Next Generation Business Data Analytics

Next Generation Business Data Analytics

21st century businesses demand timely access to varied data sources, analysis of exponentially growing data volumes, and rapid reaction to/prediction of changing events. Common challenges such as meeting increased performance needs, handling complex analytic queries, mitigating risk, and solving specific business problems in a continually diminishing time window require a transition from the days of relational database management systems that have been in vogue for the last 30 years to analytic engines that can process huge data volumes rapidly.

This current business environment has given rise to a new class of products that enable high performing analytics. Analytic databases are gaining in popularity and acceptance as they successfully meet the high-volume analytic needs of business for a wide variety of solutions at a favorable price/performance point.

Business analysts, Web analysts, marketing managers, and other users employ analytics for various business purposes. For example, financial analytics reveal trends and deliver what-if scenarios that affect the planning, budgeting and forecasting processes. Risk managers need to review large quantities of information to find outliers that may negatively impact outcomes. Customer relationship management and marketing analytics employ data mining and predictive methodologies to address customer propensities to buy or churn and create measures of lifetime customer value. Web analytics are used to understand customer behaviors and optimize product and service offerings, marketing and sales campaigns. Still other analytic applications have been applied in myriad ways to the examination of large amounts of data.

This eBook will examine next generation technology that empowers data-driven businesses. Analytic database engines enable organizations to tackle the most complex analytic challenges and gain deep insights into vast volumes of data. Data-driven organizations in a wide range of industries such as financial services, retail, health care, and government are taking advantage of analytic databases to tackle critical, time-sensitive questions outside the scope of conventional data warehouses and existing analytic tools.

New analytic database technology has evolved from the static warehouse and traditional business intelligence tools to include columnar-based, massively parallel processing, accelerating data insight at the speed of now.

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