Data Fabric Governance & Quality
Data Fabric Governance & Quality: A Strategic MVP Framework for AI-Ready ServiceNow Portals. It is often the rush to deliver innovative, AI-powered data platforms, that prompts a premature leap into UI/UX design before aligning on the foundational backend structure. This imbalance leads to fragmented UI/UX designs, as the underlying governance structures are not robust enough to support cohesive user experiences. Consequently, late-stage design decisions may falter, and user trust can diminish.
Notably, McKinsey highlights that poor master data management leads to inefficiencies and lost revenue opportunities. Similarly, Gartner emphasizes that many data and analytics initiatives falter due to poor data quality and inconsistent governance.
To address these challenges, organizations must balance data quality and governance initiatives. By doing so, they ensure cohesive UI/UX designs that foster user trust and scalability.
🧱 Part 1: Foundation – Align Decisions and Terminology First
Building a sustainable Data Fabric portal necessitates a robust technical foundation. Without it, the UI becomes disconnected from the reality of data flow, governance, and interpretation.
✅ MVP Readiness: Key Technical Elements to Finalize First
✅ MVP Readiness: Essential Technical Components
| Category | Key Actions |
|---|---|
| Data Domains | Identify and prioritize domains (e.g., Clinical, HR) with assigned ownership. |
| Data Object Model | Standardize tables, fields, metadata relationships, and track data sources. |
| Glossary Terms | Define terms, manage synonyms, assign stewards, and implement audit tracking. |
| Data Quality Rules | Establish rule types (accuracy, uniqueness, timeliness) with scoring logic. |
| Rules Engine | Configure execution points (on ingest/access), result tagging, and ownership. |
| Lineage Structure | Map data transformations across systems with version control. |
| Governance Model | Define CRUD privileges per persona (Steward, Custodian, Analyst, Viewer). |
| Registration Process | Develop intake workflows for dataset onboarding, including profiling and approval. |
| Change Control | Set up versioning, review, and approval steps for rules and glossary changes. |
| Audit & Traceability | Determine events to log, visualize, and make available for review/export. |
🔁 Part 2: Design Process – Connect Logic to Experience
After achieving technical alignment, proceed to the design phase using a layered, logic-first model. This approach ensures continuity between data management and user interaction.
🔧 A. Back-End – Engine and Service Layer
| System Layer | Required Activities |
|---|---|
| Profiling Engine | Develop logic to scan, score, and store metadata for DQ evaluation. |
| Rules Engine | Configure reusable rule templates, trigger types, and tagging schema. |
| Governance Workflow | Design lifecycle flows for glossary approvals, access management, and reviews. |
| Lineage Logging | Record transformations and movement across domains with historical tracking. |
| API Layer | Enable integrations for rule runs, glossary lookups, lineage views, and more. |
🧩 B. Front-End – Configuration & Stewardship Interfaces
| Interface | Key Capabilities |
|---|---|
| Glossary Management | Enable term CRUD, synonym handling, relationship mapping, and audit logging. |
| Rule Configurator | Provide a visual builder to define logic, thresholds, triggers, and tags. |
| Data Source Registration | Guide stewards through field mapping, validation, and domain classification. |
| Stewardship Dashboard | Display exception queues, rule failures, and review task workflows. |
| Governance Matrix | Present permissions by persona, data type, and business function. |
🖥️ C. UI/UX Portal – Empower End Users with Clarity
Before launching into UI/UX design for your ServiceNow Data Fabric portal, it’s imperative to ensure that all technical dependencies are firmly in place. This proactive approach guarantees that users interact with complete, accurate, and governed data. To achieve this, consider addressing the following critical questions:
🔍 Data Readiness & Governance
- Have we clearly defined all critical data domains (e.g., HR, Clinical, Finance) and assigned ownership?
- Are glossary terms standardized, including definitions, synonyms, and stewardship assignments?
- Have we established data quality rules (e.g., accuracy, timeliness, uniqueness) with appropriate scoring logic?
- Is data lineage comprehensively mapped, capturing transformations across systems and versions?
- Do we have a defined process for data registration, encompassing submission, review, profiling, and approval?
🛠️ Technical Infrastructure & Integration
- Is the rules engine configured with execution points (e.g., on ingest, on access) and result tagging?
- Are APIs available for rule execution, glossary access, lineage views, and data ingestion?
- Have we established version control and change management processes for rules, tables, and terms?
- Is there an audit trail capturing events that need to be logged, visualized, and exported?
👥 Roles, Permissions & Workflows
- Are CRUD (Create, Read, Update, Delete) privileges defined by persona (e.g., Steward, Custodian, Analyst, Viewer) and object type?
- Have we designed governance workflows for term approvals, data access reviews, and rule validations?
- Is there a stewardship dashboard for managing exceptions, approvals, and task assignments?
- Do we have a governance matrix to manage permissions by object type and role?
📊 Metrics, Dashboards & User Experience
- Are key performance indicators (KPIs) and dashboard metrics defined with identified data sources and owners?
- Have we established a unified vocabulary to ensure consistent language across the platform?
