Shifting the data measurement mindset from POC to MVP
One of the best practices, a lot of orgs. follow the idea of doing proof of concept for data products before crystallizing the solution using a data engineering owned ETL framework. Anytime a new metric or a new product feature is launched, it is natural for product leaders to figure out a way to measure it. If you look at this new product or feature from analytics lens, everything associated with this is considered to be" brand new". So, when releasing a new metric to measure a new product feature or the business impact the feature is creating , the first step is to create a POC metric, build it using a temp. data pipeline using an in-house querying tool or a manual SQL, build a dashboard on top of this unstable data pipeline and release it to the stakeholders. Let us understand the rationale why analytics leaders naturally gravitate to this POC approach. 1. Speed to market Organizations need to really analyze how "quick" is this quick method. Typically, these qui...