Data mesh is a new approach to organizational data management that helps data-driven organizations become more agile and prudent with managing, analyzing, and distributing data.
Four main principles are fundamental in a data mesh system. The first principle is domain ownership which we discussed in our previous blog post.
The second principle is data as a product of which domain data is developed and shared as a product for internal and external customers.
The following post will cover the data as a product principle and the benefits it presents to an organization.
What is Data as a Product?
Data as a product is the concept of leveraging analytical domain sourced data to develop a high utility data product to be shared and used across the organization.
In a decentralized data mesh architecture, domain teams ingest, process, and distribute their sourced data as usable data products. Developing data products at the domain source reduces friction and high costs typical to traditional data management methodologies.
Data as a product is an evolution in thinking in which external product management techniques are applied internally. Internal users of the data product are the customers, and the domain teams are responsible for the product and prioritizing the customer experience.
What Problems Does Data as a Product Address?
The value of data within a data-driven organization is uncovered through its usability. For data to become usable, it needs to be converted into a product and then shared within the organization.
Likewise, evolving the thinking around data from an asset to a product promotes an organization where data becomes part of the culture when planning, making decisions and implementing strategies.
As such, data as a product addresses the following data problems within an organization.
Data siloing: Data as a product prevents domains from becoming a funnel for data collection, and instead, domains become a data product team creating products to be shared.
Value: Data as a product increases the value of data by increasing the utility and shareability of the data.
Utility: Data as a product extracts more utility from data as teams innovate around data products, increasing the quality and trustfulness of the data.
Data as a Product Role in Data Mesh
Data as a product can be defined through its customer experience. To provide a unified customer experience, the domain teams optimize the usability of the data for the organization.
Various attributes define organizational data usability. Zhamak Dehghani best defines these attributes in her book Data Mesh.
The following summarizes the eight attributes Dehghani outlines for data usability within the organization:
Discoverable: Data products are developed with discoverability as a priority. Data users can easily search and find data products for their needs.
Addressable: Each data product includes a unique address that allows users to programmatically and consistently access the data product as it changes throughout the product lifecycle.
Understandable: Data products are developed to be easily understood by the user. Users will understand the data’s meaning, how it is presented, and how it has been used within the organization.
Trustworthy: The truthfulness of the data is established in each data product. The user has confidence in the product’s reliability and accurate facts.
Ensuring data quality in a data mesh presents unique challenges. Learn how to select the best technology for data quality management.
Natively Accessible: Data products can be read and accessed by various modes of access by different user types.
Interoperable: Data products have consistent standards for exchanging and using data.
Valuable: Data products are developed with stand-alone value for the user.
Secure: Data products contain standardized security policies.
The Challenges of Data as a Product and How They are Addressed
Below are the primary challenges of data products and the mesh principle that addresses the challenge.
Cost of ownership: Domain ownership of data products can increase the cost of data through increased data product management. This cost is mitigated through the self-serve platform principle, which reduces duplicative efforts and increases workload capacity and productivity.
Consistency of value and utility: Consistency of product quality across multiple domains can become a challenge and ultimately reduce the value and trustworthiness of the data. The federated governance principle helps address this through the application of data quality technology, global policies, and accountability across domains and subject matter experts.
What’s Next After Data as a Product?
Data as a product optimizes enterprise data by turning analytical data into a product to be shared and used across the organization. By turning data into a product, the organization reduces data distribution friction and costs and prevents data siloing from domain ownership.
To distribute the product, a self-serve platform is needed. Developing a self-serve platform is the next step in a data mesh architecture, and we will cover it in our next blog post.
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