Over the past several years, technology has rapidly changed what enterprise analytics can do. Analytical approaches that incorporate predictive models have begun to displace merely descriptive approaches. Descriptive analytics, which continue to be valuable for many users, have evolved as well, making greater use of visual analytics and moving toward a self-service model in which nontechnical users can often develop their own analyses. In general, analytics are quickly becoming both easier to use and more powerful.
AI Can Help Companies Tap New Sources of Data for Analytics
Automation makes it possible to leverage contextual information that’s otherwise inaccessible.
March 19, 2021
Summary.
Improvements in technology have dramatically changed what enterprise analytics can do, but predictive and descriptive analytics still require time, expertise, and heaps of data, and often produce only narrow insights. However, AI is making it possible for analytics to automatically incorporate and process important context from a broad array of sources — many of which would have previously required analysts to navigate silos and poorly maintained catalogues. Now, much as Google applications can tell you based on your home address, calendar entries, and map information that it’s time to leave for the airport if you want to catch your flight, companies can increasingly take advantage of contextual information in their enterprise systems.
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Excel in a world that's being continually transformed by technology.