Frequently Asked Questions

A Data Product delivers value to its consumers. It is not just a table in a database or a standalone dashboard. A Data Product goes through a defined process, meets specific quality requirements, and delivers sustained business value. It can surface as a report, an API, an AI algorithm, or other formats, but the key criterion is always: does it create value for someone?

A data asset is raw or semi-structured data that does not yet serve a specific business purpose. A data project is time-bound and focused on answering a single question. A Data Product, by contrast, is ongoing: it is continuously managed, maintained, and improved. Think of it this way: you don't build a car to use it once.

DRIVE stands for Data Retrieval, Integration, and Value Extraction. It captures the core lifecycle every Data Product goes through. First, you retrieve data from source systems. Then, you integrate and transform it into a usable structure. Finally, you extract value by making it accessible and actionable for its consumers. DRIVE builds on proven engineering practices like ETL, but goes further by embedding governance and people into every step.

GAP stands for Governance, Architecture, and People. These three dimensions must be present at every step of building a Data Product. Governance ensures quality, trust, and compliance. Architecture provides the technical backbone. People bring domain knowledge, collaboration, and accountability. Technology alone is never enough.

Neither extreme works well on its own. Centralized setups offer consistency and governance but tend to be slow. Decentralized setups are closer to the business but risk creating silos and governance drift. The recommended approach is a Hub-and-Spoke model: a central Hub defines standards and operates shared platforms, while business-unit Spokes maintain ownership of their domain-specific Data Products.

The financial impact of a Data Product can typically be measured across four dimensions: cost reductions through higher productivity, increased revenues from cross-selling or upselling, new revenues from data monetization, and efficiency gains from faster processes. The benefits must clearly outweigh the total cost of ownership, which includes development, infrastructure, integration, and ongoing maintenance costs.

CIA stands for Continuous Improvement and Adaptation. A Data Product is never truly finished. It must evolve to keep up with shifting business needs, new data sources, and changing user expectations. CIA is not a standalone step in the lifecycle. It is a mindset that spans the entire DRIVE process, driven by user feedback, usage analytics, and structured iteration.

Every Data Product has a business dimension and a technical dimension. The business dimension covers aspects like clear purpose, consumer relevance, usability, and discoverability. The technical dimension covers composability, security, shared platforms, data quality, and monitoring. Both dimensions must work together for a Data Product to succeed. A technically perfect pipeline without business value is not a Data Product.