What Is Data Governance : Definition, Applications, and Best Practices for 2021

Data is a valuable commodity in today’s business world, whether it’s a major corporation or a small business run out of your home. Data is everywhere, and it must be collected, structured, managed, and overseen to maximize its effectiveness. In other words — data needs governance.

Let’s explore the concept of data governance and what it means. In addition to establishing a data governance definition, we will look at data governance standards, the overall data governance process, examples, implementations, and other aspects.

We begin by defining data governance.

Data Governance: A Definition

Actually, before we define data governance, let’s establish what we mean by “data.” In terms of IT, data is information that is stored and processed on a digital device like a server, database, computer, tablet, or smartphone.

Now that we have dealt with the basics, we move on to explaining data governance. Data governance is an umbrella term for collecting practices, policies, processes, roles, standards, and metrics dedicated to helping an organization realize its goals using efficient and practical information. Data governance manages the availability, integrity, security, and usability of all data residing in enterprise systems.

Post Graduate Program in Data Engineering

Your Gateway To Becoming a Data Engineering ExpertView Course
Post Graduate Program in Data Engineering

Data governance should not be confused with data management. Data management entails overseeing an organization’s entire data lifecycle needs. Data governance is a core component of data management — tying together nine additional disciplines, including data security, data cleansing, data quality, database operations, data warehousing, and more. In more straightforward terms, data governance creates the policies and procedures regarding data, while data management enacts those procedures to gather and use the data for decision-making.

The Goals of Data Governance

Our data-heavy world makes a solid data governance program mandatory. It’s no exaggeration to say that any business today that doesn’t have a data governance strategy puts itself at unnecessary risk. Here are the goals of a healthy, effective data governance model:

  • Ensure the company can make confident, consistent business decisions based on reliable data
  • Boost data security by establishing data ownership parameters and related responsibilities
  • Monetize the data to increase profits
  • Meet regulatory requirements and avoid punitive measures such as fines by documenting data asset lineage and data-related access controls, including implementing compliance requirements
  • Establish consistent rules for data use
  • Reduce overhead costs
  • Increase the overall value of data
  • Improve both internal and external communication

The Benefits of Data Governance

The line between goals and benefits gets blurred often because met goals ultimately turn into advantages. At the risk of redundancy, let’s list the benefits of data governance. There may be enough distinction to justify it.

  • The organization’s data increases in value
  • There are clear and improved standards for data policies, systems, and procedures
  • The company achieves greater transparency regarding data-related activities
  • The business’s revenue grows, and costs shrink
  • The organization gains a platform that meets the demands of the many government regulations in force today. Businesses must adhere to the European Union’s General Data Protection Regulation (GDPR), the United States’ HIPAA (Health Insurance Portability and Accountability Act), and other industry requirements like the PCI DSS (Payment Card Industry Data Security Standards)
  • Since valuable data fuels commercial success in today’s digital world, this gives the company an edge over the competition
  • A good data governance platform facilitates the use of big data analytics
  • Data governance improves cybersecurity, protecting corporate assets from theft and tampering

Data Governance Challenges

Although the benefits of enterprise data governance appear self-evident, some organizations push back on the concept. Here are a few challenges facing data governance:

  • Budgetary Concerns.

It isn’t easy to convince the people who hold the purse strings that they should allocate financial resources to deal with something the organization is not immediately facing.

  • Dichotomy Between Flexibility and Standardization.

It isn’t easy finding the right balance between adhering to a set of governance standards and having flexibility. Where do you draw the line?

  • Business Culture Clashes.

Data governance relies on an open corporate culture, which entails accepting new ideas and methodologies.

  • Perceived Difficulty.

Skeptics within an organization may assume data governance is difficult to implement and refuse data governance implementation outright.

A Data Governance Framework

Data governance frameworks consist of sets of data rules, processes, technologies, and organizational role delegations, designed to bring the company together and ensure everyone is heading in the same direction regarding data.

A good data governance framework also has a well-defined mission statement, goals, success definition, responsibility delegation, and accountability for different functions within the program. The framework should be documented and shared with every appropriate member of the organization.

Data Governance Principles

There are eight guiding principles of data organization:

  1. Everyone participating in data governance must have honesty and integrity in dealing with each other. This honesty includes being forthcoming in discussing every aspect of data-related decisions.
  2. Data governance requires transparency, especially regarding how and when decisions were made and controls implemented.
  3. All data-related controls, decisions, and processes must be auditable and include documentation.
  4. Define who is responsible for data-related, cross-functional controls, decisions, and processes.
  5. Define who is answerable for stewardship activities controlled by groups of data stewards or individual contributors.
  6. Programs must define accountabilities in a way that provides checks and balances between the technology and business teams and between those who collect and create information, the information users, information managers, and individuals who introduce compliance requirements and standards.
  7. The data governance program must introduce and support enterprise data standardization.
  8. The program must support reactive and proactive change management activities established for reference data values and the use and structure of metadata and master data.

