Big Data Analytics refers to the examination of huge data sets consisting of a variety of data generally called “big data”. The purpose of the examination is to uncover hidden patterns, correlations, customer preferences, market trends, amongst other valuable business information. Big data analytical findings are targeted at new revenue opportunities, more productive marketing, exceptional customer service, better operational efficiency and competitive advantages, etc.
Why is big data analytics important?
The major goal of big data analytics is helping organizations make more informed business decisions by enabling predictive modelers, data scientists and other analytics professionals for analyzing large volume of different forms of data that may have been left untouched by conventional business intelligence (BI) programs. This can include Internet click stream data and web server logs, social media content and social network activity reports, survey responses and text from customer emails, phone call detail records and machine data captured by the sensors connected to the Internet of Things.
Which form of data analytics is best for you?
For many years, customers have evolved newer analytics methods from a reactive view into a proactive approach with the help of predictive and prescriptive analytics. While reactive as well as proactive approaches are used by companies, looking closely one can find out what is best for their organization based on the task type.
There are four approaches to big data analytics:
Reactive – This type of analysis examines the static past and thus has purpose in a limited number of situations.
Reactive -When reporting is pulled from big sets of data it is known as performing big data BI. However, decisions based on these two methods are reactionary.
Proactive – Usually forward-looking, proactive decisions require proactive big analytics such as optimization, text mining, predictive modelling, forecasting and statistical analytics. These help in identifying trends and spot weaknesses to make the right future decisions. Although proactive, analytics can’t be performed on big data as the traditional storage environments can’t be kept up.
Proactive – Big data analytics can be used to extract the relevant information from petabytes and terabytes to analyze and transform your business decisions accordingly.
Being proactive with something like big data analytics is not at all a one-time endeavor. It is like a culture change that leads to a new way of gaining ground, freeing your analysts and decision makers at the same time meeting success with in-depth knowledge and insight.