5 Lessons from Data Analytics Ninjas: Page 2
Develop Data Analytics Skills
Even with self-service data analytics tools that purport to have intuitive interfaces, it's a good idea to train users. Taking online courses from sites such as Coursera and edX and leveraging the resources of partners are good ways of developing data skills, Barclay said.
IT Business Edge's Loraine Lawson shared some advice from two McKinsey directors who specialize in data analytics and recommend building data teams that include five key roles: data hygienists, who address data quality and cleaning issues; data explorers, who separate data you need from the data you don’t; business solutions architects, who put the discovered data together and organize it for analysis; data scientists, who develop sophisticated analytics models; and campaign experts, who turn data analytics models into results.
Get Good at Identifying Good Data
Mobeen Khan, executive director of Product Marketing Management at AT&T, in an Enterprise Apps Today article on how the Internet of Things will impact data analytics, discussed the need to determine what type of data is important, what should be transmitted immediately, what should be stored and for how long, and what information should be discarded. Without doing this, companies could end up with an almost infinite pile of data to analyze, when only a relatively small portion is of real importance.
"Some data just needs to be read and thrown away," Khan said.
Though he was addressing data generated by sensors, the same principle applies to all data. Separating the data wheat from the chaff is so important that the McKinsey directors suggest creating that "data explorer" role just to do so.
Ann All is the editor of Enterprise Apps Today and eSecurity Planet. She has covered business and technology for more than a decade, writing about everything from business intelligence to virtualization.