Data Governance Roles and Responsibilities

There are four chief roles in data governance. They include:

  • Chief Data Officer.

This role is the data governance leader, who is responsible for overseeing the process. Duties include securing funding and staffing and aiding in the process’s setup and monitoring.

  • Committee Members.

The committee sets data governance policies and procedures. They set the rules for data usage and who can access it. The committee also resolves disputes.

  • Data Stewards.

Whereas the committee sets policies, the data stewards carry them out. They oversee data sets and keep them in proper order. Data stewards also train new staff.

  • Data Owners.

Data owners assume direct responsibility for the organization’s data. They protect the data and maintain its quality as a business asset. Data owners are always on the teams that use the data.

Data Governance Best Practices for 2021

Much like data governance frameworks, there are various sets of data governance practices out there. Here is a brief list of the ten best practices for the next twelve months.

  • Set Specific, Clear, and Measurable Goals.

Establishing well-defined goals is the only way you can tell if you're making progress. When you meet the goal, note the achievement, then prepare for the next one.

  • Start Off Small.

Instead of taking on a massive challenge, break the operation into smaller pieces, and do them one at a time, making gradual progress. Don’t try to solve all of the world’s problems on your first go!

  • Clearly Identify Responsibilities and Establish Roles.

You don’t want duplication of effort or people stepping on each other’s territory.

  • Tag and Classify All Data.

Enforce metadata standards that help you meet your business goals and permit reusable data.

  • Automate, Automate, Automate!

Try automating as much of the framework as you can, including approval processes, data requests, permission requests, and workflows.

  • Don’t Forget Unstructured Data.

There is a plethora of unstructured data sitting around. Have a plan on how to handle data that resides in folders, files, and shares.

  • Minimize the Impact on Individuals and Teams.

Data governance is not the chief responsibility of the entire company. While everyone should handle data responsibly, ultimately, data governance falls on the designated team.

  • Map Your Infrastructure, Architecture, and Tools.

Get an understanding of where the data governance framework fits in the overall IT landscape.

  • Communicate Often.

So many problems get solved (or prevented entirely) when people and teams communicate clearly and often.

  • Educate Your Stakeholders.

People are more likely to accept risks and allocate resources when they know what they’re supporting.

Add Another Star to Your Performance Evaluation

Learn from industry experts for FREEStart Learning
Add Another Star to Your Performance Evaluation

Data Governance Tools

Although data governance is an ongoing process, there are many tools available to make the job easier. Here is a small sample of popular data governance tools:

  • IBM Data Governance. This tool leverages the concept of machine learning to collect and curate various data assets. Teams can use the integrated data catalog to find, curate, prepare, analyze, and eventually share data.
  • Alation. This tool is an enterprise data catalog that supports self-service analytics. Alation automatically indexes data by source.
  • Collibra Governance. This tool is an enterprise-wide solution that helps you take care of the “Automate!” best practice listed above. Collibra automates many governance and stewardship jobs. It comes with a business glossary, data helpdesk, and policy manager.
  • SAS Data Management. SAS Data Management is built on the SAS platform, and provides a role-based graphic user interface (GUI) for managing processes. This tool includes lineage visualization, SAS and third-party metadata management, and an integrated business glossary.

Data Governance Applications

Data governance is a helpful tool that improves the digital platforms of businesses and organizations of all kinds.

  • Finance.

Data governance plays a crucial role in safeguarding accurate and consistent financial reports.

  • Legal Matters.

No company today can escape digital standards and practices, and data governance helps businesses achieve compliance.

  • Sales.

Data governance provides trusted insights into customer behavior and habits.

  • Production/Manufacturing.

Artificial intelligence and machine learning bring a new level of automation to the industrial sector. Data governance makes deployment more manageable and safer.

  • Procurement.

Supply chains have been severely impacted, no thanks to the COVID pandemic. Data governance brings operational efficiency and cost reduction to the process by providing actionable data and fostering greater business collaboration in any crisis.

Want to begin your career as a Data Engineer? Check out the Data Engineering Courses and get certified.

Interested in Becoming a Data Engineer?

Data engineering and data governance work together, and the world needs more data engineers. Simplilearn can train you in a career in data engineering though our Post Graduate Program in Data Engineering.

Conducted in partnership with Purdue University and collaboration with IBM, this certification course is ideal for professionals. The program, aligned with AWS and Azure certifications, covers critical topics like the Hadoop framework, data processing using Spark, data pipelines with Kafka, and big data on AWS and Azure cloud infrastructures.

According to Payscale, data engineers in the U.S. can earn an average yearly salary of USD 92,407. Payscale also reports that data engineers in India can earn an annual median salary of ₹ 838,090.

Check out Simplilearn’s courses today, and get a head start on finding a great data engineering career!

About the Author

SimplilearnSimplilearn

Simplilearn is one of the world’s leading providers of online training for Digital Marketing, Cloud Computing, Project Management, Data Science, IT, Software Development, and many other emerging technologies.

View More
  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.