Wednesday, November 8, 2023

Data Governance - Intelligent Data Quality

Data is the business asset for every organisation which is audited and protected. To gain in their business, it becomes very urgent for every organization to make sure that good quality data is available to everyone who needs it.

Note: Poor data governance regulations include many sources of truth, poorly defined KPIs, poor data quality, and an abundance of other issues.


Data Governance maintains data integrity and compliance, while Data Analysts give insights for decision-making; both positions interact to offer reliable and applicable insights. Data quality is measured on six critical factors, each of which is equally important, and they are: 

  1. Data Quality is ensuring that data is correct, consistent, and devoid of "noise" that might obstruct usage and analysis. 
  2. Data Availability entails making data available and easily consumable by the business operations that require it. 
  3. Data Usability entails ensuring that data is properly formatted, documented, and labelled, that it can be easily searched and retrieved, and that it is compatible with technologies used by business users.
  4. Data Integrity is the process of ensuring that data keeps its important properties even when it is stored, processed, transmitted, and accessed across several systems.
  5. Data Security entails categorizing data based on its sensitivity and developing protocols for protecting information and avoiding data loss and leakage.

As we know, bad data can come from every area in our organization under diverse forms from business departments, sales, marketing, or engineering. Data quality initiatives are generally centered on improving these metrics so that data will promote optimal performance of business systems and support user faith in the systems’ reliability.  


It involves the duties that people must carry out, the standards that they must adhere to, and the technology that supports them throughout the data life cycle. Data governance is the act of forming internal standards—data policies—that govern how data is gathered, preserved, processed, and disposed of. 

Data Governance Strategy

A data governance strategy is a framework used by organizations to manage, safeguard, and assure the quality and compliance of their data assets. It is critical for ensuring data integrity, privacy, and security, as well as assisting organizations in making educated decisions based on accurate and trustworthy data. The following are critical elements of a data governance strategy:

  1. Executive Sponsorship: Data governance requires support from top management to allocate resources and enforce data policies. Establish an executive sponsor or steering committee responsible for overseeing the strategy.
  2. Data Governance Framework: Develop a framework that defines the roles, responsibilities, and processes for data governance. This should include data owners, stewards, and users.
  3. Data Governance Policies: Create clear and comprehensive data governance policies that outline data classification, access controls, data retention, and data quality standards.
  4. Data Inventory: Identify and catalog all data assets within your organization. This includes structured and unstructured data, databases, spreadsheets, files, and data stored in various systems.
  5. Data Stewardship: Assign data stewards responsible for data quality, metadata management, and ensuring that data is used appropriately.
  6. Data Quality Management: Implement data quality tools and processes to monitor and improve data quality. Define data quality metrics and thresholds.
  7. Data Security: Establish security measures to protect sensitive and confidential data. This includes encryption, access controls, and compliance with relevant data protection regulations (e.g., GDPR, HIPAA).
  8. Data Privacy: Ensure compliance with data privacy regulations, such as GDPR or CCPA. Develop policies and procedures for handling personal data and obtaining consent where necessary.
  9. Data Classification: Categorize data based on its sensitivity, value, and regulatory requirements. This helps determine appropriate access controls and security measures.
  10. Data Lifecycle Management: Define the stages of data from creation to archiving and deletion. Implement data retention and disposal policies.
  11. Data Catalog and Metadata Management: Create a data catalog that provides a centralized repository of data assets and their metadata. Metadata should include data lineage, definitions, and business context.
  12. Data Governance Tools: Choose and implement data governance tools and platforms that facilitate data management, data quality, and metadata management.
  13. Data Governance Training and Awareness: Train employees on data governance principles and policies to ensure understanding and compliance.
  14. Data Governance Metrics and KPIs: Define key performance indicators (KPIs) to measure the effectiveness of your data governance program. Track metrics related to data quality, compliance, and security.
  15. Data Governance Communication: Establish a communication plan to ensure that all stakeholders are informed about data governance initiatives and changes.
  16. Data Governance Continuous Improvement: Regularly review and update your data governance strategy to adapt to changing business needs and evolving data regulations.
  17. Audit and Compliance: Conduct regular audits to ensure that data governance policies and practices are being followed and maintain compliance with relevant regulations.
  18. Data Governance Culture: Foster a culture of data stewardship and responsibility throughout the organization to ensure that everyone understands the importance of data governance.
  19. Data Governance Steering Committee: Create a committee responsible for making decisions, resolving issues, and overseeing the data governance program.
  20. Data Governance Roadmap: Develop a roadmap that outlines the phased implementation of your data governance strategy.


