The reason is that big data represents a huge opportunity for companies because it likely includes important information about customer behavior, security risks, potential system failures, and more, according to CIO. But just having all that information isn’t enough. The challenge for most companies is hiring the right people to figure out how to mine all that data.
Data scientists can help the business by detecting hidden patterns in unstructured data like customer behavior or market cycles and opening up new opportunities based on that information, according to CIO. A data scientist can also use deep data trends to improve a company’s website for better customer retention. And a skilled data scientist can help IT by finding potential storage cluster failures early or tracking down security threats through forensic analysis, according to the article.
“There’s now an intellectual consensus in business that the only way to run an enterprise is to use analytics with data scientists to find opportunities, Norman Nie, CEO of Revolution Analytics, told CIO.
Because of the immense opportunity for strategic insight buried in all that data, companies now have an unlimited demand for people with backgrounds in quantitative analysis, he said.
One tool in the data scientist’s tool box is the R programming language, according to CIO. Other tools include business analytics software from well-known providers like SAS Institute, IBM’s new InfoSphere platform and the analytics technology EMC recently acquired when it bought Greenplum and Isilon Systems, according to the article.
In another CIO article, reporter Meridith Levinson talked with Brian Hopkins, a principal analyst with Forrester Research, who said he expects the demand for data scientists to grow as companies seek to use the huge amounts of data they collect to beat out their competitors and as those companies realize data mining and BI tools alone just aren’t cutting it.
Hopkins told CIO that companies need “this specialized class of data scientist to create and run [statistical] models against data and present the results in ways people can act on.”
Steve Hillion, vice president of analytics with data storage company EMC, agreed that companies are realizing that they can’t just rely on software to make sense of their data—they need data scientists with very specialized skills and they have to give them the right technology to get the job done.
Hillion told CIO that companies need to provide robust, scalable hardware and software so data scientists can perform their analyses. He added that the technology is relatively cheap enough so that many companies can afford to purchase it.
Data scientists are most in demand in large organizations that gather a lot of data from customers like online companies, advertising companies, as well as cell phone and retail companies that track sales or marketing data, according to Hillion.
But that’s doesn’t mean the tools have failed, it just means that BI tools aren’t able to give companies all the information they need to make the best business decisions, Hillion told CIO. According to Hillion, companies need to hire data scientists to analyze the data and ask the predictive or interpretive questions.
“Business intelligence has succeeded in the sense that it’s made people hungrier for more information,” he told CIO. “The more they know about how their business is doing, the more they want to know why and how they can improve it.”
Finally, a successful data scientist has to be skilled in the areas of statistics and modeling and mathematics, and he needs to have a firm understanding of the business or domain in which you’re working, according to Hillion.
And, he said, a good data scientist has to listen to what business users, executives and management are saying then “tease out that one insight that will resonate the most with the business, have the greatest impact on the business, and is the thing that the business can actually act on.”
Good advice for any “data geek”.
Spotfire Blogging Team