- Is the UI/UX design aligned with backend logic, ensuring features like data explorers, glossary navigators, and lineage viewers function correctly?
By meticulously addressing these questions, you lay a solid foundation for a UI/UX design that is both user-centric and technically sound. This strategic preparation minimizes rework, enhances user trust, and ensures the portal’s scalability and sustainability.
| Portal View | Features That Must Map to Backend Logic |
|---|---|
| Overview Dashboard | Display quality scores, filters, active exceptions, and issue trends. |
| Data Explorer | Provide table/column views with overlays for glossary terms and quality tags. |
| Glossary Navigator | Enable users to search, filter, and explore term usage with related insights. |
| Rule Results View | Visualize rule performance, recent failures, and exception resolution status. |
| Lineage Viewer | Map data origin, flow, and transformations across systems and versions. |
| Ingestion Log | Display the onboarding timeline and profiling activity of new datasets. |
🧭 Part 3: Unified Vocabulary – Standardize Your Language
Before embarking on UI/UX design for your ServiceNow Data Fabric portal, it’s essential to ensure that all technical dependencies are firmly established. This proactive approach guarantees that users interact with complete, accurate, and governed data, thereby enhancing trust and usability. To achieve this, consider addressing the following critical questions:
🔍 Data Readiness & Governance
| Question | Purpose |
|---|---|
| Have we clearly defined all critical data domains (e.g., HR, Clinical, Finance) and assigned ownership? | To ensure accountability and clarity in data management. |
| Are glossary terms standardized, including definitions, synonyms, and stewardship assignments? | To maintain consistency and prevent misunderstandings. |
| Have we established data quality rules (e.g., accuracy, timeliness, uniqueness) with appropriate scoring logic? | To uphold data integrity and reliability. |
| Is data lineage comprehensively mapped, capturing transformations across systems and versions? | To provide transparency and traceability in data flow. |
| Do we have a defined process for data registration, encompassing submission, review, profiling, and approval? | To streamline data onboarding and validation. |
🛠️ Technical Infrastructure & Integration
| Question | Purpose |
|---|---|
| Is the rules engine configured with execution points (e.g., on ingest, on access) and result tagging? | To automate data validation processes effectively. |
| Are APIs available for rule execution, glossary access, lineage views, and data ingestion? | To facilitate seamless integration and interoperability. |
| Have we established version control and change management processes for rules, tables, and terms? | To manage updates systematically and prevent conflicts. |
| Is there an audit trail capturing events that need to be logged, visualized, and exported? | To ensure compliance and enable monitoring. |
👥 Roles, Permissions & Workflows
Are CRUD (Create, Read, Update, Delete) privileges clearly defined by persona (e.g., Steward, Custodian, Analyst, Viewer) and object type?
→ Absolutely, because without tailored access, data integrity is at risk. By assigning CRUD privileges based on role and object type, we ensure that every user interacts only with the data they’re responsible for—enhancing both security and accountability.
Have we designed governance workflows for term approvals, data access reviews, and rule validations?
→ Yes—because effective governance doesn’t happen by chance. When workflows are clearly mapped, responsibilities become actionable. As a result, approvals move faster, reviews become repeatable, and validations are no longer missed.
Is there a stewardship dashboard in place to manage exceptions, approvals, and task assignments?
→ Definitely. Because data issues demand real-time visibility, a stewardship dashboard empowers teams to triage, assign, and resolve exceptions quickly—turning oversight into insight and delays into action.
Do we have a governance matrix to manage permissions by object type and role?
→ Without a doubt. A permissions matrix transforms complexity into clarity. It provides a visual reference to assess who can do what, where, and why—making audits easier and policy enforcement consistent.
📊 Metrics, Dashboards & User Experience
- Are key performance indicators (KPIs) and dashboard metrics clearly defined with linked data sources and assigned owners?
→ Yes, because measuring success means nothing without clarity and ownership. When KPIs tie directly to trusted data sources and responsible individuals, teams stay aligned, accountable, and performance-focused. - Have we established a unified vocabulary to drive consistent language across the platform?
→ Indeed, because without shared terminology, collaboration falters. By creating a common vocabulary, we remove ambiguity, streamline communication, and foster a shared understanding across teams and tools. - Is the UI/UX design seamlessly integrated with backend logic, ensuring that features like data explorers, glossary navigators, and lineage viewers function as intended?
→ Absolutely. Because when design and logic align, the experience feels intuitive. Users navigate confidently, find insights faster, and engage meaningfully with data—driving adoption and long-term value.
Other Data Fabric Governance & Quality Resources
- A-to-Z Guide on UX Research for Beginners
- Agentic AI & Workflow Data Fabric
- BPMN for UX: Integration and Benefits in User-Centered Design | Aguayo’s
- Build ServiceNow Data Fabric
- Gartner report on data fabric and data mesh
- How Data-Driven Design is Revolutionizing UX
- Introduction to Microsoft Fabric | Data Analytics for the Era of AI
- Visualize Governance Empower Decisions
- Workflow Data Fabric | ServiceNow® Data Fabric
