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big data analytics

Big data analytics refers to the process of collecting, organizing and analyzing large sets of data ("big data") to discover patterns and other useful information. Not only will big data analytics help you to understand the information contained within the data, but it will also help identify the data that is most important to the business and future business decisions. Big data analysts basically want the knowledge that comes from analyzing the data.

The Benefits of Big Data Analytics

Enterprises are increasingly looking to find actionable insights into their data. Many big data projects originate from the need to answer specific business questions. With the right big data analytics platforms in place, an enterprise can boost sales, increase efficiency, and improve operations, customer service and risk management.

Webopedia parent company, QuinStreet, surveyed 540 enterprise decision-makers involved in big data purchases to learn which business areas companies plan to use Big Data analytics to improve operations. About half of all respondents said they were applying big data analytics to improve customer retention, help with product development and gain a competitive advantage.

Notably, the business area getting the most attention relates to increasing efficiencies and optimizing operations. Specifically, 62 percent of respondents said that they use big data analytics to improve speed and reduce complexity.


Webopedia Big Data Resources
Webopedia Download: 2014 Big Data Outlook
What is Big Data?
What is Structured data?
Unstructured data Defined
How Much Data is Out There?
Big Data Analytics Expert Predictions


The Challenges of Big Data Analytics

For most organizations, big data analysis is a challenge. Consider the sheer volume of data and the many different formats of the data (both structured and unstructured data) collected across the entire organization and the many different ways different types of data can be combined, contrasted and analyzed to find patterns and other useful information. 

The first challenge is in breaking down data silos to access all data an organization stores in different places and often in different systems. A second big data challenge is in creating platforms that can pull in unstructured data as easily as structured data. This massive volume of data is typically so large that it's difficult to process using traditional database and software methods.

Big Data Requires High-Performance Analytics

To analyze such a large volume of data, big data analytics is typically performed using specialized software tools and applications for predictive analytics, data mining, text mining, forecasting and data optimization. Collectively these processes are separate but highly integrated functions of high-performance analytics. Using big data tools and software enables an organization to process extremely large volumes of data that a business has collected to determine which data is relevant and can be analyzed to drive better business decisions in the future.

Examples of How Big Data Analytics is Used Today

As technology to break down data silos and analyze data improves, business can be transformed in all sorts of ways. According to Datamation, today's advances in analyzing Big Data allow researchers to decode human DNA in minutes, predict where terrorists plan to attack, determine which gene is mostly likely to be responsible for certain diseases and, of course, which ads you are most likely to respond to on Facebook. The business cases for leveraging Big Data are compelling. For instance, Netflix mined its subscriber data to put the essential ingredients together for its recent hit House of Cards, and subscriber data also prompted the company to bring Arrested Development back from the dead.

Another example comes from one of the biggest mobile carriers in the world. France's Orange launched its Data for Development project by releasing subscriber data for customers in the Ivory Coast. The 2.5 billion records, which were made anonymous, included details on calls and text messages exchanged between 5 million users. Researchers accessed the data and sent Orange proposals for how the data could serve as the foundation for development projects to improve public health and safety. Proposed projects included one that showed how to improve public safety by tracking cell phone data to map where people went after emergencies; another showed how to use cellular data for disease containment. (source)







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