What do data governance practices help for? Or we should ask first, do you know where to seek out particular data in your company, or who to contact for it?
Businesses that are still in their early phases understand the importance of data-driven choices in boosting their financial performance. A strong data governance plan may help you save time and money by raising the quality and ease with which teams access data. Following recommended data governance standards can guarantee that you benefit from a policy strategy, but first, what is data governance?
A data governance strategy focuses on establishing who has control and power over data assets within an organization. It includes people, procedures, and technology to handle and protect data assets. We explained data governance definition in detail in a previous article.
Organizations of different types and industries require varying degrees of data governance. It’s especially crucial for firms that adhere to regulatory standards, such as finance and insurance. Organizations must have formal data management procedures to control their data throughout its lifecycle to comply with regulations.
Another aspect of data governance is protecting the company and sensitive consumer data, which should be a top priority for businesses nowadays. Data breaches are becoming increasingly common, with governments passing legislation – as evidenced by HIPAA, GDPR, CCPA, and other privacy laws. A data governance strategy creates management to safeguard data and help organizations comply with regulatory requirements.
Despite the fact that data governance is a major area of concern for many businesses, not all methods deliver the intended benefits. Because of it, you need the best data governance practices for your businesses.
What does it mean to govern data?
Data Governance is the term used to describe a company’s data management, usage, and protection activities. Governing data refers to either all or a part of a firm’s digital and hard copy assets in this context. Indeed, defining what data means to an organization is one of the best practices for data governance.
Consider data governance to be the who, what, when, where, and why of your company’s data.
Why is data governance important?
The value of data is becoming increasingly crucial for businesses. Everywhere you look, digital transformation is a hot topic. You must be able to control your data to profit from your data assets and achieve a successful digital transformation. This implies choosing a data governance framework customized to your organization and future business goals and models. The framework must establish the required data standards for this journey and delegate roles and responsibilities inside your company and within the business ecosystem where it is based.
A well-designed data governance framework will support the business transformation toward operating on a digital platform at many levels within an organization. You should add these components to your data governance practices.
- Management: This will guarantee top management’s commitment to corporate data assets, their value, and their potential impact on the company’s evolving business operations and market opportunities.
- Finance: This will protect accurate and consistent reporting for finance.
- Sales: This will allow accurate knowledge of consumer preferences and behavior for sales and marketing.
- Procurement: Because of the use of data and business ecosystem collaboration, this will help to increase cost reduction and operational efficiency initiatives based on tapping into data and integrating with the business ecosystem.
- Production: This will be necessary for production use in putting automation into action.
- Legal: This will be the only option for legal and compliance as new regulation standards emerge.
Data inconsistencies in different systems across an organization may go unresolved because of ineffective data governance. For instance, customer names, for example, might be presented differently in sales, logistics, and customer service systems. Integrating data from various sources and formats into single reports and dashboards may be complex. These changes could create data integrity issues that harm the effectiveness of business intelligence (BI), enterprise reporting, and analytics tools. Furthermore, incorrect data might go unnoticed and unaddressed, which will impact BI and analytics accuracy.
Data governance framework
Data management is the process of organizing, understanding, and leveraging data to meet organizational goals. A data governance framework can help ensure that your organization follows best practices for collecting, managing, securing, and storing data.
To assist you to figure out what a framework should include, DAMA imagines data management as a wheel with data governance as the center from which ten specific data management skills radiate:
- Data architecture: Overall, the data structure and data-related resources are essential components of the company architecture.
- Data modeling and design: Data governance is for analysis, design, building, testing, and maintenance.
- Data storage and operations: Storing structured physical data assets, including deployment and maintenance.
- Data security: Data governance ensures privacy, confidentiality, and appropriate access.
- Data integration and interoperability: Data governance is for acquisition, extraction, transformation, movement, delivery, replication, federation, virtualization, and operational support.
- Documents and content: Data governance is the practice of managing, archiving, indexing, and providing access to data from non-structured sources.
- Reference and master data: Standardization of data definition and usage and shared data reduction to improve data quality and reduce redundancy.
- Data warehousing and business intelligence (BI): Data management analyzes data and gives access to decision support data for reporting and analysis.
- Metadata: Metadata is a term that refers to any information associated with a digital item, such as title and author. It collects, classifies, keeps, integrates, controls, manages, and delivers metadata.
- Data quality: Defining, tracking, and ensuring data integrity and quality are essential aspects of data quality.
When developing data governance practices, businesses should consider each preceding aspect: collecting, managing, archiving, and utilizing data.