Remember that a data governance strategy is an ongoing effort that requires continuous attention and improvement. It is crucial for organizations to adapt and evolve their data governance practices meeting changing data management challenges and compliance requirements.


Data Governance Roles


Effective data governance requires the assignment of specific roles and responsibilities to individuals or teams within an organization. These roles ensure that data is managed, protected, and used in a way that aligns with organizational objectives and complies with relevant regulations. Here are some common data governance roles and their responsibilities:

  1. Data Steward - Responsibilities:
    1. Manages and maintains data assets.
    2. Ensures data quality and accuracy.
    3. Defines and enforces data standards and policies.
    4. Monitors data usage and compliance.
    5. Collaborates with data owners and users.
  2. Data Owner - Responsibilities:
    1. Accountable for specific data sets or domains.
    2. Determines who has access to the data.
    3. Ensures data is used in alignment with business goals.
    4. Collaborates with data stewards on data quality.
  3. Data Custodian - Responsibilities:
    1. Manages the technical aspects of data storage and access.
    2. Implements data security and access controls.
    3. Maintains data infrastructure and databases.
    4. Collaborates with data stewards and data owners.
  4. Chief Data Officer (CDO) - Responsibilities:
    1. Sets the overall data strategy for the organization.
    2. Ensures data governance policies and practices are in place.
    3. Manages data-related risks and compliance.
    4. Drives data-driven decision-making.
  5. Data Governance Manager/Director - Responsibilities:
    1. Oversees the data governance program.
    2. Develops and enforces data governance policies.
    3. Coordinates with data stewards and data owners.
    4. Reports to senior management on data governance progress.
  6. Data Quality Analyst - Responsibilities:
    1. Monitors and assesses data quality.
    2. Defines data quality metrics and thresholds.
    3. Investigates and resolves data quality issues.
    4. Collaborates with data stewards and data owners.
  7. Data Compliance Officer - Responsibilities:
    1. Ensures compliance with data protection and privacy regulations.
    2. Develops and enforces data privacy policies.
    3. Manages data access requests and consent.
    4. Collaborates with legal and compliance teams.
  8. Data Architect - Responsibilities:
    1. Designs data models and data structures.
    2. Ensures data is organized and accessible.
    3. Collaborates with data stewards and data owners on data design.
    4. Implements data integration strategies
  9. IT Security Officer - Responsibilities:
    1. Ensures data security and access controls.
    2. Manages encryption and authentication mechanisms.
    3. Protects data against cybersecurity threats.
    4. Collaborates with data custodians and data owners.
  10.  Business Analyst - Responsibilities:
    1. Defines business requirements for data.
    2. Ensures data supports business processes.
    3. Collaborates with data owners and data stewards.
    4. Validates data for reporting and analytics.
  11. Data Governance Steering Committee - Responsibilities:
    1. Provides leadership and decision-making authority for data governance.
    2. Sets data governance priorities and policies.
    3. Resolves conflicts and issues related to data management.


The specific roles and their responsibilities may vary depending on the organization's size, structure, and industry. In some cases, a single individual may take on multiple roles, especially in smaller organizations. The key is to establish clear lines of responsibility and accountability to ensure that data governance is effectively implemented and maintained. 

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