Big Data Analytics - Slide 4

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Companies need to create a scalable architecture that supports big data analytics from the outset and utilizes existing skills and infrastructure where possible. To do this, many companies are implementing new, specialized analytical platforms designed to accelerate query performance when running complex functions against large volumes of data. Compared to traditional query processing systems, they are easier to install and manage, offering a better total cost of ownership and sometimes a cost as little as $10,000 per terabyte.

These systems come in a variety of flavors and sizes. There are data warehousing appliances, which are purpose-built, hardware-software solutions; massively parallel processing (MPP) databases running on commodity servers; columnar databases; and distributed file systems running MapReduce and other non-SQL types of data processing languages. Sometimes companies employ multiple types to address processing requirements. For instance, comScore, an online market research firm, uses Hadoop to acquire and transform Web log files and Aster Data’s nCluster database for analysis.

There are two major trends causing organizations to rethink the way they approach doing analytics.

Big data. First, data volumes are exploding. More than a decade ago, Wayne Eckerson, director of TDWI Research, participated in the formation of the Data Warehouse Terabyte Club, which highlighted the few leading-edge organizations whose data warehouses had reached or exceeded a terabyte in size. Today, the notion of a terabyte club seems quaint, as many organizations have blasted through that threshold. In fact, he contends, it is now time to start a petabyte club, since a handful of companies, including Internet businesses, banks, and telecommunications companies, have publicly announced that their data warehouses will soon exceed a petabyte of data.

Deep analytics. Second, organizations want to perform “deep analytics” on these massive data warehouses. Deep analytics ranges from statistics — such as moving averages, correlations, and regressions — to complex functions such as graph analysis, market basket analysis, and tokenization. In addition, many organizations are embracing predictive analytics by using advanced machine learning algorithms, such as neural networks and decision trees, to anticipate behavior and events. Whereas in the past, organizations may have applied these types of analytics to a subset of data, today they want to analyze every transaction. The reason: profits.

For Internet companies, the goal is to gain insight into how people use their websites so they can enhance visitor experiences and provide advertisers with more granular targeted advertising. Telecommunications companies want to mine millions of call detail records to better predict customer churn and profitability. Retailers want to analyze detailed transactions to better understand customer shopping patterns, forecast demand, optimize merchandising, and increase the lift of their promotions.

In all cases, there is an opportunity to cut costs, increase revenues, and gain a competitive advantage. Few industries today are immune to the siren song of analyzing big data.

This slideshow features a basic set of guidelines from TDWI and Aster Data for implementing big data analytics.

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