Big Data Analytics

What it is & why it matters

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.

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Why is big data analytics important?

For years SAS customers have evolved their analytics methods from a reactive view into a proactive approach using predictive and prescriptive analytics. Both reactive and proactive approaches are used by organizations, but let's look closely at what is best for your organization and task at hand.

Reactive vs. proactive approaches

There are four approaches to analytics, and each falls within the reactive or proactive category:

  • Reactive – business intelligence. In the reactive category, business intelligence (BI) provides standard business reports, ad hoc reports, OLAP and even alerts and notifications based on analytics. This ad hoc analysis looks at the static past, which has its purpose in a limited number of situations.
  • Reactive – big data BI. When reporting pulls from huge data sets, we can say this is performing big data BI. But decisions based on these two methods are still reactionary.
  • Proactive – big analytics. Making forward-looking, proactive decisions requires proactive big analytics like optimization, predictive modeling, text mining, forecasting and statistical analysis. They allow you to identify trends, spot weaknesses or determine conditions for making decisions about the future. But although it's proactive, big analytics cannot be performed on big data because traditional storage environments and processing times cannot keep up.
  • Proactive – big data analytics. By using big data analytics you can extract only the relevant information from terabytes, petabytes and exabytes, and analyze it to transform your business decisions for the future. Becoming proactive with big data analytics isn't a one-time endeavor; it is more of a culture change – a new way of gaining ground by freeing your analysts and decision makers to meet the future with sound knowledge and insight.

With SAS, you can truly change operations, prevent fraud, gain competitive edge, retain more customers, anticipate disease outbreaks or run unlimited budget simulations – the possibilities are endless.

With high-performance analytics, we're already seeing customers change their thought processes, change their businesses and change their approach to the data.

Keith Collins
Senior Vice President and Chief Information Officer, SAS

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