The Business Application Research Center (BARC) cautions that it is not a “grand slam.” Data governance can erode participants’ trust and interest over time as a very complicated, continuous effort. BARC advises starting with a minor or application-specific prototype project and gradually expanding throughout the firm based on learnings.
BARC developed the following procedure to aid in the implementation of a successful program:
- Define objectives and analyze the advantages.
- Examine the existing condition and delta changes.
- Create a route map by combining the product plan and feature roadmaps.
- Convince stakeholders and obtain funding for the project.
- Develop and implement a data governance program.
- Implement the data governance program.
- Monitor and control.
What are data governance best practices?
We gathered the best data governance practices for your organization. A data governance strategy is only as effective as the company that uses it. You should follow rigorous data governance procedures to get the most out of a data governance plan. We all know the most effective methods in creating a data governance policy.
Check out our top six data governance practices to get you started collecting, storing, and utilizing your data more successfully.
Begin small and work your way up to the big picture
People, procedures, and technology are all critical aspects of data management. Keep all three elements in mind when developing and executing your data plan. However, you don’t have to improve all three areas simultaneously.
Start with the essential components and work your way up to the final image. Begin with people, progress to the procedure, and conclude with technology. Before any component may proceed, it must build on top of the preceding ones for the whole data governance plan to be well-rounded.
The process won’t work without the correct individuals. If the people and procedures in your company aren’t managing your data as you intended, no cutting-edge technology can suddenly repair it.
Before developing a process, search for and hire the proper people. Use these data specialists to help you establish a data governance strategy. After that, you may use whatever technology best automates your processes and gets the work done correctly and swiftly.
Get business stakeholders on board
You need top-level executive buy-in to develop a data governance strategy, but getting the go-ahead is only the beginning. You also want to Engage your audience and encourage them to take action so that your data governance plan is implemented throughout your business.
The ideal approach to get executives interested in your data governance strategy is to make a business case for it. You demonstrate leadership by creating a business case, demonstrating the specific advantages they might anticipate from a data governance approach.
Define data governance team roles
When roles, responsibilities, and ownership structures are well-defined, data governance methods are more likely to be effective. The foundation for any data governance strategy is the creation of team members’ data governance functions across your company.
Data governance practices aim to improve data quality and collaboration across departments. It necessitates input and data ownership from all levels of the company. While each organization’s data governance framework will appear unique, there are undoubtedly vital players that should be included in your structure:
- Data governance council or board: The data governance team is responsible for the overall governance plan. They provide strategic input as part of the data governance strategy. This team also frequently prioritizes elements of the plan and approves new policies.
- Tactical team members: The tactical data governance team members create data governance policies and approaches based on the council’s recommendations. They develop the data processes and rules, which are later approved by the data governance council.
- Owners: The people in charge of particular data are known as data owners. This is the person to reach out to when someone requests information. For example, if you need sales data from last month, you would contact the sales data owner.
- Data users: The team members frequently input and utilize data as part of their regular job duties.
To measure progress, use metrics
It is critical to track progress and display the effectiveness of your data governance strategy, just as it would be with any other shift. Once you’ve acquired executive buy-in for your business case, you’ll need evidence to support each stage of your transition. Prepare ahead of time to establish metrics before implementing data policies so that you can build a baseline based on your current data management strategies.
Using the original metrics regularly allows you to track your development. This demonstrates how far you’ve come, but it also serves as a checkpoint to make sure your data governance best practices are working in practice rather than just on paper. A plan that works perfectly, in theory, may fail to work in reality. It’s critical to keep an eye on your data governance strategy and remain open to changes and improvements.
Encourage open and frequent communication
Whether you’re just getting started with a data governance initiative or have been using one for some time, staying in touch early and often is critical, communicating regularly and effectively allows you to illustrate the strategy’s impact—from highlighting triumphs to re-organizing after a failure.
The Chief Data Officer (CDO) or equivalent role should be given to an executive team member, such as the CIO or CDO, to take on the leadership of the data governance program. These executives are in charge of keeping track of the organization’s governance standards across teams and departments. Team leaders and data owners may provide regular progress updates to the senior management. The executive team member then delivers essential information to the rest of the leadership team and the entire organization.
Data governance is not a project; see it as a method
Creating a data governance plan can feel like starting a new initiative. You might be inclined to form a group to work on the project while the rest of the organization waits for you to finish it. This is when many organizations’ data governance plans come to a halt.
It is not enough to implement a data governance strategy once and then declare it finished. There is no defined ending date or conclusion. Instead, it’s a continuing practice added as part of your organization’s standard policy. Data governance becomes an aspect of everyday life at your company in the same way dress codes or leaves policies are.
