Modern analytics for businesses must move beyond a traditional approach of poring over structured data in a back office, demographic profiles, or coming up with a “gut feeling” solution. With the explosion of the Internet, its “Things,” the cloud, and social media, data is proliferating quickly. Getting a handle on Big Data, and pinpointing it for business advantage, is a Herculean task. It’s one that requires scalable, simple-to-use solutions, according to a position paper from Enterprise Management Associates/9sight.
Operational analytics, which provides real-time support and decisions, was cited as the largest project challenge in an EMA survey of 259 Big Data professionals. “For many organizations, the term ‘real time’ has stopped being a marketing buzzword and is now a core business requirement,” report authors stated. “Equally urgent is the need to simplify getting to value.”
Big Data Challenges
That’s a tall and wide order, as analytics must now deal with large data sets, including structured, semi-structured (XML and JSON), and unstructured data. In addition, there are new sources of data, including analytical database platforms/appliances; NoSQL data store platforms; data discovery platforms; the cloud; Hadoop and related technologies.
In such a Big Data environment, the cloud is crucial, as it provides the elasticity to cope with scaling, speed, and data-access issues. Companies also need code-free solutions that can be deployed quickly and be understood by line-of-business professionals, not just data scientists. (See more on this subject from RTInsight’s expert Connie Moore.)
One solution, RapidMiner, was evaluated. EMA argued it met the requirements of a modern analytics platform, which include ability to deal with full data sets while being easy to use; scalability, acceleration of time to value, and Agile development methods.
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