Big Data Analytics
Every industry has its own particular big data challenges. Banks need to analyze streaming transactions in real time to quickly identify potential fraud. Utility companies need to analyze energy usage data to gain control over demand. Retailers need to understand the social sentiment around their products and markets to develop more effective campaigns and promotions. Analytics solutions help organizations take control of big data and uncover the insights they need to make the best decisions.
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. 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.
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.
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