What is Business Intelligence Architecture?
Business intelligence architecture refers to the infrastructure that organizations use to define their data collection streams, information administration, and all the technology that supports their business intelligence.
Creating a sustainable architecture depends on understanding the different components that are involved with developing successful business intelligence tools. The process is broadly divided into three areas: data collection, information management, and business intelligence.
The first area refers to the different channels and methods of collecting data from activities carried out within your organization. This includes understanding which data different users need to meet their requirements, as well as a clear idea of the quality, type, and currency of the data. This step is vital for adding value as the right data produces the best BI insights.
The second major component is data management. This covers various aspects of integrating data, scrubbing datasets, and fashioning the overall structures that will house and administer data.
Finally, business intelligence is the part of an organization’s architecture that analyzes properly organized data sets to produce insights. This area involves using real-time analytics, data visualizations, and other BI tools.
See it in action:
How Can I Use Business Intelligence Architecture?
Regardless of the organizational type, it’s important to carefully plan your data analytics and collection strategies to create a robust and effective framework for your business intelligence.
Most critically, business intelligence architecture will inform different areas of a project, including development, implementation, and usage.
One useful way to create a valuable and efficient architecture is to begin by considering an organization’s data sources and what intelligence needs it exhibits. This will inform the different streams your organization will use and help identify the best sources of data.
With data sources selected, choosing how to parse it most efficiently will help deliver better insights over the long run. In this case, selecting which data is valuable and how to store it most effectively represents a crucial decision.
Poorly sorted data can affect the quality of the overall intelligence. Finally, picking the right data analytics tools can help provide different perspectives on the data.
Using tools like Natural Language Processing, ad hoc analysis & querying, visualizations, and the right BI dashboards helps organize data into a digestible format that provides the most valuable insights possible.