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Actionable insights are contextually or statistically relevant conclusions derived from data that provide understanding into the future, and inform what action or response should be taken. It is a result of processing and analyzing data (quantitative, qualitative, structured, unstructured), whether through manual analysis or by using a business intelligence (BI) and analytics solution.
In the context of BI and data analytics, actionable insights reveal the crucial ‘why’ behind the ‘what’. Data itself, whether it’s on a dashboard or a report, can communicate and reveal what occurred, but it’s actual analysis performed by a person, which surfaces why something occurred and should be acted on (to improve performance, etc), that defines an the insight as ‘actionable’.
Actionable insights are so sought after because everyone, from line-of-business users to executives to leaders, need accurate information from their data to improve performance, make necessary changes and replace any decisions made on gut feeling with hard, reliable facts.
But what if users don’t have all the expertise or context required to find answers in the first place? This is where augmented analytics greatly helps, by automating and streamlining sophisticated BI tools to explain to users what the information is, how it’s relevant, and why it should be actioned, without having to rely solely on analyst assistance or manual analysis.