Five Phases of Big Data Projects

First, identify the data and brainstorm a use case. Then make sure everything’s in place to make it work. Enterprises today have a mass of data to analyze—whether from their own database systems, machines equipped with sensors, real-time business transactions, or ecommerce.

Often they will embark on a “Big Data” effort in hopes of achieving a business objective—whether more sales, a better customer experience, reduced fraud, optimized production, or predictive maintenance. “There is increasing pressure on analytics teams to reduce time to insight and answer questions faster,” according to a recent TDWI paper on “Best Practices for a Successful Big Data Journey.” “Yet big data has introduced many new forms of complexity.”

Author: Chris Raphael


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