Why modeling data for analytics and data science consumption is hard?
Follow-me-Home Blog Series with Data Engineers, Analysts, Scientists, AI developers #1
About this Blog Series: Every week, I dive into conversations with teams of data professionals working at the forefront of the industry — Data Engineers, Analysts, Scientists, AI developers, ML Engineers, and Data/MLOps experts. These are the people who are hands-on, navigating the challenges of transforming raw data into real business impact. I explore the critical steps they take and the roadblocks they encounter. Each blog focusses on a key task from the journey map that I dug into further during the conversation.
Designing Data for consumption
The Story of Casey and Taylor
In any tech-driven organization, the ability to generate timely insights from data is paramount.
Meet Casey (fictional name), a business analyst tasked with tracking the performance of a recently launched product. Casey understands the business context, key performance indicators (KPIs), and the strategic needs of the organization. They are also comfortable with using tools like SQL, business intelligence (BI) platforms, data science frameworks to analyze data. However…
