Are you a Citizen Data Scientist?
Joel Shapiro gives a great explanation what democratization of data analytics mean but we need to use caution. To read the full article please visit it here
Thoughts?
The array of tools available for data analytics is both remarkable and daunting, raising the question of how to stay current. Most data resides across various systems and often requires cleaning, merging, and, if necessary, aggregating. While not beyond the capabilities of a Citizen Data Scientist, it's important to recognize that data analysts and engineers, with their deeper familiarity with the data, are crucial participants in this process.
The belief that data, as received from the source system, is ready for analysis is a misconception. Such data is usually extracted from a system of records, which often includes unique attributes like effective times and various indicators not commonly encountered. Despite the potential of AI to draw inferences, it is imperative to rigorously validate the data, ensuring it aligns with expectations from its source to its analytical representation.
When Subject Matter Experts (SMEs) become overly confident or perceive themselves as the ultimate authorities on their data, it can lead to friction with analytics departments, sparking debates over correctness. SMEs may tend to tailor logic around their data to confirm their version of "correct," overlooking potential discrepancies in the source system. This approach can result in a significant departure from reality, underlining the need for a balanced approach that respects both the empirical integrity of data and the contextual expertise of SMEs. It's about finding a harmony between technical data processing and the nuanced insights provided by those closest to the data, ensuring analytics are both accurate and meaningful.
