Page 244 - Data Architecture

Chapter 6.3: Introduction to Data Vault Architecture

How Does Managed Self Service BI Fit in the

Architecture?

First, understand that self-service BI in and of itself is a misnomer. It emerged in the
market in the 1990s as federated query engines, also known as enterprise information
integration. While it is a grand goal, it never truly was able to overcome technical
challenges that vendors touted it would. In the end, a data warehouse and business
intelligence ecosystem are still needed in order to make accurate decisions. Hence, the

term managed self-service BI is feasible and readily applicable to the solution space
discussed in this book.

That said, Data Vault 2.0 architecture provides for the managed SSBI capabilities with
the injection of write-back data (reabsorbing data on multiple levels) either from direct
applications (sitting on top of the data warehouse) or from external applications like SAS,
Tableau, QlikView, and Excel, where the data sets are physically “exported” from the
tools after having been altered and fed back into the warehouse as another source.

The difference then is that the aggregations and the rest of the soft business rules rely on
the new data in order to assemble the proper output for the business. The soft business
rules (i.e., code layers) are managed by IT, while the processes are data-driven, and the
business manages the data. An example of this can be found in the simple example of
allowing businesses direct access to managing their own hierarchies.

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