{"id":7161,"date":"2024-12-24T15:25:01","date_gmt":"2024-12-24T09:55:01","guid":{"rendered":"https:\/\/blogs.infosys.com\/digital-experience\/?p=7161"},"modified":"2024-12-26T11:30:41","modified_gmt":"2024-12-26T06:00:41","slug":"generative-ai-devising-data","status":"publish","type":"post","link":"https:\/\/blogs.infosys.com\/digital-experience\/emerging-technologies\/generative-ai-devising-data.html","title":{"rendered":"Generative AI – Devising Data"},"content":{"rendered":"

\"'Blend<\/p>\n

‘Blend -> Mend -> Tend’<\/strong><\/h1>\n

This blog is focusing on how data integrations tools are playing key role in Artificial Intelligence and high-level view of industry’s tools on data integration. Gartner’s Magic Quadrant details on Tools for Data Integration are good place to start.<\/p>\n

Looking at Generative AI role in enhancing data integration tools. Some views on the same:<\/p>\n

By 2027, AI-enhanced workflows (including AI assistants) in data integration tools will increase self-service of data management and reduce human intervention by 60%.
\nAugmentation Features: Leveraging GenAI and prepackaged ML algorithms to auto-generate data pipeline code and documentation, optimize data integration operations (e.g., anomaly detection, auto-recovery), and use natural language to query and transform data.<\/p>\n

 <\/p>\n

Gartner’s details on Data Integration Tools in below <\/span>Magic Quadrant<\/span>\u00a0<\/span><\/h3>\n

\"\"<\/p>\n

Market Overview<\/h2>\n

Growth: The tools in market for data integration grew by 9.8% in 2023, driven by modern data integration requirements and cloud data ecosystems.
\nTrends: Tools must support hybrid and multi-cloud deployments, multiple user personas, and modern data management architectures like data fabric, data mesh, and lake-house.<\/p>\n

Technical Overview of Data Integration Tools<\/h2>\n

Data Extraction and Delivery<\/h4>\n
    \n
  • Styles: Bulk\/batch, replication, streaming, virtualization.<\/li>\n
  • Connectors: Out-of-the-box and configurable for seamless data access.<\/li>\n<\/ul>\n

    Data Transformation<\/h4>\n
      \n
    • Levels: Basic (string manipulation), intermediate (data source merging), advanced (complex parsing).<\/li>\n
    • Components: Prebuilt, reusable, configurable, or custom.<\/li>\n<\/ul>\n

      Data Preparation<\/h4>\n
        \n
      • Techniques: Low-\/no-code ingestion, basic modeling, data blending, visual exploration.
        \nAugmentation<\/li>\n
      • Capabilities: Generative AI, prepackaged ML algorithms for pipeline optimization.<\/li>\n<\/ul>\n

        Metadata Management<\/h4>\n
          \n
        • Features: Discovery, access, sharing of technical and operational metadata.<\/li>\n<\/ul>\n

          Data Governance<\/h4>\n
            \n
          • Functions: Data quality, lineage, policy enforcement, masking. Vendor-Specific Realizations<\/li>\n<\/ul>\n

             <\/p>\n

            Vendor Analysis<\/h3>\n
            \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
            Vendor<\/strong><\/td>\nLeaders In<\/strong><\/td>\nImprovements Needed<\/strong><\/td>\n<\/tr>\n
            Ab Initio Software<\/td>\nEnterprise focus, AI capabilities, customer satisfaction.<\/td>\nHigh price, operational complexity.<\/td>\n<\/tr>\n
            Amazon Web Services (AWS)<\/td>\nZero-ETL integrations, serverless architecture, support for multiple personas.<\/td>\nHigh cost, limited multicloud vision.<\/td>\n<\/tr>\n
            CData<\/td>\nStrong sales strategy, low TCO, data virtualization.<\/td>\nPoint solution approach, evolving market positioning.<\/td>\n<\/tr>\n
            Confluent<\/td>\nStream data integration, data governance, modern data management support.<\/td>\nLimited nonstreaming integration, high cost.<\/td>\n<\/tr>\n
            Denodo<\/td>\nData virtualization, partnership growth, customer experience.<\/td>\nPhysical data movement limitations, distributed deployment management.<\/td>\n<\/tr>\n
            Fivetran<\/td>\nConnectivity, ease of use, scalable pricing.<\/td>\nLimited transformation capabilities, basic metadata support.<\/td>\n<\/tr>\n
            Google<\/td>\nAI-aided workflows, data governance, developer experience.<\/td>\nGoogle-centric products, complex portfolio.<\/td>\n<\/tr>\n
            IBM<\/td>\nData integration vision, global presence, streaming capabilities.<\/td>\nHigh cost, solution complexity.<\/td>\n<\/tr>\n
            Informatica<\/td>\nMetadata use, AI-ready data vision, mature portfolio.<\/td>\nSlower growth, migration challenges.<\/td>\n<\/tr>\n
            Microsoft<\/td>\nMarket momentum, broad ecosystem vision, AI-powered capabilities.<\/td>\nLimited hybrid\/multicloud vision, gaps in supporting capabilities.<\/td>\n<\/tr>\n
            Oracle<\/td>\nMulticloud vision, complex architecture support, operational integration.<\/td>\nHigh cost perception, limited selection outside Oracle ecosystem.<\/td>\n<\/tr>\n
            Qlik<\/td>\nProduct portfolio, data replication, governance.<\/td>\nPace of R&D, price increases.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

             <\/p>\n

            Innovation and Future Trends<\/h2>\n

            GenAI Integration:<\/h4>\n

            Vendors are increasingly integrating GenAI to enhance data integration capabilities, automate complex tasks, and improve user experience.<\/p>\n

            AI-Ready Data:<\/h4>\n

            Tools are evolving to support the creation and management of AI-ready data assets, enabling more efficient and effective AI applications.<\/p>\n

            Conclusion<\/h3>\n

            Each tool\u2019s placement in the quadrant is justified by its strengths in specific features and capabilities, as well as its limitations. Leaders like AWS, Google, and Microsoft excel in innovation and comprehensive support, while niche players like CData and Safe Software offer specialized solutions with lower TCO and strong customer satisfaction.<\/p>\n

            For AI architects seeking tools with less integration effort, AWS and Denodo stand out due to their strong support for multiple personas, seamless data access, and robust data virtualization capabilities. However, cost and operational complexity should be considered when making a decision.<\/p>\n

            Generative AI is playing a crucial role in transforming data integration tools by automating tasks, enhancing user interfaces, and enabling more efficient data management practices. This trend is expected to grow, with significant advancements anticipated by 2027 (may be even earlier).<\/p>\n

             <\/p>\n

            Glossary:<\/h2>\n

            Devising (verb) (present participle) — Invent or plan (a mechanism, complex procedure or system) by careful thought<\/p>\n

            Blend (verb) — form a harmonious combination<\/p>\n

            Mend (verb) — repair (something that is broken or damaged) or improve (an unpleasant situation)<\/p>\n

            Tend (verb) (tend to\/towards) — be liable to possess or display (a particular characteristic)<\/p>\n","protected":false},"excerpt":{"rendered":"

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