Big Data Analytics

Big Data is a buzzword used to describe a massive volume of both structured and unstructured data that is so large that it’s difficult to process using traditional database and software techniques. In most enterprise scenarios the data is too big, it moves too fast or it exceeds current processing capacity.

According to leading technology research firm Gartner Inc., big data is high-volume, high-velocity and high-variety of information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. This term is known as the 3 “Vs” (volume, velocity and variety), other analysts add a fourth “V” from data Value.

The term big data analytics, when used by software vendors, refers to the technology (which includes tools and processes) that an organization requires to handling data at extreme scale affordable.

Sources

  1. Wikipedia
  2. Webopedia
  3. Techopedia
“Some of the advantages of the BIRT Analytics product are its fast in-memory engine, its ability to handle large amounts of data, and the more advanced analytic capabilities in the system.” “Actuate Makes Big Play with BIRT Analytics” Tony Cosentino, Ventana Research, January 2013

An Example of Big Data Analytics

When dealing with large datasets, organizations face difficulties in being able to integrate, manipulate, and manage big data. Big data is particularly a problem in business analytics because standard tools and procedures are not designed to search and analyze massive datasets. Many attempts to use standard BI tools fail.

An international telecom company, which provides a complete voice, data and internet-based services portfolio to its more than 6 million customers, is using an innovative big data analytics technology, BIRT Analytics, to manage more than 1 Tb of data, 14 DBs and160 tables, daily. Current analytical CRM eases integrated analysis of data for accurate knowledge of each customer. Campaign micro-segmentation, customer loyalty, individually targeted offerings, contact optimization, customer matrix and churn analysis are now easy to perform.


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Engineering Field
Frequency Representation: You can summarize a field by categories and also see the percentage of the total value that each category accounts for within the field..
"BIRT Analytics enables our customers to find hidden information in their data. All they need to do is combine their own business smarts with BIRT Analytics's innovative advanced and predictive analytics techniques. BIRT Analytics gives business users the power to perform predictive analyses without complex mathematical models." Fred Veldhuis, Managing Director, CPM Factory
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