Big Data Analytics – Why Is It Important?
"With high-performance analytics, we're already seeing customers change their thought processes, change their businesses and change their approach to the data."
Chief Technology Officer
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 of data requires that you not only understand big data to decipher the information that counts, but also – more importantly – the possibilities of what you can do with it using big data analytics.
As your data builds within multiple data stores in abundant formats, you may find your organization has accumulated billions of rows of data with hundreds of millions of data combinations. So the solution to the big data challenge then becomes obvious – big data requires high-performance analytics to process and figure out what's important and what's not. 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, nothing should restrict your desire and ability to approach the most difficult and challenging business problems. For simpler and faster processing of only relevant data, SAS offers our customers high-performance analytics to enable timely and accurate insights using data mining and predictive analytics, text mining, forecasting and optimization on big data to continuously drive innovation and make the best possible decisions.
Why Big Data Analytics?
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.
There are four approaches to analytics, and each falls within the reactive or proactive category:
- 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.
- 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.
- 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.
- Lastly, 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 big data analytics from 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.
"This is an era of visualization, so we should provide ranking officers and board members with eye-catching tables and charts that help them quickly grasp the meaning of the data provided and make informed decisions."
Chief Risk Officer
Big Data Analytics from SAS
Whether you need to analyze millions of SKUs to determine optimal price points, recalculate entire risk portfolios in minutes, identify well-defined segments to pursue customers that matter most or make targeted offers to customers in near-real time, high-performance analytics from SAS forms the backbone of your analytic endeavors. Combined with a breadth of technologies to perform big analytics across the enterprise, large or small, SAS helps you extract meaningful insight from your big data and obtain the real business value.
- SAS® In-Memory Analytics: With SAS In-Memory Analytics solutions, organizations can tackle unsolvable problems using big data and sophisticated analytics in an unfettered and rapid manner.
- SAS® Visual Analytics: SAS Visual Analytics is a high-performance, in-memory solution for exploring massive amounts of data very quickly. It enables you to spot patterns, identify opportunities for further analysis and convey visual results via Web reports, the iPad® or an Android tablet.
- SAS® Social Media Analytics: A solution that integrates, archives, analyzes and enables organizations to act on intelligence gleaned from online conversations on professional and consumer-generated media sites.
- SAS® High-Performance Analytics Server: An in-memory solution that allows you develop analytical models using complete data, not just a subset, to produce more accurate and timely insights. Now you can run frequent modeling iterations and use sophisticated analytics to get answers to questions you never thought of or had time to ask.