Prior to establishing intricate metrics or constructing pipelines, a Data Product's success hinges on understanding the user's needs, their tasks, and the specific information necessary to complete them.
The effectiveness of a data product depends on its alignment with these three elements. Failure to address all three will hinder adoption.
Who is using this data? It is crucial to have a thorough understanding of their technical skills, daily tasks, and preferred methods of information consumption.
What is the purpose of the task? Data only holds value when it is used to make informed decisions, take action, or automate processes that propel the business forward.
Which specific details are needed to assist the User with the Task? This includes determining the schema, latency, history, and quality SLA necessary.
The responses from the User, Data, and Task trio heavily influence your engineering approach. Output Ports The delivery of data may require multiple output ports to accommodate various triads.
Failure is inevitable if you create an exquisite real-time API (Data) for a CFO (User) solely interested in receiving a monthly PDF report (Task).
User: CMO (Non-technical)
Task: Monthly Budget Allocation
Data: Highly aggregated, historical
→ Build a curated BI Dashboard View.
User: Recommendation Engine (Machine)
Task: Serve live product suggestions
Data: Granular, sub-second latency
→ Build a high-performance REST API.
Before writing any SQL code, begin defining your data products with a strong focus on the User and their Task.