Presented by Besa Bauta, Chief Data and Analytics Officer, Texas Department of Family and Protective Services & Jennifer Bush, Chief Information Security Officer, Texas Department of Family and Protective Services

This presentation provides an overview and key principles for a sustainable Data Management and Governance Program. It includes a review on how to create both standardization and transparency around processes that govern information, including the; (1) identification, security (obfuscation), and storage of data, (2) cataloging, tagging, normalizing, and standardizing,  (3) determining the highest value data assets and uses for the business, (4) assessing and decommissioning data that has reached end-of-life for the organization, (5) determination of retention periods, cold storage, or destruction of end-of-life data (i.e., Data-Life-Cycle-Management), and lastly (6) leveraging information across the organizations to inform decisions.

The Outcome-Based Data Enablement/Management (OBDEM) includes two key concepts; Minimum Viable Data Governance and Data Quality. An outcome-based approach also includes various stakeholders in the process and focuses on the “what’s in it for me” approach to generate buy-in. This is accomplished by examining 4-cross-functional domains across Data Governance and Data Quality and mapping out both foundational and operational components. Additionally, teams and stakeholders are aligned on a RACI approach (i.e., Responsible (team member who does the work and/or completes the task), Accountable (person who delegates work and provides final review on a task and/or deliverable before its complete), Consulted (Individuals who provide input on a deliverable based on the impact on their work or domain of expertise/subject-matter experts (SMEs)), and Informed (Individuals who need to be kept in the loop or updated on project progress). This allows for better enablement of projects and talent engagement. Additionally, both operational and functional components are run in parallel and intertwined when necessary for moving forward projects, including embedding governance and quality activities as part of the way of doing projects. This ensures justification and value are tied to the program and not the work within Data Enablement (i.e., Data Governance, Management, and Quality Activities).

We also propose a new perspective and re-branding for data governance programs from Data Governance to Data Enablement, and from Data Owners to Data Trustees, to discern that data and information infrastructure belong to the organization and not individual departments and/or entities. Establishing data as an enterprise asset and avoiding the tendency toward data hoarding and silos. We also highlight the differences between Data Governance and Data Stewardship. Data Governance is the policies, procedures, and rules that govern data. Data stewardship is the implementation of those policies, procedures, and rules. It is important to emphasize that the implementation does not refer to only the tools. Data stewardship impacts people, processes, and technology. We also focus on key frameworks that can be utilized to govern data enablement activities, assessments to identify enterprise-related data/information issues, and strategies to build foundational activities in order to optimize enterprise activities to become data-driven (i.e., proactive evidence-based decisions and automated data improvements).

Lastly, we aim to take Data Governance/Enablement, Management, and Quality from Boring to Exciting!