Solving Data Inefficiency: Setting Up for Long-Term Success

Many organizations find that adding new datasets or extending toolsets to cater for AI takes too much time and effort. This often stems from technical bottlenecks and a lack of foundational engineering that leads to duplicated effort across teams. In our experience, these hidden inefficiencies mean that a large number of data platforms require a significant rebuild to optimize the successful delivery of modern Data & AI projects. Thorogood has worked with leading enterprise organizations across industries to engineer data flows that eliminate friction. In this video, we share our experience in identifying and resolving the structural issues that stall progress. We discuss the specific steps required to create a data flow framework that addresses business context while leveraging the power of technologies like Databricks.