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        Solve the Right Problem by Tying Analytics to Operational Processes

        For decades analytics tools have been focused on solving the wrong problem. They’ve been designed to make it easier for business analysts to do ad hoc analyses on behalf of others, instead of enabling business users to perform their own analysis. There will always be a place for ad hoc analyses, but organizations can better meet their day-to-day analytic needs with more directed, purpose-built analytical applications that are embedded into operational processes. Line-of-business personnel want to get their jobs done as quickly and effectively as possible, so organizations should seek to empower them. These individuals shouldn’t have to become analytics experts or application programmers.

        Analyses that are pre-built and embedded into business processes provide many benefits. Decisions can be made much more quickly, and they are made more quickly for several reasons. Obviously, if the analyses are pre-built, they are immediately available. But perhaps more importantly, pre-built analyses help avoid the Achilles heel of analytics: data preparation. Data preparation slows down analytic processes dramatically. Our research shows that more than two-thirds of organizations (69%) identify it as the most time-consuming part of the analytics process. If the data is prepared as part of the pre-built analytical applications, it saves the line-of-business functions from performing this time-consuming task.

         
         

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        About the Author

        Dave_2016_Circle_Zoomed

        David Menninger

        SVP and Research Director
        Ventana Research

        David Menninger is responsible for the overall direction of research on data and analytics technologies at Ventana Research. He covers major areas including artificial learning and machine learning, big data, business intelligence, collaboration, data science and information management along with the additional specific research categories including blockchain, data governance, data lakes, data preparation, embedded analytics, natural language processing (NLP) and IoT.