As agents assume more of the implementation and operational work, organizations will need to reconsider what data engineering is intended to deliver.

For years, organizations have invested heavily in making data platforms more reliable. Infrastructure is monitored around the clock. Data pipelines generate alerts when jobs fail. Data quality ...

An unpopular opinion from someone who builds analytics for industrial operations every day: most companies in distribution, manufacturing, and marine transportation don't have an analytics problem.

Generative AI is changing how organizations interact with data and analytics. Rather than navigating dashboards, writing SQL, or relying on predefined reports, business users can increasingly ask ...

It is a demanding time for data engineering. Many enterprises have already modernized their data platforms once by moving to the cloud, but the pipelines that fed those platforms often remained the ...

Typically, what most validation pipelines do when there’s a failure is report some kind of error code or even a line number and leave a human to make sense of what ...

Analytics teams already understand the cost of poor data. Bad definitions create conflicting dashboards. Missing fields create manual reconciliation. Stale records weaken forecasting. In agentic ...

Success with agentic AI will depend on much more than selecting the right language model. Organizations are moving beyond generative AI assistants to explore intelligent agents that can reason, plan, ...

SEATTLE, WA—TDWI Research has released a new original research report. Based on survey data and focus group data, the TDWI Blueprint Report: Building an AI-Ready Data Foundation explains a capability ...

Enterprise AI budgets are expanding at a pace that is making CFOs pay close attention. According to a 2026 survey of 2,360 senior executives, companies expect to spend approximately 1.7% of their ...

As generative AI, copilots, and agentic AI systems move from experimentation into production, organizations are discovering that long-term AI success depends on the strength of the underlying data ...