Big Data Analytics

Big data is now a reality: The volume, variety and velocity of data coming into your organization continue to reach unprecedented levels. This phenomenal growth means that not only must you understand big data in order to decipher the information that truly counts, but you also must understand the possibilities of big data analytics.

What is 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.