Informatica Metadata Management

Informatica’s metadata management approach is designed to help enterprises fully harness the value of all their data with active metadata. Informatica Metadata Management allows enterprises to start this journey by tapping into four major categories of metadata:

• Technical: Database schemas, mappings and code, transformations, quality checks
• Business: Glossary terms, governance processes, application and business context
• Operational and infrastructure: Run-time stats, time stamps, volume metrics, log information, system and location information
• Usage: User ratings, comments, access patterns

Metadata in these four categories becomes the basis for a common metadata foundation. Informatica Metadata Management uses a rich set of capabilities to create this shared foundation:

• Collect: Scan the metadata from all of an enterprise’s data systems across cloud and on-premises—including databases and filesystems, integration tools and processes, and analytics and data science tools—with a high level of fidelity.
• Curate: Document the business view of data with glossary terms, concepts, relationships, and processes. Augment the collected metadata with this business context. Gather user input in the form of ratings, reviews, and certifications to help assess the usefulness of data assets to other users.
• Infer: Apply intelligence to derive relationships not obvious in the collected metadata, including data lineage, data similarity, and ranking the most useful data sets for different types of users.

The Power of a Unified Metadata Platform

By gathering technical, business, operational, and usage metadata, Informatica creates a knowledge graph of an enterprise’s data assets and their relationships. We make this metadata graph active by applying AI and machine learning and integrating it with all of our data management solutions.

Active metadata serves as the unifying foundation for Informatica’s Intelligent Data Platform, an integrated and modular platform that allows you to grow and evolve at your own speed, while enabling you to address all of your data management requirements. It fuels the intelligence in the CLAIRE™ engine, the industry’s first metadata-driven artificial intelligence, to accelerate and automate core data management and governance processes. CLAIRE leverages metadata to automatically discover data domains, classify data, identify similar data and other data relationships, recommend next-best actions, and associate business terms with physical datasets.

By making an intelligent data catalog a core part of your data infrastructure, you can ensure that active metadata is integrated into all your data management processes. Informatica Enterprise Data Catalog helps you capture enterprise-wide metadata and turn it into active metadata using extensive connectors that scan and index metadata augmented with intelligence from CLAIRE. Active metadata adds automation and makes it easier and more efficient for users to build, deploy, and operate data management applications for analytics, data science, governance, and any other data-driven business priority.

Benefits of Active Metadata Management

Here are a few ways Informatica’s active metadata management approach delivers value across the data management lifecycle:

• Next-Generation Analytics
  • Enables self-service through simple search, discovery, and recommendations for relevant data
  • Provides a complete view of data including lineage, relationships, and quality to improve trust and confidence in the data for analytics
  • Helps accelerate AI/ML projects with improved data visibility for agile data preparation, analysis, and development of ML models for AI applications
• Data Quality and Governance
  • Discovers, classifies, and documents key data elements to help you prioritize data governance activities
  • Provides detailed metadata and lineage to bridge technical and business context for data governance
  • Documents data quality in the context of business systems and processes to increase visibility into sources of data quality problems
• Data Privacy
  • Correlates relationships between individual subjects and personal data across structured and unstructured sources to help automate subject access requests
  • Tracks protection status, access, proliferation and risk exposure of sensitive data to increase compliance transparency
• Master Data Management
  • Discovers and accelerates onboarding of new data sources that should be part of your master data
  • Infers and recommends additional attributes and hierarchy structures to simplify enrichment of master data models
• Cloud Modernization
  • Enables comprehensive understanding of data landscape to help prioritize datasets and workloads for cloud migration
  • Provides detailed lineage and impact analysis to support cloud migration with minimal disruption
• Data Integration
  • Speeds up development of data integration pipelines with recommendations on mappings to extract, transform and deliver data
  • Automatically derives structure from messy device and log files, making them easier to understand and work with
• DevOps for Data Management
  • Provides predictive analytics and recommendations for future capacity planning
  • Helps effectively manage changes with detailed lineage views and business logic that enable what-if impact analyses