Few jobs today typify the “knowledge economy” as does the role of a data analytics professional.
At a minimum, she’s got strong math skills, is familiar with a programming language or two and is able to divine the business or marketing value of disparate collections of raw data (with the help, of course, of some powerful software such as SAS or Tableau). And she’s really good at Excel.
But to take that next step, to be the analytics professional who manages a team, she needs something else. In the only half-joking words of Jan Kestle, president of Environics Analytics, she has to be able to “write sentences with subjects and verbs in them.”
In fact, in interviews with over half a dozen companies and individuals, everyone identified communication as the most critical skill a data analytics manager can possess. At telecom giant Rogers, which does both B2B and B2C analytics, VP of customer experience strategy Corby Fine said being a “universal translator” is essential.
“Given the … amount of value a great data analytics manager can bring to the table, it’s incredibly important that they be able to express that fact to the rest of their organization,” he said in an interview. “Often managers fail simply because they are too focused on the data and analytics, and not on ensuring their peers know how to take advantage of them.”
It’s about being able to translate between the technical and the business. “ In analytics whether it’s advanced or basic, not a lot of senior leaders get even simple correlation analysis,” points out Tom Peters, partner in the financial advisory practice at Deloitte Analytics. “So part of the challenge is to make them understand what that might mean or how they’re going to make a decision next. So the manager is really playing both roles—interpreting the technical and translating it into the business. “
That can mean slightly different things at different places but in particular it means knowing your industry. At Toronto-based Kingsdale Shareholder Services, for example, the focus is on securities and finance. In December 2014, the company launched In-Sight Voting Analytics, a system for delivering and analyzing intelligence on how shareholders of public companies are likely to vote their proxies. Victor Li, VP, governance advisory and proxy analytics, says his staff has to possess deep knowledge about Glass Lewis and ISS (Institutional Shareholder Services), the leading resources for corporate governance intelligence. In this way they can not only use their raw technical skills to analyze data, but their knowledge in the vertical is essential for understanding how to identify and correct data that may be incorrect, or how to apply rules and policies on a case-by-case basis in a way machines simply can’t do.
Jason Smith, president and CEO at Real Matters, seconds that. He puts his criteria for hiring managers into two buckets, one for an ability to mine insights on raw data and the other for an ability to solve business problems by transforming that data. The latter means knowing your customers and what he calls their “pain points.” Real Matters’ customers include banks, insurance companies and real estate agents.
“So the data analytics managers in my world are product managers,” Smith says. “They need to be able to have deep insights in terms of the vertical or industry that we’re solutioning. … For us it could be an insurance company that’s interested about gaining insights about residential property. So I would have someone who’s an insurance expert working with our insurance customers trying to understand what their pain points are and how might we leverage data to solve a problem.”
On the prowl for talent
Apparently, it’s not easy to find such people.
The talent sources are different for each company depending on its needs. Kingsdale’s Li says his company’s space is new and niche enough that he tends to get his talent straight out of school and then trains them himself. Smith at Real Matters says his staff typically has deep knowledge of their given vertical and often come from other service providers such as Equifax, MPAC, Teranet or the banks.
But industry associations and schools provide some leads as well. “I just found one out of school with a 3.9714 GPA–masters in science and advanced computational mathematics and absolutely brilliant—and so I like to take a little bit of experience and marry it within a team dynamic with a little bit of the raw young mind, too,” Smith says.
Staffing companies are active in the space too, and also offer some insight into what it takes to get past the first gatekeeper before a sit-down with the hiring company proper. Andy Robling, VP sales at Hays, says the focus is on competency- and behavioural-based interviewing. A candidate might be asked how he helped develop a report on how to reduce a bottleneck such as an inventory process, or for a specific example of how their work helped make a material change in the business such as an increase in sales or reduction in costs.
Interestingly, he says that while a degree in something like computer science may be desirable, it’s not as essential as certifications. That’s because the latter demonstrate the candidate is not only up to date technically, but also that there’s a commitment to ongoing skills development.
And yet Robling brings up a recurring theme when he says that ultimately “the main skill-set that is going to make the distinction between an also ran and a very good candidate is their business skills and acumen.”
One analytics professional, who asked not to be named as he isn’t authorized to speak on behalf of the company, works for a large Canadian telecom, and says that while a background in math is indispensable, success isn’t just about the analytics itself. “I had done the progression of being IT focused to development focused, which is understanding code. Then after that I got into market research and then analytics. So all that helped in having that broad knowledge of data. It makes you more of a well-rounded analyst versus just understanding one single focus.”
The education system hasn’t quite caught up to this need for the well-rounded individual—instead tending to keep the math nerds in one silo and the business-savvy but innumerate in another—but there are concrete signs that’s changing.
Environics’ Jan Kestle says she’s met with several major business schools and university math departments over the last six months and they’re all serious about integrating skills so that students come out of the gate ready for management-type roles in data analytics. Says Kestle, “I’m very excited about multi-disciplinary initiatives in the universities to ensure that whatever their specialty is they have a little of the balancing exposure through their programs.”
Photo via Apaxo.de
Latest posts by Samson Okalow (see all)
- The Fintech Philosopher: Tabitha Creighton at InvestNextDoor - August 15, 2016
- The Realities of Big Data for B2B: Optimism and declarations of failure - April 14, 2016
- The Realities of Big Data for B2B: Very high to very negative returns - April 12, 2016