Enterprise Data Management Maturity Model

Phases of Enterprise Data Management Maturity Model

Journey showing the end-to-end process

L0
Preparation
L1
Initial
L2
Repeatable
L3
Defined
L4
Managed
L5
Optimized
What needs to be done in order to reach "L0-Preparation" maturity phase
Task Responsible Role Where to Maintain Success Measure
L0-1. Identify Stakeholders Data Domain Owner / Business Users Enterprise Data Management Data Domain stakeholders are onboarded
L0-2. Identify Data Domain Use Case Data Domain Owner Enterprise Data Management Domain Use Case is implemented as Project
L0-3. Identify Data Sources Data Domain Owner Enterprise Data Management Systems definitions are implemented
What needs to be done in order to reach "L1-Initial" maturity phase
Task Responsible Role Where to Maintain Success Measure
L1-1. Define DataDomain in DGC Data Domain Owner Enterprise Data Management Data Domain hierarchy is built in DGC
L1-2. Define Critical DataElements Data Domain Owner Enterprise Data Management Business terms with CDE classifications are implemented
L1-3. Request a Connection Data Domain Owner Enterprise Data Management Connections do data sources established
What needs to be done in order to reach "L2-Repeatable" maturity phase
Task Responsible Role Where to Maintain Success Measure
L2-1. Define Datasets Data Domain Owner / Business Users Enterprise Data Management Data sets definitions implemented
L2-2. Define relationships Data Domain Owner Enterprise Data Management Linkage between technical data elements and glossary assets are built
L2-3. Curate auto-tagged data elements Data Domain Owner Enterprise Data Management AI suggested tagging for data elements is reviewed
What needs to be done in order to reach "L3-Defined" maturity phase
Task Responsible Role Where to Maintain Success Measure
L3-1. Assign data classifications Data Domain Owner Enterprise Data Management Data Assets are classified
L3-2. Define Terms of use Data Domain Owner Enterprise Data Management Data Standards and Terms of Use are registered in EDM
L3-3. DQ Rules Implementation in Data Integration Data Domain Owner Enterprise Data Management Data Quality Rules implemented
What needs to be done in order to reach "L4-Managed" maturity phase
Task Responsible Role Where to Maintain Success Measure
L4-1. Define Usage Types Data Domain Owner Enterprise Data Management Usage Types for future data products implemented
L4-2. Define DeliveryTargets Data Domain Owner Enterprise Data Management Delivery targets for the future data products are implemented
L4-3. Define Data Access Rules Data Domain Owner Enterprise Data Management Data Access Policies Implemented in CDAM
What needs to be done in order to reach "L5-Optimized" maturity phase
Task Responsible Role Where to Maintain Success Measure
L5-1. Define Data Domain in EDM Data Domain Owner Enterprise Data Management Data Domain hierarchy is built in EDM
L5-2. Register Data Product Data Domain Owner Enterprise Data Management Data Product registered
L5-3. Enhance dashboards observability Data Domain Owner Enterprise Data Management Dashboards Observability enhanced

Delivery of Cloud Data Governance and Catalog & Marketplace


Background

  • The organization sought to enhance its data governance, cataloging, and marketplace capabilities to streamline data accessibility and compliance.
  • Existing data governance processes were fragmented, done in Excel files, leading to inefficiencies and lack of transparency across data assets.
  • Informatica Cloud Data Governance and Catalog, along with Informatica Marketplace, were identified as the ideal solutions to centralize data governance and improve data usability.


Objectives

  • Conduct a thorough analysis of the enterprise landscape to align the solution with business and technical needs.
  • Develop a structured framework to guide the seamless implementation and adoption of Informatica tools across Data Domains (called the Roadmap).
  • Develop a structured framework to enable Data Governance Team to deliver Data Governance across Data Domains (called the Playbook).
  • Enable stakeholders with comprehensive training to maximize the value derived from the new platform.
  • Build a comprehensive workspace for the stakeholders to learn and share the knowledge on Informatica Cloud Data Governance and Catalog / Informatica Marketplace.


Solution

  • Assessed the current data governance maturity and data management practices across chosen Data Domains (assessment).
  • Designed detailed use cases that showcased tool functionalities and illustrated how they addressed specific business challenges (assessment).
  • Developed a robust delivery framework and playbook outlining the steps necessary for successful implementation and adoption (design).
  • Delivered comprehensive training to maximize the value derived from the new platform (implementation).
  • Onboarded Data Domains stakeholders into Data Catalog and Marketplace (implementation).
  • Built a comprehensive workspace for the stakeholders to learn and share the knowledge on Informatica Cloud Data Governance and Catalog / Informatica Marketplace (implementation).


Challenges

  • Managing stakeholder expectations regarding implementation timelines and deliverables.
  • Navigating organizational silos that hinder cross-functional collaboration and decision-making.
  • Demonstrating the business value of Informatica services to Data Domains and securing their buy-in.
  • Ensuring alignment between Data Domains and Data Governance Teams for seamless collaboration.


Results

  • Developed reusable framework and training materials, reducing the time required for onboarding new Data Domains and their stakeholders.
  • Trained stakeholders across business and IT teams, increasing adoption and tool proficiency.
  • Successfully onboarded Data Domain Stakeholders into a centralized data governance platform, providing clear data ownership and accountability across the organization.
  • Increased user confidence in leveraging Informatica tools, leading to self-sufficiency in data governance operations.


Lessons Learned

  • Conducting a thorough enterprise landscape analysis upfront ensured the solution was aligned with both business and technical needs, reducing implementation friction.
  • Developing a structured delivery framework and playbook provided clear guidance, minimizing confusion and streamlining execution.
  • Early stakeholder engagement was critical to overcoming resistance, securing buy-in, and ensuring a smooth transition.
  • Designing use cases that clearly showcased tool functionalities helped drive adoption by demonstrating real-world benefits to stakeholders.
  • Tailoring training content to different user groups ensured that both business and technical teams could leverage the platform effectively.
  • Recording and delivering comprehensive training programs significantly improved user proficiency and confidence.
  • Iterative feedback loops with users helped refine training materials and support ongoing improvements in platform usage.


Conclusion

  • The delivery of Informatica Cloud Data Governance and Catalog, alongside Informatica Marketplace, successfully addressed the organization's data governance challenges.
  • The structured delivery approach ensured that both business and technical stakeholders were equipped to leverage the platform effectively.
  • The initiative set a strong foundation for future advancements in data governance and marketplace capabilities.

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