Enterprise leaders in big data programs are beginning to differentiate themselves from competitors and slow adopters by matching advanced analytic plans with a blend of specialists and business demands, according to a presentation Thursday led by research firm Aberdeen Group.
The presentation, entitled “Uncovering Customer Insight in Big Data,” featured Aberdeen enterprise data management Research Analyst Nathaniel Rowe, with a few examples from Tom Starek, marketing intelligence services director at security systems vendor Diebold Inc., and Phani Nagarjuna, founder and CEO of customer analytics provider Nuevora.
Aberdeen indicates that companies that have taken on big data initiatives have a 4 percent greater rate of customer growth year-over-year than their counterparts who have not taken on these advanced analytic capabilities. Rowe was quick to follow up that big data programs can’t be directly linked to profitability, only that these successfully companies were the ones investing in and expanding big data plans. However, this “hand-in-hand” business growth and big data adoption does reveal insight into what enterprises are going after with big data programs and what separates successful and struggling initiatives.
According to Aberdeen research where enterprises provided multiple answers, the top sources for their big data initiatives were transactional, structured data (95 percent), social and customer data (85 percent), clickstream (83 percent) and internal, unstructured data (82 percent). Rowe says these adoption percentages reflect a maturity scale of sorts, and he expects enterprises with a solid grasp on their transactional structured data to be able to branch out into other data streams more quickly and aptly.
Surveying enterprises engaged in big data intiatives, Aberdeen split a group of 99 respondents into “leaders” and “followers” on ROI. Of those leaders, 63 percent had a data analyst or data scientist on staff to search out value in data, compared with 42 percent of followers. Rowe says that “this role really wasn’t a thing five years ago” but this job title is becoming mainstream along with big data initiatives. –– However, such initiatives will only truly succeed as data scientists are able to tie information streams into business needs and decision-maker plans. In addition, 54 percent of leaders had assessed enterprise department needs with big data ahead of time, compared with 27 percent of followers who had done the same. Twenty-eight percent of big data leaders had an understanding of their data sources, compared with only 9 percent of followers, according to the Aberdeen survey.
At the lowest adoption rate for leaders and followers was data retirement practices and tools. Only about one-fifth of all enterprises surveyed had included this as part of their big data plans, which Rowe said may stifle agility and storage plans moving into the near future.
“When you think about business data growing by 40 percent year over year ... and you’re not addressing when you can delete, retire and destroy that data, you’re keeping a lot of data that may not have any more value for your organization,” he says, later adding, “Nobody really cares what someone was tweeting about six months ago unless you are in a very specialized field.”
Starek gave an outline of Diebold’s big data project to understand banking customer machine preferences and fill out possible “blind spots” in their strategy and business information. With customer analytics solution-based research from Nuevora, Diebold found that only 3 percent of consumers were focused on one way to access their banking information. Weeding through large sets of Diebold’s customer interaction data, the company was surprised to see that smartphone access to banking was its “biggest growth opportunity,” says Starek.
This story originally appeared at Information Management.
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