A-Z Data Fabric Glossary

A-Z Data Fabric Glossary Terminology

A-Z Data Fabric Glossary provides governance and quality management terminology critical to AI-driven decision making. This A-Z glossary provides essential definitions, industry standards, and links to tools and best practices for data professionals.

Data governance is the foundation of reliable business intelligence, automation, and AI-driven decision-making. However, businesses struggle with inconsistent terminology, making it difficult to implement effective policies. According to Gartner, poor data quality costs companies an average of $12.9 million per year. With increasing compliance demands from GDPR, CCPA, and HIPAA, organizations must standardize their data governance frameworks.

This A-Z glossary defines essential terms, tools, and standards used in data governance, quality management, and compliance. It includes links to best practices and industry documentation from ServiceNow, IBM, Microsoft, and leading regulatory bodies.

A-Z Data Governance, Quality, and Compliance Glossary

A

  • Adaptive AI – AI that dynamically adjusts based on real-time data.
  • AI Data Connections – Automated linking between structured and unstructured data sources.
  • Audit Logs – Records tracking modifications, transformations, and data access.

B

  • Boolean Operators – Logic-based syntax for refining search queries.
  • Business Glossary – Standardized business definitions for data assets. Best Practices

C

  • Column Schema Checks – Validation of column data types, constraints, and relationships.
  • Compliance & Regulatory Standards – Legal frameworks ensuring responsible data management. GDPR Overview
  • Cross-Platform Workflow Automation – Automated workflows integrating cloud and on-prem environments. Microsoft Power Automate

D

  • Data Asset Taxonomy – Categorization of data based on governance attributes.
  • Data Cleansing – Removing duplicate, inconsistent, or erroneous data.
  • Data Fabric – A unified approach to managing and integrating enterprise data.
  • Data Governance – The framework ensuring data accuracy, security, and compliance. DAMA Data Governance Guide
  • Data Ingestion – The process of collecting and processing data for analytics.
  • Data Integrity – Ensuring the accuracy, reliability, and consistency of data.
  • Data Lineage – Tracking the origin, movement, and transformation of data.
  • Data Retention Policies – Rules governing how long data is stored and managed.

E

  • ETL (Extract, Transform, Load) – A data pipeline for collecting, processing, and storing structured data.

F

  • Faceted Filtering – Refining search results based on tags, attributes, and sources.

G

  • Generative AI – AI capable of creating new content based on existing data.

I

  • IBM Data Fabric – IBM’s AI-driven platform for managing enterprise data.
  • Interactive Data Visualization – Graphical representation of trends, metrics, and insights. ServiceNow Dashboards

K

  • Keyword-Based Search – AI-driven search methods optimizing data discovery.

L

  • Lineage Tracking – Visualizing data movement across an enterprise system.

M

  • Meta Tagging – Applying metadata to enhance searchability and classification.
  • Microsoft Workflow Data Platform – Microsoft’s workflow and automation tool. Power Automate

N

  • N-Level Taxonomy Tree – Hierarchical classification of data assets.

O

  • OAuth 2.0 & SAML Authentication – Secure methods for data exchange and access management.

P

Q

  • Query Optimization – Improving database query efficiency and speed.

R

  • RaptorDB – ServiceNow’s high-performance database for analytics and decision-making. ServiceNow Data Management
  • Real-Time & Batch Processing – Data processing methods for analytics and automation.

S

  • Schema Management – Defining data structures and validation rules.
  • Snowflake Data Warehouse – A cloud-based storage and processing system. Snowflake Overview

T

  • Table Schema Checks – Validating structural integrity of tables in a database.

U

  • User Permissions & Access Control – Restricting access to data based on user roles.

W

  • Webhook Event-Driven Architecture – Real-time event notifications and data streaming.
  • Workflow Automation – AI-driven execution of business processes. Microsoft Power Automate

Y

  • Yokohama Pro – The latest ServiceNow release featuring enhanced AI-driven data governance.

Conclusion: Standardize Data Governance Practices

Mastering data governance terminology is crucial for businesses implementing AI-driven analytics and automation. Standardized frameworks improve compliance, mitigate risks, and enhance data quality. Explore industry tools like ServiceNow Performance Analytics, IBM Data Fabric, and Microsoft Power Automate to integrate governance best practices.

Other Resources to A-Z Data Fabric Glossary

Association-of-Generative-AI https://www.linkedin.com/groups/13699504/
Association-of-Generative-AI https://www.linkedin.com/groups/13699504/

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