Data visualisation consultancy and training organisation The Information Lab has trained thousands of people to use Alteryx and Tableau over the past five years. In June 2015, it set up a dedicated training centre, The Data School, which is currently teaching its third cohort of eight students to use advanced data analytics and interactive data visualisation software.
According to head coach, Andy Kriebel, the student body is close to gender parity with women making up 45%. He said this stems from the application process, which doesn’t include a list of requirements that could intimidate prospective candidates. Those who want to join The Data School simply have to download Tableau Public, create a data visualisation, and send in a “nice” email with a link to the visualisation. Successful students will then have to make it through two interviews – one over the phone and one in person.
"Women won't apply unless they have all the qualifications. If you don't list them, you remove that barrier."
“A lot of the research I've read says that men will typically still apply even if they don't have all of the qualifications, whereas women won't apply unless they have all of them. If you don't list the qualifications, then you remove that barrier in the first place,” he said. According to Kriebel, this then leaves it totally up to the individual whether they want to apply or not, and they have a self-selected group of enthusiastic learners.
Kriebel also said that he is able to make a much more objective decision on a person’s application if he has no knowledge of their educational or career background - CVs are so insignificant to the process that if someone does send one, he won’t open it. He said that one advantage of being an American working in the UK is that he only knows of two British universities, so there is almost no chance of being swayed by a person’s alma mater.
For Kriebel, a person’s body of work is more important than what or where they have studied. He sees this with one of his children who spends all his time coding. “My 15-year-old has probably got 100 different applications he’s made. Well, that’s his CV right there. He can show that he can do it,” he said.
Kriebel remembered working for a tech firm in Silicon Valley where candidates for engineering roles had to meet a long list of requirements, but he sees this as a less effective way of recruiting. Although Kriebel hopes that other data and analytics companies will adopt a similar approach to hiring to his own student selection method, he thinks they may be afraid of change. He said: “Everybody tells us it makes sense, even big companies. They’d say: ‘We should do it, but we’ve always done it this way’.”
A bigger fear in Kriebel’s eyes is having a homogenous workforce or student body. “What kind of new ideas are you going to get if everybody is the same? What kind of diversity is that going to give you as a company? It’s probably all going to be white males because they are in math degrees or whatever it might be. I don’t think that’s good for anybody,” he said.
He is looking to work with and train people who can create amazing visualisations, but also survive in the training room and collaborate with seven other students, and then go out and work with clients. “Some of those things are intangible,” he said.
“The diversity has just evolved, I guess. We don’t intentionally do it.”
Kriebel is also making sure that his interview panel is also diverse and so he is drawing on the alumni of The Data School. He said: “I can now bring in more women to help with the interviews, so it is not just men. That helps to make people more comfortable and helps bring in different opinions.”
This approach of removing the barriers so that as many people from as many backgrounds as possible feel comfortable and confident in applying to The Data School means that creating a more diverse environment hasn’t been a chore. “The diversity has just evolved, I guess. We don’t intentionally do it.”