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Customer Transaction Info Is Leading Big Data Element

Sales, marketing departments are most interested in Big Data

Financial services and healthcare companies in the US are among the leaders in Big Data adoption—with financial services particularly ahead in terms of usage—and their data strategies are indicators of trends across the verticals. A July 2013 survey from data and analytics consulting company NewVantage Partners found that US executives from these industries reported that on average, 68% expected to spend more than $1 million on Big Data initiatives this year, while 19% said they would spend more than $10 million. And by 2016, half of execs expected to spend more than $10 million.

The study found that the sales and marketing departments were driving investments in Big Data, cited by 77% of respondents. Risk management, fraud and security was another set of business functions well served by execs investing in data, cited by 68%. In third place was a three-way tie between new product development and innovation, research and discovery, and IT and operational management, each cited by 64% of respondents.

Even the business function seen by the fewest respondents as driving Big Data investment—logistics—was still cited by 40% of execs. These responses suggest that while there may be areas where Big Data adds the most value, it is seen as useful across business functions, as companies move to a more information-driven, analytical approach to their work.

The data domains that were of the most interest to execs again spanned the gamut, with customer transaction data as No. 1, cited by 82% of respondents. Other top data sets included financial data (75%), market data (68%) and social media and behavioral data, at 65% each.

Companies also have choices as to how to organize and invest in the various data sets they are examining. Accelerating analytical processes, as well as making the analytics more sophisticated, were on the radar for 70% of respondents each, followed immediately—at 69%—by more effectively integrating existing sources of data.

The emphasis on better analysis and integration speaks to the challenge of Big Data—everyone knows there is more to be done with it, and everyone wants to do it better.


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