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Changing Data Patterns Are Impacting Analytics

Collectively, we are amid a transition from data at rest to data in motion. Large volumes of data are streaming into organizations from all sides and with increasing frequency, forcing organizations to rethink how they process data. And the changing nature of the frequency with which data informs decisions requires a corresponding change in analytic approaches. The batch processing and ad hoc analyses of the past are no longer enough for this new data environment. Organizations that collect this information without analyzing it more quickly put themselves at a competitive disadvantage.

Our research shows that one third of organizations (34%) need to process the data they collect every hour or in real time. Gone are the days when organizations could get by with weekly or monthly processing of the information they collect. There are certainly still monthly, quarterly and annual reporting processes, but these processes must be combined with more frequent processing of operational data as well. Whether it’s data from web sites, point of sale registers, call center interactions, products or location information, organizations need the ability to react in real time if they want to affect outcomes while the window of opportunity still exists.

 
 

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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.