Components of Data Governance

Data stewardship

Data steward makes it essential to be accountable or different sections of data. The most important objective of this type of data governance is the assurance of data quality with considerations to accessibility, updating, accuracy and completeness.

Actual implementations of data governance are guided by teams formed of data stewards. Such teams consist of business analysts, database administrators and businessmen who coordinate with persons in the overall data lifecycle to make sure a company’s policies regarding data governance are being put in practice.

Data quality

Most of the data governance activities are driven by the force of data quality. Some crucial must-haves of successful initiatives are completeness, consistency and accuracy. The most common element in data quality initiative is data scrubbing, also termed as data cleansing, which correlates, identifies and eradicates duplicates of a particular data point. This process is responsible for the numerous ways of describing the same customer or product. To assist organizations attain best data quality various software types are included such as data linking tools, data mining tools, data differencing utilities and version control, workflow, and project management systems.

Master data management

Data governance is linked to most of the aspects of data management, but the one process it excels in is master data management (MDM). This particular subject provides a master reference to facilitate persistent data usage, especially in large organizations.

Metadata repositories consisting of information about other data is frequently used for building cross-group reference data in MDM programs. Such MDM systems lay major emphasis on customer and product data. In accordance with data governance, master data management projects get captured in controversies in the organizations itself as various lines of business and varied product groups encourage divergent opinions on the presentation methodology of data.

The extent of master data management began expanding large scale as more externally generated data was included in corporate computing, often gathered from the cloud or via the web. This data is unstructured and varies from the structured relational data which was initially the focus of MDM. As a result graph data stores supporting descriptions of highly complex data interrelationships were being utilized by MDM tools. Gradual flattening of corporate organization structures and consistent advancements have increased the focus on a more flexible approach to data governance with coordination to big bang and waterfall-style projects.

Data governance use cases

Data governance is a very important aspect of business process management, financial and regulatory compliance, legacy modernization, mergers and acquisitions, business intelligence applications, data lakes, credit risk management and data warehouses. With upcoming technological advancements and expansion of data usage, data governance is gaining wider applications. As a result of frequent high-profile data breaches, a central part of data governance has arisen as data security.

Numerous demands for data privacy have made data governance programs to also consist of data privacy audits and data protection. The European Union’s (EU’s) directive concerning General Data Protection Regulation (GDPR) best portrays a use case for data governance.

Comments are closed.

Magoosh blog comment policy: To create the best experience for our readers, we will only approve comments that are relevant to the article, general enough to be helpful to other students, concise, and well-written! 😄 Due to the high volume of comments across all of our blogs, we cannot promise that all comments will receive responses from our instructors.

We highly encourage students to help each other out and respond to other students' comments if you can!

If you are a Premium Magoosh student and would like more personalized service from our instructors, you can use the Help tab on the Magoosh dashboard. Thanks!