Data Product 101:
The Overview.
Learn how to turn your organization's data from a passive resource to an active, value-producing asset by embracing the Data Product mindset.
1. Why Data Products?
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.
- Scale & Speed: Giving domain experts the autonomy to build at their own pace by decentralizing ownership.
- AI Enablement: Machine learning requires trusted, structured data not messy data swamps.
- ROI Focus: Transforms data from an IT expense hub to a quantifiable driver of business value.
2. What is Data as a Product?
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.
The 6 Core Characteristics:
3. The Core Frameworks
Modern data teams depend on two separate operational frameworks to effectively construct and oversee a Data Product.
The Drivetrain Approach
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.
The Product Lifecycle
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.
4. How DataKnobs Delivers Value
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 StudyThe DataKnobs Execution Formula
Strict Data Contracts
Guarantees schema stability and quality before the product is published.
Standardized Ports
Data is only accessible via secure REST APIs, GraphQL, or certified SQL views.
Cross-Functional Pods
Constructed by distributed teams consisting of a Data Engineer, Product Manager, and Domain Expert.