WHY IS DATA GOVERNANCE IMPORTANT?
Poor data governance can also hamper regulatory compliance initiatives. That could cause problems for companies that need to comply with the increasing number of data privacy and protection laws, such as the European Union's GDPR. An enterprise data governance program typically includes the development of common data definitions and standard data formats that are applied in all business systems, boosting data consistency both for business uses and to help meet regulatory requirements.
Without effective data governance, data inconsistencies in different systems might not get resolved. For example, customer names are listed differently in sales and customer service systems. If that isn't addressed, it complicate data integration efforts, causing operational problems in those departments, and create data integrity issues that affect the accuracy of business intelligence, reporting and data science applications. In addition, data errors might not be identified and fixed, further affecting analytics accuracy.
DATA GOVERNANCE KEY SUCCESS FACTORS
Executive Sponsorship and Support
Strong commitment from leadership is essential to prioritize data governance initiatives, allocate necessary resources, and ensure company-wide alignment. Focus on business outcomes that matter the most.
Clear Roles and Responsibilities
Clearly defining roles such as Data Stewards, Data Owners, and Data Custodians ensures accountability and effective management of data assets.
Effective Data Stewardship
Establishing a team of data stewards who are responsible for managing data quality, consistency, and compliance helps enforce governance practices across the organization. Apply varying degrees of governance depending on the data asset.
Comprehensive Data Policies and Standards
Developing clear data governance policies, standards, and guidelines ensures consistency in how data is managed, shared, and protected.
Data Quality Management
Regular monitoring and improvement of data quality through tools and processes such as data cleansing, validation, and profiling is critical for building trust in the data. Prioritize data quality improvement to solve critical business problems.
Cross-Departmental Collaboration
Successful data governance requires collaboration between various departments such as IT, Legal, Compliance, and Business teams to ensure alignment with organizational goals.
Change Management and Communication
Consistent communication and training programs are important to engage stakeholders and ensure adoption of data governance practices across the organization.
Technology Enablement
Implementing data governance tools for metadata management, data lineage, data cataloging, and data quality management can automate and streamline governance processes.
Compliance with Regulations
Aligning governance practices with regulatory requirements (GDPR etc.) ensures legal compliance and reduces the risk of penalties or data breaches.
Measurable Metrics and Continuous Improvement
Establishing KPIs for data governance, such as data quality scores or compliance rates, and regularly reviewing them helps in tracking progress and driving continuous improvement.
Data Standarisation
Take an opportunistic approach to data standardization.
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