Real-time analytics is considered both an operational must-have and a strategic competitive advantage. With such increasing priority, the much-coveted data scientist needs access to a platform that supports data mining and complex analytics to scale; is agile in supporting evolving data types; can ingest massive volumes of new data sets quickly or recover just as quickly should the data load fail; and can present a prototyping environment to test models without breaking the bank. This last requirement is so crucial because, while budgets are growing modestly, IT is still required to do more with less. Once these models have been tested, they must be operationalized so that the business can benefit on a day to day basis. Shifting to a more real-time operational business model means analytics platforms with more advanced data management features as they become systems of record.
Source: ESG
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