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  • Peter Meyers

Strategies for Information Governance and Data Quality: Overcoming Integrity Challenges

reflection of the word DATA in a city window

At MSS Business Transformation Services (MSSBTA), we view information as a vital asset managed through a three-tiered approach: Knowledge Management, Information Governance, and Data Governance.

Knowledge Management enhances organizational efficiency and innovation by managing the knowledge lifecycle from creation to sharing.

Information Governance ensures information security and optimization, involving organization-wide efforts beyond IT to meet goals and comply with regulations.

Data Governance focuses on data availability, integrity, and security, primarily under IT's purview, ensuring consistency in data management practices.

These distinct yet interconnected strategies are essential for effectively leveraging information as an asset.

Infographic of Information as an Asset, Information Governance, Data Governance, and Knowledge Management

The Data Challenge

Many organizations need more data quality and integrity concerns, which impedes the effective use of information as an asset. Inaccuracies, incompleteness, and inconsistency across data systems characterize these issues. Poor data quality can lead to misguided insights and decisions, affecting the organization's performance and credibility. The scope of these issues includes: 

  • Data Management Challenges: According to an IBM report, businesses in the U.S. lose $3.1 trillion annually due to poor data quality, highlighting the extensive monetary impact of data management challenges.

  • Cost of Poor Data Quality: Gartner estimated that poor data quality could cost organizations an average of $15 million annually in losses. This cost comes from numerous factors, including wasted resources, missed opportunities, and incorrect decisions based on inaccurate data.

  • Impact on Decision-Making: A survey by Experian found that around 95% of organizations see an impact on their bottom line from poor data quality, affecting trust in the data used for decision-making.

  • Data Silos: Many organizations suffer from data silos, where information is stored in separate, disconnected systems. This fragmentation makes it challenging to get a comprehensive view of the organization's data, hinders data analysis, and complicates decision-making processes.

  • Data Overload: The sheer volume of data generated and collected by organizations can be overwhelming. Without adequate data management and analytics strategies, valuable insights can be lost in the noise.

A Strategic Approach to Addressing Data Quality and Integrity

Addressing these data quality and integrity challenges requires a strategic approach focusing on understanding information as an asset, adopting best practices in information and data governance, and fostering a culture that values data-driven decision-making. Committing to all three efforts provides a meaningful approach to better managing organizational data quality and integrity.

1. Understanding Information as an Asset

  • Recognition: Information should be recognized as a critical asset that requires proper management and protection. This understanding should be widespread across the organization.

  • Valuation: Implement methods to assess and communicate the value of data and information assets, making it easier to justify data quality and integrity investments.

2. Adopting Best Practices in Information and Data Governance

  • Establish Governance Framework: Develop and implement a robust information and data governance framework that outlines policies, standards, roles, responsibilities, and procedures for managing data across its lifecycle.

  • Data Stewardship: Appoint data stewards or guardians responsible for data quality and integrity within specific domains. They play a key role in enforcing governance policies and resolving data issues.

  • Quality Management: Implement data quality management practices, including data profiling, cleansing, enrichment, and validation, to ensure data is accurate, complete, and reliable.

  • Security and Compliance: Ensure data management practices comply with relevant regulations and standards (e.g., GDPR) and incorporate data security measures to protect data integrity and confidentiality.

3. Fostering a Culture That Values Data-Driven Decision Making

  • Promote Data Literacy: Invest in training and resources to enhance data literacy across the organization. Employees at all levels should understand the value of data, how to interpret it, and how to use it in decision-making processes.

  • Encourage Collaboration: Foster a collaborative environment where data is shared and used cross-functionally. Breaking down silos encourages innovation and improves data quality through diverse inputs and perspectives.

  • Incentivize Quality Data Practices: Recognize and reward contributions to data quality and integrity. Incentives can motivate employees to take ownership of data and prioritize its accuracy and completeness.

Wrap Up

Information is an existing resource and a crucial asset for mitigating organizational risk, enhancing efficiency, and improving engagement with employees, customers, and constituents. Achieving the desired outcomes and benefits associated with using information as an asset is worth the effort related to improving organizational data quality and integrity.


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