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    The Benefits of Big Data Hindered by Traditional Analytic Approaches

    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.”

    The Benefits of Big Data Hindered by Traditional Analytic Approaches - slide 1

    Click through for results from a Big Data survey, conducted by OpTier.

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    Despite all the hype about social media, the majority of Big Data resides within the four walls of a company’s data center.

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    Companies are not gaining a business advantage from their Big Data because it is distributed across many silos and lacks the context and uniformity necessary to allow analysts to quickly leverage it.

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    A huge bottleneck to analytics is the data preparation phase, which accounts for 30 to 60 percent of the time spent in analytics, because data is saved without context.

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    The companies surveyed that have a Big Data analytics solution in place spent two to three years setting up their data warehouses, and spend a minimum of two to three months each time a new data set is incorporated.

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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).

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    Companies surveyed agree that context would dramatically accelerate analytics because they would be able to reduce the time and cost spent on analytics by 50 to 90 percent.

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    Companies reported that the ability to understand the value and/or cost of servicing each individual customer would be extremely valuable.

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