Learn how to turn your organization's data from a passive resource to an active, value-producing asset by embracing the Data Product mindset.
Conventional centralized data structures like warehouses and lakes have become significant obstacles due to their inability to scale, as the central IT team lacks the necessary domain knowledge to quickly generate insights.
Approaching data as a product involves applying traditional product management techniques to datasets, viewing data as a strategically designed asset tailored to meet the needs of a particular audience, rather than just a byproduct.
Modern data teams depend on two separate operational frameworks to effectively construct and oversee a Data Product.
An approach to creating prescriptive data products that begins with the desired outcome and traces back to the data, rather than starting with data and searching for a problem.
Data is always a work in progress. A Product Manager must constantly manage a Data Product, cycling through an endless loop of development and enhancement.
DataKnobs, a leading data firm, does not manually create data products. Instead, they employ a scalable 'Factory Model' to consistently produce valuable business outcomes.
They concentrate on transforming raw data through a range of abilities: from uncovering past events to forecasting future outcomes, ultimately. recommending and automating the optimal action.
Read the full DataKnobs Case StudyGuarantees schema stability and quality before the product is published.
Data is only accessible via secure REST APIs, GraphQL, or certified SQL views.
Constructed by distributed teams consisting of a Data Engineer, Product Manager, and Domain Expert.