Historically, the people, processes, and technology that help businesses use data and analytics to make smarter decisions and work more intelligently. More recently, business intelligence refers to the tools for creating reports and dashboards.
Successful business Intelligence (BI) projects require many considerations and much planning. Many organizations focus most of their attention on the technology stack or on designing dashboards. The reality is that software projects require more than just design questions or general requirements gathering to create success. Companies need to look at BI for its potential and develop a project roadmap that deals with all of the components required for the project as opposed to only identifying business and technical requirements without the broader implications.
Business intelligence initiatives always include more than just the front end tools. The technical architecture also includes data warehouses and data marts, data integration and data quality components, dictionaries, repositories, and many other technologies. More importantly, organizations should have a proper BI strategy that goes well beyond an architecture blueprint to include non-technical requirements, alignment with the corporate strategy, organizational models, outcome-based priority settings, and a proper roadmap.
Business intelligence (BI) and data environments are notoriously heterogeneous and fragmented. Companies have multiple BI tools, each with their own semantic models, portals, file formats, reports, and dashboards. The situation is even more dire on the data side: there are dozens, if not hundreds or thousands of data warehouses, data marts, data sets, and data silos scattered throughout an organization. But rather than fight the fragmentation through enterprise standardization, perhaps it’s wiser to simply go with flow. Ride the wave, instead of swim against it.