Data Governance

The Five Pillars of Data Governance

• Data Stewardship - Ensures accountability for managing institutional data
• Data Management - Focuses on processes and tools for handling data throughout its lifecycle
• Data Integrity & Consistency - Ensures accuracy and reliability in decision-making
• Data Standards & Policies - Sets rules and guidelines for data handling
• Data Security, Privacy, & Ethics - Protects sensitive data and guides ethical data practices

What is Data Governance?

Formalizing data management policies and roles by establishing clear policies, procedures, and roles for data management to ensure consistent and effective data handling practices.

DAMA International defines Data Governance as “the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.” 

The Data Governance Institute defines it as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” 

The Gartner Glossary defines it as “the specification of decision rights and accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics.” 

Robert Seiner defines it as "the ability to execute and enforce authority over the management of data definition, production, and usage of data at all levels of the organization, has become an indispensable cornerstone of assuring enterprise success."

How do we approach Data Governance?

Non-Invasive Data Governance Approach

Ensuring accountability for data, while guaranteeing its accuracy, integrity, and accessibility, is crucial not only for informed decision-making but also for regulatory compliance. This requires a thorough framework and operating model that goes beyond traditional data governance approaches, enabling every part of an organization to be formally responsible for their data activities (Seiner, 2014). 

Seiner, R. S. (2014). Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success. United States: Technics Publications LLC.

How do we use it and why does it matter?

• Ensuring Data Availability, Usability, Integrity, and Sustainability:
  • Ensure the availability, usability, integrity, and sustainability of our most critical data in support of analytics, business operations, and compliance.
• Promoting Data Collaboration, Efficiency, and Literacy:
  • Promote data collaboration, efficiency, and literacy aligned with our strategic plan and business objectives.
• Enhancing Data Quality and Accessibility:
  • Improve the quality of data for better decision-making and operations while ensuring data is accessible where needed, breaking down silos.
• Ensuring Regulatory Compliance and Data Security:
  • Maintain compliance with regulatory requirements and enhance data security and privacy, while still making data accessible for legitimate uses.
• Simplifying Data Architecture and Integrating Data:
  • Simplify the data architecture and models for easier integration and shared business operations, facilitating a consistent source of truth.
• Creating a Roadmap for Data Management Maturity:
  •  Develop a roadmap to mature the organization’s data management activities, including inventorying shared data in a catalog and defining critical data terminology.
• Promoting Data Awareness and Trust:
  • Promoting awareness and usability of data across the organization, building data trust and integrity among employees, customers, and vendors, and supporting equitable outcomes.
• Understanding how the data will impact the institution and provide value for a data-informed culture:
  • Maintain the integrity and sustainability of our most critical data in support of analytics, operations, and compliance.