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At the end of this inefficient and multistep process were analyses. You got historical analyses (i.e., information after the fact rather than in real time). These reports usually arrived too late for the business to change or influence the outcome of the activity it depicted. Thus, business analysts, department heads, and C-suite leaders typically received reports with delayed, overly simplistic, and vague information. Sometimes the information was irrelevant when it finally made its way to business analysts or the C-suite because the company had changed direction or other factors emerged in the meantime. Even so, dashboards and reports made in this way rarely changed. Things proceeded as they always had: the same questions asked, the same data queried, the same reports and dashboards generated—day after day and week after week.

By contrast, today’s self-service BI apps let business analysts bypass the middlemen and unstop many of the IT bottlenecks. This self-service software also enables the use of data outside the company as well as from within, such as social media, the cloud, public data sets, and IoT data. Some self-service BI apps can use real-time data, but many are limited to near-time data (frequent refreshes). However, near-time data usually isn’t a business limitation. There are actually only a few use cases where real-time data analysis warrants the extra effort and expense. After all, near-time refreshes can be as frequent as every minute or less.