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Here are some of the 30 big data takeaways from the conference at Temple University and a recent ZDNet TechLines roundtable discussion.
1. Where do you start a big data project? Skunk works projects were a popular route and then those groups evolved to become dozens of employees and petabytes of data. Other options included the underserved business unit. Some companies had business leaders as sponsors.
... 3. Use cases for big data abound. Among the possibilities:
- Network optimization.
- Fraud detection.
- Seeing what the customer experiences.
- Healthcare simulations.
- Consumer focused marketing efforts require more social networking analysis and predictive capabilities. Consumer data is inherently unstructured.
- Marketing support and tracking of attrition rates in a subscriber-based business.
9. Storage will be an ongoing big data issue because data scientists are pack rats---even hoarders---but there's a budget limit. T-Mobile can only keep 10 days of its clickstream data, said Twiford, who noted the company is trying to process more information in flight. Storage limitations will result in sampling.
12. Big data talent is tough to find. One company appointed internal people with business knowledge and supplement with a partner who had statistic and analytics wonks available (consultants). The long-term talent strategy for this company is to recruit heavily from universities to build an analytic employee pool. Talent has to be able to use data.
13. Visualization tools and crowdsourcing may alleviate the big data talent crunch, said Skytland. Perhaps "citizen scientists" will bridge the gap, said Skytland. Visualization tools can bring big data to the masses.
24. Big data isn't new, but now has reached critical mass as people digitize their lives. "People are walking sensors," said Skytland.
See the video of the panel and read the full list at
www.zdnet.com/30-big-data-project-takeaways-7000005499/
(ZDnet, by Larry Dignan, October 10, 2012).