Build vs. buy for AI: Choosing the right data foundation

A new MIT Technology Review Insights report reveals that 64% of C-suite executives prioritize data readiness for AI success, but face challenges in building the necessary data foundation. The biggest pitfalls include data integration and pipelines, with 45% of respondents citing data integration as their top challenge. Legacy DIY methods are often used for enterprise data pipelines, leading to average losses of $406 million per year. Automated, reliable, and secure data integration is crucial for trustworthy AI, but many organizations struggle with this. The report highlights the importance of a strong data foundation for GenAI, as well as the need for a data quality mitigation strategy like retrieval-augmented generation (RAG) to incorporate proprietary business data. DIY data pipelines do not scale and can become costly liabilities over time. Modern, automated solutions like Fivetran offer built-in schema change support and automatic propagation of data source changes, reducing the operational burden and improving AI performance.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.