A couple of contracts back I was consulting as a solution architect at a national retail organisation I ran an experiment as a proof of the cadence which is possible using Data Autonomy.
Shortly after the project went live, I decided to build it again myself using Data Autonomy as realistically as possible. The result was a far better solution in a third of the time & cost.
BI & analytics loves Data Autonomy and event driven architecture. In the operational side of Semantic Hub / Data Mesh, data is already clean and in business form. Data engineering can subscribe to all significant business object changes and metrics can be automatically calculated live. Dimensional modelling also becomes a much simpler process as canonical data is available near real time.
The data engineering and integration teams can become an active part of data governance and data stewardship. Working closely with the business domain SMEs, everyone is on the same page and reporting is simplified.
Data Autonomy is a collaboration of A Holistic Data Fabric/Mesh, RESTful interfaces, RESTful events and enterprise micro-services. Focused around Resource Oriented Architecture (ROA) rather than Service Oriented Architecture (SOA), its purpose is to provide the ability to change rapidly by keeping the level of dependencies constant across the IT landscape.
Standard event driven integration practice is to take data from a system, transform into a middle model and then transform to each destination. Data Autonomy simply says that while we have the data in the middle form, lets save it.
Each business domain must be free to evolve and mature independently as the view of significant business objects evolves. The data governance group with data stewards and SMEs should be responsible for producing domain aligned data product definitions which are then realized into data products. The features of these data products should cover interfaces and persistence for both operational and analytical data.