Prior to creating intricate metrics or constructing systems, every effective Data Product must understand the overlap between the user, their task, and the specific information needed to complete it.
The success of a data product depends on the alignment of three key elements. If even one is missed, the product's adoption will suffer.
Who is using this data? It is important to have a thorough understanding of their technical expertise, daily routines, and preferred methods of information consumption.
What does the 'Job to be Done' entail? Data only holds value when it is utilized for a particular action, decision, or automated process that propels the business forward.
What specific data is needed to assist the User with the Task? This determines the schema, latency, history, and quality SLA needed.
The responses of the User, Data, and Task trio greatly influence your engineering approach. Output Ports Delivering the data may require multiple output ports in order to accommodate various triads.
Your product will fail if you create a stunning real-time API for a CFO who simply needs a monthly PDF report.
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 by placing a strong emphasis on the User and their Task.