![]() ![]() They can create their own information marts, called user marts, and create their own dashboards. But power users can also extend the solution by additional data, not yet integrated by the data warehouse team. These solutions should use artifacts from the controlled area, including data artifacts from the Raw Data Vault, business rules from the Business Vault and information artifacts from one of the information marts. Managed self-service BI supports two major use cases: first, the case of power users who want to extend the data analytics platform by additional, custom-built solutions. Reasons for Self-Service and Data Science The goal of managed self-service BI is to turn these decisions into rational decisions by an educated, i.e., knowledgeable decision-maker. Too often, this decision-making process is often based on gut-feeling. And there are plenty of them: in our opinion, every employee, from the C-level down to the lowest level, needs to make decisions – otherwise one would question why their job is not automated yet. Here, the goal is to make data and information widely available to any decision-maker. This concept, referred to as “managed self-service BI,” is used to transform organizations into a data-driven organization. While the data warehouse team provides the infrastructure and many capabilities, it is driven by power users and data scientists. In this article, we are discussing another extension to the reference architecture: the integration of custom solutions for power users and data scientists. These architectures have one thing in common: they are setup and further developed by the data warehouse team. In our previous articles of this blog series, we introduced Data Vault 2.0 as a concept for designing data analytics platforms and introduced both the Data Vault 2.0 reference architecture and the real-time architecture. ![]()
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