In my recent column I lamented the fact that the typical corporate web team is unlikely to have the budget or resources to do measurement well. Even if budget were no object, an article by Gil Press, Forbes contributor, shows how the problem is compounded because data scientists are in such high demand. We’ve all seen the explosion of job titles with ‘digital’ in them; now it’s ‘data’.
Gil defines a data scientist as ‘an engineer who employs the scientific method and applies data-discovery tools to find new insights in data’. Our own super-star data scientist Helen Lindsay is indeed an engineer who uses the excellent tool Tableau to provide our clients with insight.
In the survey on which Gil’s article is based half of the respondents cited turning analytical insights into business actions as one of their top analytics challenges – as my column suggests and as Helen would attest.
Tom Davenport, writing for Deloitte, defines the need for ‘light quant’ as someone who knows something about analytical and data management methods; knows a lot about specific business problems; and can connect the two. An ‘analytical translator’ is someone who is extremely skilled at communicating the results of quantitative analyses.
I’m not sure that Helen would describe the job as ‘sexy’ - as Gil Press attempts to, although with tongue in cheek - since it involves equal measure of painstaking detail (data wrangling) and frustrating generality (people wrangling). But I’m sure she’d agree with Tom that these are indeed the types of skills needed by anyone trying to find the metrics that the board cares about most.
- Dan Drury