Traditional Data Product Management
Federated data management and data product builds and sharing has little to do with traditional data product management.
- Traditional data management primarily revolves around the collection, storage, and processing of data for internal operational needs within organizations.
- It often involves managing data in siloed systems, with a primary emphasis on ensuring data quality, consistency, and compliance with regulatory requirements.
- The goal of traditional data management is to support internal business operations, such as customer relationship management, inventory management, and financial reporting, by providing accurate and reliable data for decision-making and analysis.
Modern Data Product Management
In contrast, modern ie cloud based data product management extends beyond traditional data management practices by emphasizing the development, delivery, and optimization of data-driven products and services.
- Rather than solely focusing on internal operational needs, data product management is customer-centric, with a primary focus on delivering value to external customers or end-users through data-driven solutions.
- This involves identifying market opportunities, understanding customer needs, and translating those needs into data products that address specific pain points or deliver tangible benefits to users.
Although data products can be initially designed to serve internal needs, some of them might become business assets that are commercialized (value realization happens externally). This idea of external commercialisation is usually a long term benefit and should not be the focus of your short term efforts. You have data products internally to share data, reuse data sets, decrease costs and in many use cases, enhance revenue producing products which already exist (eg an external report) and which depend on clean data and easy access to that data.
Objectives, life cycles
Streamlining processes for both internal and external data products involves optimizing the entire lifecycle of data product development, delivery, and maintenance to improve efficiency, effectiveness, and agility. Ideally, you should have one process model that enables you with minimal effort expose the internal data product to external commercialization at any moment needed. Treat every data product as if it would be necessary to make it public if business so dictates.
Contracts, Metadata
A firm must be able to enable reuse of metadata. A Data Product Blueprint is the sketch for both internal and external value realization. All of the data products have Data Contract (technical focus).
Depending on the business goals and opportunities:
- data product blueprint containing minimal business metadata and data contract combined into data product for internal purposes.
- data product blueprint with full business metadata including pricing plans and data contract with legal aspects (becomes data agreement) is combined into data product for external purposes.
==END