Why teams struggle to get data instrumentation right?
Follow-me-Home Blog Series with Data Engineers, Analysts, Scientists, AI developers #2
In a world driven by data insights and decisions, the importance of precise and consistent data instrumentation cannot be overstated. For data scientists and analysts, reliable data is foundational to everything from simple reporting to sophisticated predictive modeling. However, all too often, gaps in data instrumentation create significant roadblocks, hampering the ability to extract value from data effectively. Here, we examine a common scenario that highlights the challenges of data instrumentation, the impact these challenges have on business goals, and actionable solutions to address root causes across people, processes, and technology.
Section 1: The Story of Rob, Sue, and Maggie
Consider a scenario involving Rob, Sue, and Maggie, each of whom plays an essential role in launching a new feature. Their individual responsibilities are clear, but misalignment in data instrumentation leads to a cascade of issues that affect the entire project.
Rob, a product manager, is focused on defining a product feature. He diligently documents the user stories, functional requirements, and key objectives in a Product…
