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.

What We Cover:

• Recognizing the indicators of inefficiency, duplicated effort, and technical bottlenecks within your organization.
• How to design and build a data flow framework that serves everything from standard reporting to complex AI and Machine Learning.
• How to set foundations that allow you to address current use cases while remaining ready for future requirements.

Is this For You?

• Are you a commercial or function lead struggling with technical bottlenecks that cause projects to take too much time?
• Are you looking to move away from duplicated manual tasks and toward standardized, reusable code?
• Are you responsible for ensuring your organization’s data platform is engineered for efficiency and ready to tap into the power of the latest AI technologies?