The definition of big data analytics

Big data analytics is the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions. With big data analytics, data scientists and others can analyze huge volumes of data that conventional analytics and business intelligence solutions can't touch. Consider that your organization could accumulate (if it hasn't already) billions of rows of data with hundreds of millions of data combinations in multiple data stores and abundant formats. High-performance analytics is necessary to process that much data in order to figure out what's important and what isn't. Enter big data analytics.

Why collect and store terabytes of data if you can't analyze it in full context? Or if you have to wait hours or days to get results? With new advances in computing technology, there's no need to avoid tackling even the most challenging business problems. For simpler and faster processing of only relevant data, you can use high-performance analytics. Using high-performance data mining, predictive analytics, text mining, forecasting and optimization on big data enables you to continuously drive innovation and make the best possible decisions. In addition, organizations are discovering that the unique properties of machine learning are ideally suited to addressing their fast-paced big data needs in new ways.

Most organizations collect data about their interactions with customers, gathering a wealth of information. Those data often have information about customer interests, usage patterns, and other useful information that’s invaluable in making both operational and policy decisions in an organization.

Big data is opening up a world in which millions of disparate data points can be mapped and cross-correlated, illuminating economic patterns and providing business insights previously unavailable. As an example, data gathered in the subcontinent’s groceries and shops has been shown to accurately reflect fluctuations in the currencies. These kinds of correlations and insights are highly valuable to Premise’s customer base of banks, hedge funds, consumer goods manufacturers, retailers, national policy makers, and non-governmental organisations.