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October 29, 2012

Analytics Driven Business Processes

By Matt Sarrel

Big data doesn’t exist in a void. Collect all the data you want and without analytics capacity it won’t have much value. A good analytics program requires a combination of software, developers, and the skills and knowledge to use them. In a recent survey conducted by IBM, more than 75 percent of respondents reported using core analytics capabilities such as query, reporting, and data mining. More than sixty-seven percent reported using predictive modeling.

Predictive modeling is where this all gets interesting. Chances are that your business already holds the data you need to understand and optimize business processes – you just need the right analytics component to leverage it.
Many businesses benefit from the ability to incorporate real-time streaming data and analysis into business processes and decision making. In order to use high volumes of streaming data from transactions, securities market feeds, smart grid, and social networks, a business must be able to capture, process, and analyze at great speed. New database technologies (NoSQL or New SQL) are very well suited to executing these tasks rapidly because they were, in general, built for speed and not consistency. A practical application of a time-sensitive business process that could be improved in this way is fraud detection.

Advanced data visualization techniques make real time analysis possible. Data sets are typically too large to scan visually. In the IBM survey, seventy one percent of respondents of active big data efforts rely on data visualization skills. Much of this effort is devoted to tracking and improving existing business processes. It’s important to carefully define the objectives of any analytics program, especially when business process optimization is involved. A clear goal, such as “decrease shipping costs by 2%”, is a necessity so that you can demonstrate your project’s success.

For example, Automercados Plaza’s, a family-owned chain of grocery stores in Venezuela, built a big data analytics solution that integrated over six terabytes of product and customer data in order to give management better inventory management and the ability to more quickly adjust to changing market conditions. They’ve realized almost a thirty percent increase in revenue and a $7 million increase in annual profitability.

For more insight on how companies are using analytics to shape their futures, join IBM on November 7, 2012 at 1:00 p.m. ET. For a webinar: Analytics: The real-world use of big data / How innovative enterprises extract value from uncertain data.

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