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Leaving Data on the Table: Obstacles to Big Data Analytics

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Leaving Data on the Table: Obstacles to Big Data Analytics

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There is frequent conversation about the explosive growth of Big Data in the age of wearables, compulsive social media and ever more capable computers, but when it comes down to gleaning useful insights from data, data scientists face more challenges with variety than with sheer volume. At least according to a survey of data science professionals conducted by Paradigm4.

According to the survey, which was administered to 111 data scientists, 71 percent said Big Data had made their analytics more difficult and data variety, not volume, was to blame.

The trend toward hyper-personalization and precision targeting illustrates this well. Recommendations, search results and ads are becoming ever more relevant and micro-targeted as they tap more and diverse data like social networks, current location, and browsing and purchasing history. Personalized insurance offerings are augmenting sensor data about driver behavior to incorporate contextual data like time-of-day and road congestion. Precision medicine providers are gaining a more refined understanding of what works for whom by integrating molecular data with clinical, behavioral, electronic health records and environmental data. But the ability to use diverse data types poses a serious challenge. (For more on this topic, see, “Big Data at Work: Dispelling the Myths, Uncovering the Opportunities,” by Thomas Davenport, Chapter 1: “Why Big Data is Important to you and your Organization.”)
Other key takeaways from the survey include a general dissatisfaction with Hadoop as a data analytics tool and a move towards complex analytics in Big Data. Check out InsideBigDATA's breakdown here or download the full report for yourself.


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