In our previous post, we discussed the opportunity of developing a data mesh architecture and how it can benefit data-driven enterprise organizations.
In the next four posts of our data mesh series, we will cover the fundamental principles that make up the organizational functionality of a data mesh. This post will cover the first principle, Domain Ownership, and the evolution of decentralized data management.
What is Domain Ownership?
Domains represent natural business units in an organization where data is sourced or consumed.
Therefore, domain ownership decentralizes the data ownership and management responsibilities to the business units or domains within an organization, creating data management at the source.
Domain ownership changes the flow of data within the organization. In a traditional architecture, data flows from the source to a centralized system, like a data lake, to be ingested, processed, and served. This centralized system is commonly where new individuals or groups take responsibility for owning the data. In a data mesh, the domain is the source where data is ingested, processed, and served to the organization.
What Problems Does Domain Ownership Address?
As enterprise organizations become increasingly data-driven, there is a higher demand for acute knowledge of the data source to quickly process and share data in a valuable and agile way.
Rapidly understanding and processing data is a challenge with traditional centralized architecture where data is partitioned by technology whose data engineers work with multiple sources.
Domain ownership solves these challenges in the following three primary ways.
Knowledge responsibility: Domain ownership allows teams closest to and most knowledgeable of the data to own it, improving the value and truthfulness of the data outputs.
Change and agility: Data modeling is localized to the business units most familiar with the data through domain ownership. This localization allows for more agile modeling and the ability to implement change without centralized coordination.
Scaled data use and sharing: Increasing the number of sources ingesting and processing data means higher data utility and output, leading to increased sharing and consumption.
Domain Ownership Role in Data Mesh
Data ingestion and processing: In a decentralized architecture, the domains are responsible for ingesting data for immediate use or storage. Likewise, they are responsible for processing or cleaning data for analysis and insights.
Analytics and data product: Once domains have ingested and processed the source data, they are responsible for analytics and developing a data product to be distributed and shared.
Domain data can be classified into three variations.
Source-aligned domain data: Data facts are generated by business operations. Source-aligned data most closely relates to domain events generated by domain operational systems.
Aggregate domain data: Data bound from multiple domain sources to form a data product.
Consumer aligned domain data: Data transformed for specific use cases across organizational functions.
These variations represent how domains interact with the data and develop subsequent data products.
The Challenges of Domain Ownership and How They are Addressed
Domain ownership can have a unique set of challenges that must be addressed. Below are the primary challenges and the mesh principle that addresses the challenge.
Risk of data siloing to where domains collect data and are not incentivized to share. This risk is addressed through the data as a product principle.
Lack of empowerment across domains to share data within the organization. This challenge is addressed through the self-serve platform principle.
The risk of poor engagement and isolation occurs if there is no structure for accountability, domain organization, global operation, and policy. This challenge is addressed through the federated governance principle.
What’s Next After Taking Domain Ownership?
A domain ownership approach allows data management at the source, with the source experts overseeing the data. This approach allows for reduced friction and scalability of serving analytical data to organizational data consumers. Domains allow the mesh to work by using domain data products upstream and downstream.
To learn more about the next three principles of data as a product, self-serve platform, and federated governance and how they support the challenges of domain ownership, stay tuned for our upcoming blog post or sign up below to have them delivered directly to your inbox.