DGIQ - Case Studies_ Applications of Data Governance in the Enterprise (Final).pdf
0 likes674 views
This document contains summaries of case studies demonstrating how various organizations have successfully implemented data governance programs. One case study describes how a construction firm used a data governance assessment to benchmark their maturity and prioritize initiatives. Another case study highlights how end-user training was critical to adoption at an enterprise organization. A third case study examines which tools and frameworks, such as a data catalog, were important starting points for a financial organization's data governance efforts. The last case study outlines how a federal agency developed a long-term roadmap for their data governance program after an initial 12 week accelerator to demonstrate value from a data catalog solution.
DGIQ - Case Studies_ Applications of Data Governance in the Enterprise (Final).pdf
- 1. Case Studies: Applications of Data Governance in the Enterprise Lulit Tesfaye and Thomas Mitrevski Data Governance and Information Quality 2023
- 2. ⬢ 15+ years of experience leading diverse information and data management initiatives, specializing in technologies and integrations ⬢ Most recently focused on employing advanced Enterprise AI and semantic capabilities for optimizing enterprise data and information assets Lulit Tesfaye VP OF KNOWLEDGE AND DATA SERVICES, ENTERPRISE KNOWLEDGE Thomas Mitrevski SENIOR CONSULTANT, ENTERPRISE KNOWLEDGE ⬢ 8+ years of experience in product/project management. Specifically supporting data management strategy, data catalog implementations, and data governance strategy efforts ⬢ Conducted complex data catalog and knowledge graph implementations for clients in a wide variety of commercial and government industries ENTERPRISE KNOWLEDGE
- 3. ENTERPRISE KNOWLEDGE Outline Introductions Enterprise Case Studies Expected Outcomes What You Will Learn ⬢ How Leading Organizations are Benchmarking Their Data Governance Maturity ⬢ Why End-User Training was Imperative in Seeing Scaled Governance Program Adoption ⬢ Which Tools and Frameworks were Critical in Getting Started with Data Governance ⬢ How Organizations Achieved Success with Data Governance in Under 12 Weeks ⬢ What Successful Data Governance Implementation Roadmaps Really Look Like
- 4. Case Study: Benchmarking Data Governance Maturity
- 5. Case Study Overview & Scope Project Background A construction management firm was seeking: ● An assessment of their data governance needs and advisory support for selecting a tool to address them. ● To clearly define and prioritize their data management and governance use cases. Data Governance Solutions Architecture Recommendation Tool Evaluation Matrix & Vendor Recommendation Data Governance Needs Assessment & Use Case Definition
- 6. Data Governance Findings ● Identify gaps in standardized processes for data governance ● Document and communicate processes across the organization, and define roles and responsibilities ● Focus on standardizing data governance processes ● Create an organization where individuals are eager to engage in potential data management initiatives ● Encourage staff to upskill and pursue data literacy training to help fill data governance gaps in their daily operations ● Identify underutilized customer and project data ● Correlate previously unrelated data to generate new insights ● Ability to gain insights from diverse types of data (structured/ semi-structured/ unstructured) ● Identify potential tools for better unifying and governing their data ● Unify data across source systems for an enterprise-level view regardless of physical location of data ● Build more automated integrations for business opportunities, projects, and skills ● Create the support and engagement to pursue initiatives for overarching data governance and management needs ● Prioritize a data-centric culture that enables discovery and connection of data We worked with this organization to categorize key takeaways into five data governance themes. PEOPLE PROCESS CONTENT TECHNOLOGY CULTURE
- 7. Inventory and Surface Existing Data Using a Data Catalog Priorities to Address Data Governance Challenges This is a selection of recommendations that enabled this organization to begin addressing their key data governance needs and challenges. These recommendations lay the foundation for longer term data governance strategy and initiatives. Manage and Track Access Requirements Elevate Relationships Between Disparate Datasets Manage and Leverage Unstructured Data Enhance Metadata Identify and Upskill Data Stewards Improve Data Lineage Establish Governance Organization
- 8. Case Study: End User Training
- 9. End User Training PEOPLE PROCESS CONTENT TECHNOLOGY CULTURE ● Training focused on how to establish data governance actions around Usage, Access, and Sharing at an enterprise organization. ● Training focused around identifying the correct structure for creating a data stewardship hierarchy. ● Training providing introductory guidance on the concepts of data governance and data stewardship. ● Training providing an introduction to business stakeholders on the concept of a data catalog and the value it will bring to their divisions. ● Training focused on how to communicate the value of data stewardship to various business stakeholders within an organization. We constructed a comprehensive training plan to upskill the organization in five key areas. Outcomes were directly tied to needs identified from the data governance assessment.
- 10. Guides data stewardship evolutions by providing insight into business and strategic objectives. Offers a technical perspective around suggested stewardship changes and leads changes in systems. Guides meetings and draws on stewardship best practices to inform decisions. Manages requests. Strategic Leads: Member of the data stewardship council. System Administrators: Member of the data stewardship council. Data Stewards: Do not have voting rights, but inform the stewardship council during decision-making processes. Stewardship Lead: Member of the data stewardship council. Stewardship Council: Decision-making body of the data stewardship team. Each recommended role builds on the preceding one in terms of responsibility level and decision power. Data Stewardship Hierarchy Provides strategic direction and collective decision-making. Offers program area or operational area perspective on stewardship needs.
