Big Data Analytics is a subset of computer science that studies the large amounts of data produced by complex systems. Big Data Analytics covers many areas of computer science, including information systems, engineering, statistics, business, decision sciences, etc. The field has recently been popularized by financial institutions, health care, consumer industries, and organizations. Big Data Analytics has been used for several years to analyze large amounts of data sets.
Big Data Analytics is a subset of computer science that studies the large amounts of data produced by complex systems, rather than the user information itself. It is essentially an application and a tool, such as a laboratory experiment albeit one with breathtaking flexibility. Big data analytics involves unstructured data and partially structured information (such as the contents of social media users’ comments) and semi-Structured data (such as user flows within websites) as well. These large amounts of information, when analyzed using advanced algorithms can yield timely insights into user behaviors and interests. This enables companies to better serve their customers and create greater business intelligence.
Many of today’s enterprise resource planning (ERP) systems are based on big data analytics tools. The biggest of these, Hadoop, is designed to scale up to large data sets via a server cluster, enabling managers to rapidly monitor and analyze large amounts of data in near real-time. IBM’s wide-ranging WebSphere initiative, based on Hadoop, is also an important initiative aiming to provide data analysis tools that are fast, reliable, and scalable. MapReduce, an open-source software project based on Hadoop, is another tool commonly used in the Hadoop ecosystem.
When applied to activities related to customer insight, big data analytics allows managers to provide fast answers to questions related to customer needs. Such activities include queries about product functionality, demographics, demand, channel mix, and more. By combining traditional analytics with big data technologies, managers can provide their customers with answers in just a few minutes. In fact, some analysts report that they can answer hundreds of questions in a day with the help of big data technologies.
Companies also can benefit from big data analytics because of the speed at which the data can be analyzed and measured. Traditional machine learning methods typically take days or weeks to generate a meaningful result. The Big Data stacks – consisting of several different analytics tools – can quickly measure and provide results in just a few minutes. For example, with the assistance of Google’s Big Data Analyzer tool, an analyst can quickly measure the response to a marketing campaign’s social media push, the conversion rate of a PPC ad, and the number of leads that resulted from the campaign.
Companies using big data analytics will gain a number of advantages. Customers will be able to quickly understand company offerings. Analysis of market trends can reveal new opportunities. Machine learning analytics can also be used to simply improve the management of existing customer insights.