September 14, 2011
Big data analytics is where advanced analytic techniques operate on big data sets—one of the most profound trends in business intelligence today. Using advanced analytics, businesses can study big data to understand the current state of the business and track still-evolving aspects such as customer behavior. This empowers users to explore granular details of business operations and customer interactions that seldom find their way into a data warehouse or standard report.
Big data analytics is the intersection of two technical entities that have come together. First, there’s big data for massive amounts of detailed information. Second, there’s advanced analytics, which can include predictive analytics, data mining, statistics, artificial intelligence, natural language processing, and so on. Put them together and you get big data analytics, the hottest new practice in BI.
A new flood of user organizations is commencing or expanding solutions for analytics with big data. To supply the demand, vendors have recently released numerous new products and functions, specifically for advanced forms of analytics (beyond OLAP and reporting) and analytic databases that can manage big data. This research report drills into all the aspects of big data analytics mentioned here to give users and their business sponsors a solid background for big data analytics, including business and technology drivers, successful business use cases, and common technology enablers. The report also uses survey data to project the future of the most common tool types, features, and functions associated with big data analytics, so users can apply this information to planning their own programs and technology stacks for big data analytics.
Big Data Analytics will accelerate your understanding of the many new tools and techniques that have emerged for analytics with big data in recent years. Download the full, 40-page TDWI Best Practices Report to get the complete analysis.
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