In this chapter, we will explore what exactly a data product is, defining the concepts of data product, pure data product, and analytical application. Next, we'll outline the four key characteristics that every data product should possess to maximize the value of the data assets it manage. We’ll also explore the main components that contribute to forming a data product, ensuring its modularity from both a structural and managerial perspective. Finally, we will examine some commonly adopted methods for classifying data products.
- Defining a data product
- Pure data products versus analytical applications
- Why do we need pure data products?
- Pure data product definition
- The rise of data-driven applications
- Exploring key characteristics of pure data product
- Popular ilities
- Relevance
- Accuracy
- Reusability
- Composability
- Dissecting the anatomy of a pure data product
- Anatomy overview
- Data
- Metadata
- Application and infrastructure
- Interfaces
- Classifying pure data product
- Source-aligned versus consumer-aligned
- Domain-aligned versus value stream-aligned
- Other classifications
- SKILL 1: Understand what a data product is, the different types of data products that exist, and how they differ from one another.
- SKILL 2: Understand the key characteristics a data product must have and how they impact the value of the data it manages.
- SKILL 3: Understand the structure of a data product and how each component plays a key role in making it a modular unit within the data architecture
- SKILL 4: Understand the different ways to classify data products according to the role they play in the data value chain.
- Figure 2.1 - Classification of digital products
- Figure 2.2 - Short-term and long-term value
- Figure 2.3 - Engineer to Order (ETO)
- Figure 2.4 - Assemble to Order (ATO)
- Figure 2.5 - Linear data value stream with pure data products in the MIDDLE
- Figure 2.6 - Circular data value stream with pure data products in the CENTER
- Figure 2.7 - Pure data product illities
- Figure 2.8- Data Value versus Volume
- Figure 2.9- Data Value versus Time
- Figure 2.10- Data Value versus Accuracy
- Figure 2.11- Data Value versus Usage
- Figure 2.12- Data Value versus Integration
- Figure 2.13 - Pure data product’s anatomy
- Figure 2.14 - Pure data product’s interfaces
For more information on the topics covered in this chapter, please see the following resources:
- Data Jujitsu: The Art of Turning Data into Product - DJ Patil (2012)
- The evolution of data products: The data that drives products is shifting from overt to covert - Mike Loukides (2011)
- The Data Product ABCs - Juan Sequeda (2022)
- Measuring The Value Of Information: An Asset Valuation Approach - Daniel Moody, Peter Walsh (1999)
- Build data liquidity to accelerate data monetization - MIT Center for Information Systems Research (2021)
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. (2016)
- Data Mesh: Data as a Product - Zhamak Dehghani (2022)
- Hexagonal architecture - Alistair Cockburn (2005)