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        Maximize the Value of Manufacturing Data Sources

        Manufacturing data comes in many shapes and sizes, from big data sources to unstructured documents. To be competitive and operate efficiently, manufacturers must use all the data at their disposal to improve their business processes. One-quarter of manufacturers report that they are working with more than 20 different data sources. These sources include everything from accounting and financial data to internet of things data. Bringing these varied data sources together from internal sources and even across the supply chain requires technology that can access and prepare the data for both operational and analytical processes.

        Our research finds that nearly three-quarters (73%) of manufacturing organizations that use data preparation technologies say they have improved their operations. Data preparation technologies enable line of business personnel to access, combine and prepare data. Manufacturers’ most common data preparation requirements are to join data sources and to make the tasks available for reuse. Organizations report that the most important benefit of data preparation is improved quality of information, which is critical: Nearly half of manufacturers (48%) report spending significant amounts of time preparing internet of things event data for analysis. Also included in the top five benefits are meeting analytic needs more easily and eliminating manual processes.

         
         

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        About the Author

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        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.