Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits.
The primary goal of big data analytics is to help companies
make more informed business decisions by enabling data scientists, predictive
modelers and other analytics professionals to analyze large volumes of
transaction data, as well as other forms of data that may be untapped by
conventional business intelligence (BI) programs. That could include Web server
logs and Internet clickstream data, social media content and social network
activity reports, text from customer emails and survey responses, mobile-phone
call detail records and machine data captured by sensors connected to the
Internet of Things.
Semi-structured and unstructured data may not fit well in
traditional data warehouses based on relational databases. Furthermore, data
warehouses may not be able to handle the processing demands posed by sets of
big data that need to be updated frequently or even continually -- for example,
real-time data on the performance of mobile applications or of oil and gas
pipelines. As a result, many organizations looking to collect, process and
analyze big data have turned to a newer class of technologies that includes
Hadoop and related tools such as YARN, MapReduce, Spark, Hive and Pig as well
as NoSQL databases. Those technologies form the core of an open source software
framework that supports the processing of large and diverse data sets across
clustered systems.
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