A new, less IT-centered view of BI is revolutionizing the way Business Intelligence is used.
As the modernization of Business Intelligence (BI) is ongoing, new development opportunities for companies arise. This modern BI, a notion theorized by Gartner, brings a less-centred IT view and an increase of BI processes performance and agility. However, numerous limits still slow down its implementation, essentially due to siloed organization among BI-using companies.
Current BI practices are still organized according to a top-down logic: IT teams process the queries of business teams. This organization leads to important costs for companies, especially since data to process has become ever-more important and complex. It also produces time-consuming processes: several months may have gone by between the moment business teams express their needs to IT teams, and the moment these needs are fulfilled. Finally, the processes suffer from a lack of agility, as business teams cannot access to data themselves and do their own research. Due to these limits, conventional BI methodology has become obsolete for it cannot answer to the businesses’ demand for increased agility, fast access to data and fine-tuned analysis.
In this context, modern BI, which focuses on building a centralized governance among the IT departments as well as a decentralized self-service data analysis, gives an answer to these challenges. This new organization brings a data process optimization and allows technical innovations, like the implementation of AI-based augmented analytics and of more end-user-centred BI tools. According to Gartner’s Magic Quadrant for Analytics and Business Intelligence Platforms (2018), modern BI allows companies to generate twice as much value as those which still use conventional BI . In addition, companies using modern BI generate twice as much value relative to their analytics investment.
However, despite all these benefits, modern BI is still not well established in companies for their IT-processes still suffer from siloed organization.
Thus, which practices companies need to adopt to get the most out of data?