Big data isn’t a new phenomenon and isn’t going anywhere either. Companies are investing heavily in analytics programs and using data to make big business decisions. Therefore, more and more employers are looking to hire people with strong data analytics skills, but with all of the unique job titles (e.g. data scientist, analytics manager, digital marketing analyst, data guru), it can be difficult to spot people who excel at the specific skills you’re looking for.
Here are seven interview techniques to help unveil the data analytics gurus:
The best way to truly tell whether or not someone is good at data analytics is to present them with a case study during the interview. Provide the candidate with a data set and ask them to share their insights. What analysis can they derive based on the raw data? What trends or areas of concern do they spot and what improvements do they suggest based on the findings? Have them explain the steps they take and why. It shows their thought process and methodology of looking at data. It is all a great way to prove that the candidate can talk the talk AND walk the walk.
Ask the candidate to bring examples of raw data and dashboards from previous positions and explain how their findings impacted business decisions. Can they articulate how the data effects the business? What was implemented or changed after they presented their findings? ? Hindsight being 20/20, is there anything they would have done differently after these changes were made?
There are many different resources available for data analysis. Every day there is a new software available on the market and there are numerous agencies who will analyze data for you. It’s important for employers to clarify the resources available to the candidate and the extent to which they have used them. If the candidate has been accustomed to unlimited resources, then they might not be very good at the “hands-on and scrappy” data analysis.
If they do use external resources like Marketo, Nielsen or any other external vendor use their account rep as a reference. Verify how much the candidate does on their own analysis versus the agency.
If you’d rather not give them a case study on-the-spot in the interview, assign them a project (similar to the case study) to take home. If they are unemployed, give them one day to complete it but if they are employed up to three days is a good amount of time. Keep guidelines general and see what they produce. It’s interesting to see where some candidates take it verses the others. You will also learn if they are good at meeting deadlines.
Whether they’re running through the project during the interview or in an email recap if assigned, look to see how intuitive they are. Can they see beyond just the data and understand how the data connects with business? Great data analytics people aren’t black and white thinkers. They understand that it’s okay to have ambiguity and that there are different ways to read into things.
Companies want data analytics professionals who can communicate their findings effectively to non-analytical professionals. Many data analytics professionals understand the data but have difficultly explaining it in “layman’s terms.” The best way to screen for this skill is to have the candidate share details of recent projects when they had to deliver their findings to non-technical professionals. If they don’t have prior experience in presenting, ask a non-technical colleague to sit in on the interview and have the candidate explain the findings from a recent data project.