Data Assist agent:

• Data profiling: Seamlessly analyze a dataset to understand its structure, content, and quality. Automatically generate statistics, detect issues like nulls or duplicates, and document findings.

Data Assist agent:

• Data mapping: Manage the entire data mapping journey with a centralized, industry-agnostic solution. Improve efficiency, accuracy, and standardization for any data modernization program.

Data Assist agents:

• Data modeling and review: Compare legacy data models to modern schemas, remodel legacy code, and recommend new attributes when no direct mappings exist. Validate application data structures against enterprise standards to improve accuracy, efficiency, and alignment with business data modeling guidelines.
• Data pipeline engine: Accelerate cloud data migration with automated, customizable extract, transform, load (ETL) pipelines, audit capabilities, and SmartMap integration for source-to-target mappings. 
• Reverse data engineering: Reverse engineer legacy ETL code to generate comprehensive pipeline mappings including source-to-target transformations. 
• Data platform migration and modernization: Convert data from tech stack to tech stack.
• Test case generation, execution, and monitoring: Create and run column- and transformation-level tests using mapping and actual data comparisons to report where the modernized code matches the legacy code, or where it diverges from the legacy code or the initial requirements. 
• Synthetic data generation: Generate realistic, schema-compliant synthetic datasets for testing and training, to support both user-driven (large language model) and sample-based (generative adversarial network) generation.

Data Assist agents:

• Smart data quality: Create data quality rules for critical data elements using business glossary definitions and profiling to ensure compliance with key enterprise and industry standards.
• Intelligent business glossary: Create business glossary definitions for metadata per enterprise standards, identify potential critical data elements (CDEs), and flag them for manual review.  

Data Assist agents:

• AI for BI glossary: Engage a smart chat interface to enable natural language analytics, visualization, and forecasting, featuring exportable results, query caching, and audit trails.
• Report rationalization: Extract reporting logic and mappings from existing reports, analyze them, and identify rationalization opportunities.

Data Assist solutions:

• Financial services data foundation: Pre-built data models, business glossaries, and regulatory taxonomies for Banking and Capital Markets—covering risk, finance, and compliance data structures aligned to industry standards.
• Insurance data architecture: Purpose-built reference architectures and data models for insurance operations, claims, and actuarial data, with lineage and governance controls aligned to regulatory requirements.
• Life sciences and health care data framework: Pre-assembled data models and glossaries for payer and provider environments, with sensitivity handling and compliance patterns aligned to HIPAA and applicable regulatory standards.