Predictive Analytics 101: Operationalizing Big Data
A Crash Course on Operationalizing Predictive Analytics with Real-Time Data
You’re sold on the potential of big data. But how do you make it work for your business? This brief provides you with a crash course on predictive analytics: why it matters, how businesses can operationalize it, the impact on IT, and how Intel can help.
<br>Why It Matters
Big data derives most of its value from the insights it produces when analyzed—finding patterns, deriving meaning, making decisions, and ultimately responding to the world with intelligence. As big data technology continues to evolve, businesses are turning to predictive analytics to help them deepen engagement with customers, optimize processes, and reduce operational costs. The combination of real-time data streams and predictive analytics—sometimes referred to as processing that never stops—has the potential to deliver significant competitive advantage for business.
Predictive Analytics: Looking Ahead
Enterprises have long used business intelligence (BI) for competitive advantage, applying analytics to structured data (for example, transactions and customer information) stored in relational database management systems (RDBMSs). Analytics for big data is different. Big data is characterized by huge data sets and varied data types, both semistructured and unstructured (videos, images, audio, clickstreams, weblogs, text, and e-mail). Plus, big data is generated at a faster rate than most enterprises have had to handle before.
The massive scale and growth of data in general—and semistructured and unstructured data in particular—outstrip the capabilities of traditional storage and analytic solutions, which also do not cope well with the heterogeneity of big data. Organizations may be data rich, but new analytic processes and technologies are needed to unlock the potential of big data.
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