Automated Ingestion and Data Quality Services

The number of data sources, including internal and external datasets, within an enterprise is consistently increasing. Consequently, the processes that perform data ingestion and quality checks also grow rapidly. This situation poses challenges in maintaining the code base, increasing the risk of errors, and demands higher efforts when onboarding new datasets.

A well-designed data integration approach that brings together metadata-driven design principles for ingestion and data quality checks will ensure that the system complexity is effectively managed even as the data sources and its datasets scaleup.

WinWire’s WinAIDQ (Automated Ingestion and Data Quality) solution accelerator enables enterprises to adopt a modern and reusable metadata-driven data ingestion and data quality framework by bringing together well-defined processes and best practices which increases productivity and reduces the TCO.

WinAIDQ Solution Approach

• Discovery - Assess existing data estate – source systems, data, volumes, change data capture.
• Pilot Solution - Setup AIDQ code library, config tables, map fields, choose DQ checks, and validate outputs.
• Scale-out Plan - Define the roadmap and Enterprise-wide scale-out plan for onboarding data using the AIDQ framework.
• Enable agile business with the well-architected scalable data integration pattern based on the Azure data services.

Business Value

• Up to 50% reduction in overall development time for integrating data assets.
• Source and schema agnostic capability delivering reusable data platform assets.
• Standardized deployable artifacts and minimal maintenance.
• Enable agile business with the well-architected scalable data integration pattern based on the Azure data services.

Key deliverables

• Architecture and design for data ingestion and data quality management
• Working AIDQ solution
• AIDQ adoption plan for onboarding data sources
• Capacity planning for optimized leverage of the Azure data services.

Leverage our rapid 4 weeks pilot solution and experience the value of running a metadata-driven data ingestion and data quality solution.