Big data analytics: From data scientists to business analysts

The growing popularity of Big Data management tools (Hadoop; MPP, real-time SQL, NoSQL databases; and others) means many more companies can handle large amounts of data. But how do companies analyze and mine their vast amounts of data? The cutting-edge (social) web companies employ teams of data scientists who comb through data using different Hadoop interfaces and use custom analysis and visualization tools. Other companies integrate their MPP databases with familiar Business Intelligence tools. For companies that already have large amounts of data in Hadoop, there's room for even simpler tools that would allow business users to directly interact with Big Data.

A startup aims to expose Big Data to analysts charged with producing most routine reports. Datameer has an interesting workflow model that enables spreadsheet users to quickly perform analytics with data in Hadoop. The Datameer Analytics Solution (DAS) assumes data sits in Hadoop, and from there a business analyst can rapidly load, transform, analyze, and visualize data:

Datameer's workflow uses the familiar spreadsheet interface as a data processing pipeline. Random samples are pulled into worksheets where spreadsheet functions let analysts customize transformations, aggregations, and joins5. Once their analytic models are created, results are computed via Hadoop's distributed processing technology (computations are initiated through a simple GUI). DAS contains over a hundred standard spreadsheet functions, NLP tools (tokenization, ngrams) for unstructured data, and basic charting tools.

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