Product Thinking In Data — Part-1

The introduction.

Hello, one and all, and welcome to a long series of articles combining my thoughts on the abovementioned topic. Data Engineering/ Data Platform/Data Science — all these fancy terms are relatively new. Believe me, they are new, Tech stacks like RDBMS, and Excel have been there since the '90s and are still used, so new. Data and its storage, aggregation, querying, and analysis have been there for ages (Data stored in an Oracle database was/is queried and used for decision-making). But with everything getting more and more modular and distributed and then de-centralized and then again de-normalized or collated, the simple concepts of storing data, querying, analyzing, and presenting it have evolved for multiple reasons (one being the sheer size of the data).

So What is Product Thinking in Data? Do we even need this thinking? And why go into the hustle of understanding or treating your data platform, ETL pipelines, or the querying layer as a product or service? What benefits do we gain here? What about Products/Services built on top of these underlying platforms? Why even bother reading the rest of the article? It's worth it.

Let's start with my favorite point — Why The need for Product Thinking?
When we start thinking about products, we essentially are talking about end-users/consumers who will actually use the product. They are the persona actually paying out of pocket and for which the business exists. I am not really going into detail about Product and Product -management, there are…