Big Data Analytics Forecast: The Streaming Future

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.

Big Data is the biggest game-changing opportunity for IT industry since the Internet went mainstream almost 20 years ago, particularly because of the unprecedented array of insights into customer needs and behaviors it makes possible. But many of my colleagues who agree that this is true aren’t sure how to make the most of it. Instead, they find themselves faced with overwhelming amounts of data and organizational complexity, rapidly changing customer behaviors, and increased competitive pressures.

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.


Originally published at www.7wdata.be on April 13, 2017.

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