The Benefits of Big Data Hindered by Traditional Analytic Approaches

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There are three alternatives companies use today to perform analytics: 1) traditional statistical modeling of data relationships; 2) rewriting applications and rebuilding from the ground up; and 3) capturing transaction in context at the time of execution. The first two are time-consuming and cost-prohibitive (only the largest enterprises can afford these).

OpTier, a leader in application performance management (APM) and providing end-to-end contextual transaction data, analyzing billions of transactions per day, recently announced key findings from its newly released Big Data analytics research study. The research shows that amid all the hype, few companies are able to apply analytics to Big Data for competitive advantage, because of the time and costs associated with traditional approaches to analytics.

To better understand how companies are currently analyzing Big Data, what they are lacking, and how they can better take advantage of Big Data, Jacques Takou Tuh, an MBA student at the Kellogg School of Management and Adam Kanouse, CIO of OpTier, conducted primary research with business executives at Global 250 companies. Based on one-on-one interviews and focus groups spanning industries including financial services, health care, media and entertainment, retail and telecommunications, these findings are summarized in a research paper entitled "Making Big Data Analytics Fast and Easy."

 

Related Topics : Business Intelligence Vendors, Data Quality, Data Warehousing, Key Performance Indicators, Knowledge Management

 
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