Guide: Data Governance

Data Governance Best Practices Explained

What is Data Governance?

Data is among an organization’s most significant assets, and how well it gets managed in terms of quality, management, and ownership will determine the company’s overall performance to a considerable part.

 

Data governance is a broad term referring to the rules and procedures that govern data management inside an organization. Good data governance ensures the safety and security of data. In fact, a large part of data governance is data protection. It follows data governance guidelines that guarantee that the information supplied is accurate, reliable, documented, protected, evaluated, and controlled.

Good Data Governance and Data Governance Guidelines

When it comes to data governance best practices, others who have gone through the various processes and templates may teach you a lot. While each business is unique, you will need to tailor your data governance policies to fit your workflow. This endeavor can be overwhelming, even to the most organized companies. Fortunately, there is no need to start from scratch.

Before diving into the best data governance practices, one should know what data governance is. This article will discuss the following:

This is part of our essential guide to data governance.

Improved data analytics leads to better decision-making and operations management due to data governance. It also helps to avoid inconsistencies or inaccuracies in data.

 

Below are five of the most important keys to making Data Governance Guidelines.

 

  • Rules and Regulations: Every successful data governance process will need to design and follow consistent rules and regulations to secure the data and ensure it gets managed following all necessary external requirements.
  • Accountability: A high level of responsibility gets required for any successful data governance strategy. If no one takes responsibility for data governance, it will be meaningless, unpleasant, and ineffective.

Data Quality Standards: To increase corporate data quality, the data governance council should collaborate with the data steward to develop an agreed set of data quality standards. These guidelines will ensure that the data quality gets assessed and documented.

Learn more in our dedicated data quality guide.

  • Administration of Data: The key to ensuring proper accountability for the company’s data is to recruit a specialized data administrator. This member will respond to the data council and be responsible for implementing and ensuring compliance with the council’s data rules and regulations. Another aspect of administering the data is to ensure that you have good data lineage (that data is properly tracked).
  • Transparency: To the greatest extent possible, all data governance mechanisms implemented within the company should be transparent. This transparency will protect the organization from a data breach and better understand how data gets used across the company.

Data Governance Framework

An effective data governance system has many characteristics. To start, it should include data management rules, regulations, processes, and frameworks. Here is what you need to know about data governance frameworks.

 

A data governance framework is a model for managing organizational data that is collaborative. Around data production and modification, the framework or system can provide soft or firm guidelines. Companies frequently form a data governance team to oversee proper data usage, data quality, and policy adherence.

 

A data governance framework unifies data collection, storage, and use by providing a single set of rules and procedures. The framework makes it simple to streamline and scale basic governance practices, allowing you to maintain compliance, distribute data, and enhance collaboration no matter how quickly data grows.

 

You should include the following in every Data Governance Framework.

 

  • A mission statement is a declaration of purpose that is increasingly widely used and subjected toward a goal.
  • Objectives will serve as a guideline towards achieving the company’s goal for data governance.
  • Benchmarks that you will use to evaluate the objectives.
  • Roles and duties for various aspects of data governance that are clearly defined.

 

Data governance’s ultimate purpose is to maximize the benefits of data assets by collecting critical opportunities to use them while reducing the risks of exposing them. Proper execution will allow a company to make wiser, quick, and more effective decisions.

 

Now that the basics of Data Governance and Data Governance Framework have been discussed, it is time to discuss the best practices of Data Governance.

Best Practices of Data Governance

While each company is unique, certain general best practices are to follow when ready to go forward.

Baby Steps

Consider the bigger idea, but begin with modest steps. People, procedures, and technology all play a role in data governance. To begin constructing the large picture, start with the people, then the systems, and eventually, the equipment.

 

It is difficult to create the successful processes required for the technological implementation of data governance without the right people. If a company finds or recruits the proper people for the solution, they will help construct the procedures and get the company’s technology to get the job done right.

Create a Business Case

When it comes to developing a data governance framework, getting buy-in and sponsorship from those involved in the process is critical as buy-in simply will not be enough to guarantee success.

 

Create a compelling business case by identifying the opportunities and advantages that data quality will bring to the organization and the benefits you can realize, such as additional profits, improved customer experience, and productivity.

 

Although most executives agree that poor data quality and management are issues, data governance initiatives might fall short if management does not commit to the change.

Metrics, Metrics, Metrics

If you cannot quantify it, you will not achieve it.

 

Before making any change, the company should establish a baseline to justify the outcomes. Collect those measures as soon as possible, and keep track of each step along the route. These measurements should demonstrate overall changes over time and serve as benchmarks to confirm that the processes are feasible and effective.

Endurance over Power

Data Governance is a Long Race rather than a dash. There is no such thing as excellent data governance. You do not just put together a team to start a project and then count your lucky stars.

 

Ensure to promote data governance as a long-term investment rather than a one-time activity when implementing it. There is a start and an end date for a project. On the other hand, data governance is a continuous, evolving procedure with several sub-projects and benchmarks.

 

Begin with modest pilots and integrate the lessons learned within the organization to inform larger, more extensive projects. While data governance initiatives might span for years, individual projects should not exceed three months. To weave more significant transformation into the business, incorporate smaller projects into the long-term data governance strategy.

Communication is Key

Early and often communication is essential. Engaging no matter where you and the business are in the data governance program and processes is critical. Consistent and effective communication is necessary to demonstrate the program’s effectiveness, acknowledge successes, and honestly believe shortcomings. Make a list of the concerned parties, keep it up to date, and make sure communications are easy to find and understand. This communication will ensure that the relevant people access the information they require while avoiding surprises and facilitating development.

Determine the Jobs and Tasks that are Related

Data governance necessitates cross-departmental collaboration and outputs. Every data governance program needs clearly defined roles, and it is critical to designate levels of responsibility across the company. Understanding who has responsibility and authority will aid in the socialization of the data governance program and the creation of an intelligent framework for tackling data initiatives as a cohesive unit.

Data Governance Council

A data governance council is a regulatory body in charge of the data governance program’s strategic direction, project and initiative prioritization, and authorization of organization-wide data guidelines and regulations.

Data Governance Board

A data governance board is a team tasked to design processes and regulations for treating data as a strategic asset in a business.

Data Managers

A data manager designs database systems to fulfill an organization’s data needs, whether that data is being collected or has already gotten collected.

Data Owners

A data owner is a person who is in charge of a data asset.

Data Stewards

A data steward is accountable for maintaining the quality of data pieces, such as content and metadata, by applying the data governance methods. In many cases, this team is in charge of the data catalog (and within it or outside of it, the business glossary).

Data Users

As part of its operation, data users are team members directly responsible for inputting and using data. They can immediately access and examine integrated datasets at the unit record level for statistics and research reasons.

Conclusion

People, processes, and technology all play a role in data governance. The ultimate goal of data governance is to maximize the benefits of data assets by gathering important chances to use them while minimizing the risks of exposing them. An effective program will give participants a clear grasp of where data comes from and who maintains what. Its proper execution will enhance a company’s ability to make wiser, faster, and more effective decisions.

Agile Data Governance with Satori

Satori helps you with DataSecOps for your modern data stack. This includes continuous sensitive data discovery, integration with existing data governance tools to make data governance more efficient and immediate, as well as means to streamline access to sensitive data and create security policies that are independent of the specific data infrastructure you’re using.