Big Data Analytics

Share  
1  |  2  |  3  |  4  |  5  |  6  |  7  |  8
Previous Next

Click through for guidelines from TDWI and Aster Data for implementing big data analytics.

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.

 

Related Topics : Box.net, IT Management Automation, SharePoint, Facebook, T-Mobile

 

More Slideshows

Misc50-290x195 Six Ways File-Sharing Apps Have Failed the Enterprise

Is there a real risk involved with relying on consumer-grade file-sharing and sync solutions in the enterprise, or are CIOs overreacting? ...  More >>

infra29-290x195 The Differences Between Hardware Design and Software Development

Here are a few of the key differences between hardware design and software development. ...  More >>

DataM19-290x195 Five Advantages of On-Premises File Sharing

Here are five reasons business users should consider an on-premises file sharing solution for business documents before abandoning internal servers. ...  More >>

Subscribe to our Newsletters

Sign up now and get the best business technology insights direct to your inbox.