Every technology is open to potential disruption, and that has always been the case. New technologies change the way people approach many activities. Then even newer developments replace the disruptors. What is true for technology in general is certainly the case in business intelligence. BI has changed the way many companies collect and disseminate information so managers and executives can make more intelligent decisions. But conditions keep changing and so does the atmosphere in which BI operates. Here are four conditions that could bring on disruption in BI.
The need for true functional integration
In modern business, customers have become increasingly demanding of service and quality — and rightfully so. But satisfaction isn’t some set arbitrary number. People are satisfied within a greater context. They have their needs, but satisfaction is more than having needs met. It involves understanding what other options are, how a company can perform in a coordinated and effective way, how quickly and thoroughly issues are addresses, and how much perceived inconvenience there is. A company cannot have separate functional silos focused on their own metrics and demands. Systems and processes that can better facilitate integration will win in the market.
Partnership for BI projects
BI is not a pastime for IT. The information made available is inevitably for the managers and executives who need to for decisions. The users of the data cannot be treated as an eventual receptacle for the results. They have to be involved from planning, through development, and into production. Without participation, all the effort may go for naught as the users can become distracted by other things or dissatisfied with the process and not apply the results of the money and time invested.
No more raw data
In classical statistical analysis, which is clearly related to how BI functions, there are four stages: identify a question, collect data, analyze the data, and interpret the results. Gathering the data extends to all parts of the enterprise and beyond, as external data increasingly becomes part of the process. Collected data also isn’t enough. Someone must apply intelligence to how it is analyzed, at which point there needs to be interpretation. Software to display visualizations similarly won’t be enough.
Focus on data quality
Any data has problems. There are missing values, incorrect values, varying definitions of what the data is supposed to mean, and different expressions of what should be the same data using different scales. Accepting flawed data collections is no longer acceptable. The stakes are too high and competition is too fierce. There has to be proper vetting that happens automatically.
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