Manually identifying patterns in data is like looking for a needle in a haystack. Often there is too much data or the data is too complex for a person to sort through. Augmented analytics uses machine learning and artificial intelligence (AI) techniques to automatically identify actionable insights.
This technology also helps data and analytics leaders address the challenge of high-value, time-consuming and manual analysis that may result in biased hypotheses, missed key findings, and incorrect or incomplete conclusions.
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More than 60% of respondents to a Gartner Data and Analytics Summit poll said they believe augmented analytics will have a high or transformational impact on their ability to scale the value of analytics in their organization.
For example, a fast-food restaurant chain did not consider the location of more-profitable fountain drinks relative to bottle drinks. During a store remodel, the restaurant shifted the location of the fountain drinks and the augmented analytics system picked up a 20% increase in sales of fountain drinks and profitability — changing plans for future store design.
Although most business and IT leaders understand the promise of augmented analytics, efforts to incorporate augmented analytics will likely encounter resistance. Part of this distrust stems from the “black box” approach to analytics. Without knowing the specific factors that drove a recommendation, users hesitate to trust the insights. Increased transparency and efforts to increase explainable AI will help shift this attitude.
Look for areas of the business where current data and analytics approaches fail to deliver relevant and timely results, or where analysts spend a lot of time doing root-cause analysis.
By identifying gaps, errors or time-consuming steps in the historical analytic processes, data and analytics leaders can lay the foundation for new and more automated approaches.