The vendor is required to provide data intelligence platform that supports a modernized data ecosystem to provide a unified data governance solution built on a foundation of quality data that will accelerate data innovation and achieve better ai outcomes.
- Software as a service, infrastructure as a service, platform as a service
1. Identity and access management
• Custom roles and data privacy and security
2. Usability and collaboration
• Friendly user interface for enterprise users – both business and technology users
3. Stewardship management
• Offers data stewards and data citizens a platform to collaboratively manage data assets, leveraging workflow-driven processes to streamline data governance and ensure consistent decision-making.
4. Customization
• Custom attributes and custom relationships
5. Quick data discovery    
• Enables users to easily search and explore data assets across the enterprise data platform, improving accessibility and empowering data-driven decision-making.
6. Standardized business glossary
• Provides a centralized glossary to publish and manage common definitions, ensuring a consistent understanding of terms and reducing ambiguity across the organization.
ease of maintenance of these glossaries.
7. Data contextualization
• Allows users to add context through tags, classifications, and custom attributes, enhancing data discoverability and making it easier to assess data relevance and quality.
8. Ease at import from other catalogs and export - import from cloud provider (all the cloud providers
• Allows users to add context through tags, classifications, and custom attributes, enhancing data discoverability and making it easier to assess data relevance and quality
9. Metadata collection
• Ability to collect metadata at a granular level (field) as data elements moves through the enterprise data platform
10. Metadata lifecycle management
• Metadata acquisition, metadata integration, metadata presentation, and metadata deprecation options
11. Metadata versioning
• Capability to store version information for metadata updates, the sources of updates, and if necessary, reverse updates to the original values.
12. System interface
• Functionality (API, connectors etc.) to integrate with other systems and tools in the ecosystem
13. Data support for various types of data
• Data support for various types of data - structured, unstructured, semi-struck turned, streaming, IoT
14. Policies-rules management
• Ability to create and manage quality and transformation rules at a granular level
15. Profiling
• Profiling of diverse data sources
16. Data quality
• Ability to assess different data quality dimensions
17. Collaborative workflows
• Pre-defined workflows for resolving DQ issues
• Custom workflows and templates for DQ issues
• Integration with external ticketing systems
• Chat windows on DQ rules & issues reported
• Report DQ issue
18. Proactive alerting
• DQ rule execution schedule
• Service request tickets for failed DQ rules
• Cautions and red flags on data objects
• Downstream alerts
19. Investigative features
• DQ scores on lineage
• Root cause analysis
• Remediation center
19. Profiling capabilities
• Profiling schedules
• Insights of data through profile statistics
• Profiling modes for process optimization
20. Anomaly detection
• Trend analysis
• Anomaly management center
• Configurable anomaly checks
21. Data cleansing
• Data cleansing
• AI-based data cleansing tools identify and correct errors during data ingestion for better data quality.
22. Lineage
• Ability to visualize end-to-end data lineage to understand how data sets sourced, built, transformed, aggregated, and used
23. Data modeling
• Entity relationship models - conceptual, logical, and physical data models
24. System interface
• provides comprehensive APIs in any data management tool, enabling seamless connectivity with external systems to support smooth metadata exchange and holistic governance.
25. Adaptive data integrations
• Provides the ability to create custom integrations within any data management tool, ensuring seamless data exchange across various systems and platforms.
26. multi-format data handling
• Governs diverse data formats within any data management tool, ensuring comprehensive data consistency and quality across structured, unstructured, and semi-structured data.
27. Real-time and streaming data support
• Manages streaming and IoT data within any data management tool, enabling real-time governance, which is essential for organizations that require instant data insights and action.
28. Deployment architecture: cloud, on-prem
• Cloud (SaaS or PaaS or IaaS), on-premises
29. Reporting (business, quality metrics)
• Dg metrics, quality scorecard, bi and analytics dashboards for monitoring and decision making
30. Entity matching based on rules
• Matching, merging, unmerging
31. AI and ML capabilities:  trend analysis
• Trend analysis: provides transparency into the trend of data objects, enhancing the understanding of their behavior and quality progression over time.
- Contract Period/Term: 12 Month
- Questions/Inquires Deadline: April 02, 2025