- 11. ENTERPRISE KNOWLEDGE Access, Usage, and Sharing Aggregate User(s) can create a dataset that is made up of but not linked to existing data assets. Branch User(s) can create a dataset that is made up of and linked to existing data assets. See User(s) can see the data asset in a list of results, but cannot view it. View User(s) can read the contents of a data asset. Edit User(s) can add to, modify, or remove pieces of an existing data asset. Draft User(s) can add a new data asset. Manage/Own User(s) can archive or delete a data asset. Comment User(s) can view a data asset and attach non-edit notes. Govern User(s) can determine the standards and structure of a data system. CONNECT CREATE MANAGE ENHANCE FIND
- 12. Case Study: Tools and Frameworks
- 13. Case Study Overview & Scope Project Background ● A multi-national financial organization was facing difficulties in unifying governance, discovery, and search across multiple metadata storage platforms within their global enterprise. ● In order to rectify their existing data quality and governance issues with a standardized metadata platform, this organization identified a data catalog as a foundational solution to start addressing these challenges and consulted with us to lead the implementation. Maintain compliance tracking through external ontologies A new metadata platform that enabled more advanced use cases Increased data maturity and development 8,000+ Business glossary objects 5,500+ resources
- 14. Why is a Data Catalog Foundational? A data catalog serves as a data governance tool that allows us to collect, aggregate, and present logical and physical metadata to end users. A modern data catalog…. • Contextualizes and enriches information with meaning of data based on business or data domains. • Establishes relationships across disparate data sources and across business and technical concepts. • Unifies unstructured and structured data to connect data of all formats. • Makes data and information easily searchable and discoverable. ENTERPRISE KNOWLEDGE
- 15. Data Catalog Business Value COST SAVINGS & INCREASED REVENUE ● Tag data using terms from a customized business glossary ● Increase the accuracy and range of search Provide Structure ● Target access to data to specific audiences ● Enable faster access to the right data and the people who manage it Improve Findability ● Implement a user-centric and scalable data inventory ● Help users organize, find, and discover data Improve Discoverability ● Find and connect existing data for reuse ● Minimize duplication of existing data and dashboards ● Standardize data schemas across sources Reuse Content ● Integrate multiple disparate sources of data ● Connect both structured and unstructured datasets Integrate Sources ENTERPRISE KNOWLEDGE
- 16. How a Data Catalog Fits into Data Governance Tools Application Data Fabric Layer Sources Integration/ Processing Layer Presentation Layer Extract, Transform, Load (ETL) Pipelines APIs Search Research & Analytics Recommendations / Chatbot Admin / Governance Context and Metadata Metadata Service Taxonomy / Ontology Management Master Data Management Data Lake / Data Warehouse Client Connectivity Data Livestream Data Knowledge Graph Data Catalog Content Storage APIs A data fabric enables data federation and virtualization of semantic labels or rules (e.g. taxonomies/ business glossaries or ontologies) to capture and connect data based on business or domain meaning and value. ENTERPRISE KNOWLEDGE
- 17. Case Study: 12 Week Accelerator
- 18. Data Catalog Implementation Components Use Cases and Business Capabilities Backlog 02 Prioritization, design, and validation of use cases and capabilities for the implementation backlog. Platform Architecture and Configuration 01 Installation and integration of solution with source systems, definition and enforcement of security management, and finalization of access controls. Data Organization, Enrichment, and Connectivity 04 Metadata modeling, metadata glossary design, automated enrichment, and connection of data concepts based on relationships (datasets, documents, applications, etc.). User Onboarding and Enablement 03 Phased user provisioning and enablement through foundational tasks teams need to learn to be successful on the data catalog platform. Governance and Analytics 06 Agile approach to metadata stewardship and governance with configured governance workflows in data catalog to enable collaboration and provide end-to-end visibility throughout the analytics lifecycle. Training and Adoption 05 Module-based introductory and advanced training with hands-on practice labs customized to fit business and technical personas and derive successful program adoption.
- 20. Case Study Overview & Scope Project Background ● A federal agency required a comprehensive data catalog solution to serve as a central facilitator for their overarching data strategy and governance program and as a source to search, discover, and gain insights into enterprise-wide data assets. ● This included the integration with a variety of systems and applications both internally and externally. Establish metadata quality standards and operating policies in an Agile Data Governance Playbook. Ingest and actively govern glossary terms. Define and embed governance roles and responsibilities across multiple divisions.
- 21. ENTERPRISE KNOWLEDGE Long-Term Roadmap The goal of this roadmap is to enable an organization to iteratively achieve a state where metadata is standardized, governance is embedded, and collaboration on data is consistent. Onboarding and Foundational Configuration Expand to Prioritized Use Cases Governance and Analytics Playbook Enhance and Optimize Development of Advanced Use Cases
- 22. In less than 20 weeks, validate and demonstrate to your organization how a data catalog can provide value before making a long-term investment. Demonstrate Value Early Findings from the initial use cases will inform a repeatable approach and long-term roadmap to scale the data catalog in line with organizational objectives. Align on Data Catalog Strategy for Scale Encourage Adoption Implementing a data catalog quickly will create interest and ownership by allowing users to see tangibly how their data challenges will be addressed. Gain Valuable Governance Insights The process of implementing a catalog will reveal valuable insights into your own governance processes, shining a light on what processes and procedures are effective, and which ones need to be improved. Key Takeaways