Centralized BI is the system of storing and managing all business intelligence, or data, in one central location or department – typically corporate – for governance and security reasons. This model ensures data accuracy, veracity, and economy of scale.
However, companies with centralized data warehouses may also experience significant bottlenecks because all data requests go into a single queue. This creates a backlog for the team responsible for those requests – often the IT department – and ultimately results in frustration and loss of time and money. But the other option, a truly decentralized environment, can also be problematic. It may result in insufficiencies or redundancies in BI software, staff, and applications across a company.
On the one hand, organizations today want centralized data – for governance and security reasons. At the same time, organizations want a decentralized mode of operation for data discovery as well as to find new insights in a self-service fashion without increasing the workload of the reporting team.
The “best of both worlds” approach is distributed BI, a form of decentralized BI that gives individuals greater freedom to use data while still governing their access to that data in a centralized and scalable manner. In short, distributed BI makes self-service analytics possible. It allows users to discover data insights on their own without increasing the workload or creating a backlog for the reporting/IT team.