Ten Key Factors that Ensure Successful Data Governance – Data Democratization, Governance, and Security
By Sheila Simpson / April 8, 2024 / No Comments / Amazon AWS Exams, Azure and AWS, Azure Synapse and Its ETL Features, Capabilities Covered by Tools, Microsoft Exams
Ten Key Factors that Ensure Successful Data Governance
Organization in modern times need a data governance that encompasses a wide spectrum of use cases, from business intelligence (BI) to artificial intelligence (AI). With basic principles of accountability, transparency, compliance, and quality, Here are the following factors to consider for the successful data governance program.
1. Clarity: Clarity means having a clear scope of the data governance mandate and objectives. Preplanning and clarity of purpose will help focus the efforts and value of the money spent in terms of scarce resources that are most important for governance activities.
Creating and implementing clear policies and procedures to guide interaction among governance bodies is vital; e.g., What, when, why, where, how and with whom to exchange information with consistency to improve data quality.
2. Type of Data: The application of varied degrees of governance and types of governance depend on the type of data. All data are not the same. Depending on the highest business value, volume, and usage, rigor should be applied accordingly, and priorities are decided accordingly. Start with the most valued data.
3. Sponsorship of leadership: Having continuous sponsorship of leadership from the conceptualization and initiation to the maturity of the governance program is essential for the success of the program—and more so at the time of conceptualization and initiation.
4. Measuring success: Providing continuous metrics to measure the success factors. Depth or width of key parameters can be different depending on the life cycle, maturity of program, industry, and the organization itself. Define milestones and overall progress across data domains.
5. Partnership with business: For sustenance of governance programs, it is important to collaborate with business at all times. Identify business stewardships, making sure that business is part of council and committees and is involved in decisions about data.
6. Stewardship and ownership: Stewardship and ownership is another factor. The information technology department is custodian of data. Clear ownership of business entities and data would put back ownership of data back to business. Without the consent of owners, IT should not change or modify the data, as an owner business would know all issues and plans, and progress of fixes by IT around the Entities owned by the business. Start with organization Chart and Data entities mapping to define the ownership and stewardship.
7. Standardization: Take all opportunities to standardize processes, data, integration, and technology requirements. The business glossary, data dictionary, and definitions are the first steps of standardization. Tracking lineage, metadata, master data management, and version control enterprise solutions are a few such items.
8. Data quality: Data quality is at the core of a successful data governance program. Concrete steps should be taken to improve the data quality. Quality is looked at from multiple perspectives, from users and multiple contexts. The confidence of business users in the quality of data is essential for any data governance program.