Data is an important strategic asset for your organisation. It is just like your customers and finances, it requires proper management.
What is data governance? This article will discuss the meaning of data governance in the enterprise and provide basic implementation steps.
Information About Data Governance And Terms Related To It
Data Governance (DG) refers to a set of data management processes and practices that can be used to help an enterprise manage both its internal and external data flows.
The Data Governance Program covers the business, technical and organizational aspects required to provide high-quality data for businesses. Data owners, data stewards, and other stakeholders need to work together to create a solid DG strategy.
The Data Governance Office is a strategic team that measures success and gathers metrics. The Data Governance Institute defines a “central organizational entity responsible for facilitating coordination and facilitation of Data Governance and/or Stewardship activities for an organization.
Data Stewardship is an operational concept that focuses primarily on the coordination and implementation of policies and procedures. Data Stewards manage critical data assets and issue recommendations as well as develop policies.
Data Quality is a core objective of data governance services. This includes data quality.
Master Data Management (MDM)is the discipline that defines and manages master data assets, which are key data essential to business operations, analytics, and customer and financial data.
Implementing data governance initiatives can be difficult and costly. These are the steps and areas that need special attention.
Step 1- Prepare A Value Statement
The first step in your DG program is to assess the current state of data management and identify data-related issues. This assessment will help to define your goals, identify areas for improvement, and create a roadmap for achieving them.
Step 2- Identify And Engage The Right People
Next, find the right people to manage your data and give them authority to implement the best practices in your company. Consider the roles and responsibilities of your stakeholders to identify your stakeholders.
Step 3- Perform Data Discovery/Classification
Data classification is a key part of data governance strategy. It is essential for:
- Identify data that is regulated by HIPAA, GDPR, CCPA, and PCI.
- Metadata tags can be used to automate DG processes and set up controls.
- Handling eDiscovery by enabling legal holds and archiving
Effective data governance requires that you identify the data you require and its value. It is not an easy task.
Step 4- Develop Policy
The guidelines for data governance are the guidelines that will ensure the organization’s data is properly managed. These are some of the points that a data governance policy covers:
- The purpose, scope, and structure of the data governance program
- Definitions of the roles involved in the creation and use of diverse sets of information
- Guidelines for complying with all applicable laws, regulations, and standards
- Guidelines and principles for data ownership, access and protection, classification and usage, storage, and deletion
- Requirements to conduct data quality audits.
- Relationships to other policies such as data retention or risk management policy, privacy protection policy, or data protection policy
- Supporting documents
Step 5- Implement Policy
It can take several months to implement a data governance strategy. Therefore, it is important, to begin with, the most critical business processes. Consider factors like regulatory requirements, business priorities, and impact on business initiatives when deciding which projects to prioritize.
Step 6- Continuously Evaluate Your Progress
Data governance is an ongoing process. It is not something you can do once in a while. Your DG program should adapt to internal policies, government regulations, and business needs. Regularly assess your technology and processes to ensure they are supporting the program’s goals. If necessary, make adjustments.