5 Steps Data Governance Strategy

To quote IBM, “Data is useful only when it is protected and its context, content, and quality can be trusted”. This outlook defines what data governance looks like.

Data governance is the organization’s ability to set a foundation for their data to be helpful in decision making and business insights. It starts with how data enters their environment and gets utilized and managed throughout its lifecycle, which is also better called metadata management (Data Lineage). The accuracy and trustworthiness with which it gets utilized (Data Accuracy & Quality). And finally, is the data usage, sharing and access are compliant with privacy laws and regulations (Data Compliance).

In essence, a strong data governance foundation helps activate business-ready data by helping to increase transparency, trust, and understanding of data, to accelerate time to insights, while helping address data privacy and regulatory compliance requirements. Data governance ensures data is reliable and used correctly to support business goals.

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What is a data governance strategy?

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A data governance strategy is a comprehensive plan that outlines how an organization will manage, protect, and utilize its data assets. The goal is to ensure that data is accurate, accessible, consistent, and secure while complying with relevant regulations and supporting the organization's overall business objectives. A well-defined data governance strategy encompasses policies, procedures, roles, and responsibilities related to data management.

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5-Step Data Governance Strategy

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From having a comprehensive inventory of all data assets to managing the assets for compliance and privacy, a data governance plan is an exhaustive plan to secure and protect data. Data governance strategies can be summed up into 5 simple steps.

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#1 Data Cataloging

Create a comprehensive inventory of data assets to enhance data discoverability and management.

Steps:

• Identify Data Sources: Catalog all data sources, including databases, data lakes, data warehouses, and external data feeds.

• Metadata Collection: Gather metadata for each data source, including descriptions, data types, formats, and relationships.

• Data Classification: Classify data based on sensitivity, criticality, and usage (e.g., confidential, public, internal).

• Tool Implementation: Implement an AI-based data cataloging tool to automate and manage the cataloging process.

• Access Management: Define and enforce access controls for different data assets to ensure appropriate data usage.

#2 Data Lineage

Track the flow and transformation of data across systems to ensure transparency and traceability.

Steps:

• Mapping Data Flows: Document data movement across various systems, applications, and processes.

• Identify Transformation Steps: Record data transformations, including aggregations, calculations, and data cleansing activities.

• Lineage Visualization: Use data lineage tools to create visual representations of data flows and transformations.

• Integrate with Metadata: Ensure data lineage is integrated with metadata management for comprehensive insights.

• Regular Updates: Continuously update lineage information to reflect changes in data flows and transformations.

#3 Data Accuracy

Ensure that data is correct, consistent, and reliable for decision-making.

Steps:

• Define Accuracy Metrics: Establish clear metrics and KPIs for measuring data accuracy (e.g., error rates, data consistency).

• Data Validation Rules: Implement rules and checks to validate data at the point of entry and throughout its lifecycle.

• Automated Data Monitoring: Deploy tools for automated data monitoring to detect and alert on inaccuracies and anomalies.

• Root Cause Analysis: Investigate and address the root causes of data inaccuracies.

• Data Stewardship: Assign data stewards to oversee data accuracy initiatives and enforce best practices.

#4 Data Quality

Maintain high data quality standards to support business processes and analytics.

Steps:

• Data Quality Dimensions: Define key dimensions of data quality such as completeness, consistency, timeliness, and validity.

• Data Profiling: Conduct regular data profiling to assess the quality of data assets.

• Quality Assessment Tools: Utilize AI-based data quality tools (like OptIQ) to automate quality checks and generate reports.

• Data Cleansing: Implement data cleansing procedures to correct errors and standardize data formats.

• Continuous Improvement: Establish a continuous improvement cycle to regularly review and enhance data quality processes.

#5 Data Compliance

Last but not the least, a data governance strategy should ensure that data management practices comply with relevant laws, regulations, and industry standards.

Steps:

• Regulatory Requirements: Identify and document applicable regulations and compliance requirements (e.g., GDPR, CCPA, HIPAA).

• Compliance Policies: Develop and enforce data compliance policies and procedures.

• Data Privacy Controls: Implement data privacy controls such as encryption, dynamic masking, anonymization, and access restrictions.

• Audit and Monitoring: Conduct regular audits and compliance monitoring to ensure adherence to policies.

• Training and Awareness: Provide ongoing training and awareness programs to educate employees about data compliance obligations.

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Benefits of a Data Governance Strategy

#1 Improved Data Quality‍

Ensures that data is accurate, consistent, and reliable for decision-making.

#2 Enhanced Data Security‍

Protects sensitive data from breaches and unauthorized access.

#3 Regulatory Compliance‍

Helps the organization comply with data protection laws and regulations.

#4 Operational Efficiency‍

Streamlines data management processes, reducing redundancy and errors.

#5 Better Decision Making‍

Provides high-quality data that supports strategic business decisions.

#6 Risk Mitigation‍

Identifies and mitigates data-related risks, protecting the organization from potential issues.

#7 Increased Trust and Accountability‍

Establishes clear roles and responsibilities, fostering trust in data and accountability for its management.

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How OptIQ can help in Data Governance?

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OptIQ data security platform ensures continuous and AI-based data discovery and classification, followed by sensitivity labelling (which are customizable). Any new data store or asset gets automatically discovered and classified with our AI model. Get a complete view of start to finish - that is OptIQ platform offers easy data lineage view with its access control policies and standards. This makes it easy for monitoring any anomalies, and further detection and alerting of unauthorized access.

Keep a check on how your organizations comply with regulatory laws such as GDPR, HIPAA, CCPA — all from one single compliance management dashboard for easy compliance, auditing and generating automatic reports.

Organization’s "need to know my data" comes to a complete circle with OptIQ platform, where every process has been streamlined and scalable with ease. get business ready decision making ability with OptIQ.

Get your personalized one-to-one demo and talk to our experts for your data security needs.