Analytics is a rapidly evolving field of high business value, so your organization needs a robust up-skilling strategy. I call this type of institutionalized data education platform a “data academy.” To learn more, check out my series of posts on the topic.
Enrollment in the academy is a good time to take stock of candidate skills: after all, the nature of the education depends on the current skill levels of its students. So, what are some ways to assess the data skills of your candidates in relation to what your organization needs?
Quantifying data skills
This is data education, right? So of course we would have some quantitative measures of candidate skills! While you’ll see that there are other ways to assess skills, this is one is easy to implement and can signal what immediate payoffs might result from your academy.
Self-rating
Here you ask students to self-rate their abilities on a Likert scale, something liks this:
Rate your Excel skills on a scale of 1 to 5.
This type of assessment may seem pretty ridiculously simple. But there is actually a lot of interesting math you can do with this data, as you will learn if you check out my DataCamp course on survey development in R.
In that course, you will learn that it while it’s pretty easy to collect this type of data, it takes effort to build them in a way that tells us much.
Rating your Excel skills on a scale of 1 to 5 could mean lots of different things to different people. To some, a “5” means nesting an IF() statement, while to others it means building an animated dashboard.
On top of that, some may under- or over-estimate their ratings to be in line with what they perceive to be more acceptable to their employer, co-workers or test proctor: so-called ‘social-desirability bias.’
While self-ratings of this nature have a place in skills assessments, let’s not end there.
Time estimates
Not to be too crass, but time is money, right?
In the face of rapid automation and workforce displacement, both employers and employees ought to think about where poor data workflows are leading to lost productivity.
To get a sense of how bad the problem is, a question like this may be in order:
Approximately how many hours a week do you spend cleaning and preparing data for analysis and reporting?
Don’t be alarmed by a big number in itself: data professionals typically spend the majority of their time cleaning data.
However, if you get the sense that your analysts no longer, well, analyze data, and instead just manually clean and prepare it, then it’s time to examine what needs to change in the data workflow.
Looking at the data skills in terms of productivity can make a great benchmark when it comes time to measure the ROI of the data academy.
Learning assessments
Rather than a self-rating of skills, how about asking specific technical questions like the following?
Which tab on the home ribbon allows you to create a PivotTable?
A. Data
B. Insert
C. View
D. Home
It seems like this would be the most objective assessment strategy, right? There’s no hiding data talents this way.
However, as you probably know from your own work, problems don’t come to data analysts in the form of multiple choice questions. Perhaps more important than raw knowledge is the ability to apply it in a given context.
Maybe you don’t want to be as specific, but would like a broader sense of candidate know-how. A question like this can work well as a skills assessment:
Which of these best describe how easily you could perform a left outer join in Power Query?
A. I wouldn’t know where to start
B. I could struggle through with trial and error and lots of web searching
C. I could do it quickly wiht little to no use of external help
Questions like this can give a more contextual look at candidate skills, without digging into hard-to-generalize specifics.
Qualifying data skills
Given these limitations of quantifying candidate data skills, perhaps we should consider — yes, really — qualifying the skills.
It sounds hokey, but being a data analyst is as much about mindset and attitude as it is raw technical ability. So we might want to try getting inside the minds of our candidates and understand their perspectives on data.
Experiences
Really take the time to understand the pain points of how your candidates work with data — this should be about more than just pinching pennies on productivity.
Understand: what are the pain points that people have in working with their data? What do they wish they could do, if they knew how? What’s holding them back in the current environment?
To learn about particular experiences, you could ask something like this:
What systems do you pull your data from and to what extent do they interface?
Your candidates may be able to articulate what works and doesn’t but doesn’t have the know-how or the clout to do anything about it. This is what you need to figure out in the enrollment process.
Emotions
Now this really seems nuts for a cold, exacting field like data analytics. But working with data can be scary!
You’re working with tons of data in difficult-to-control processes with no technical support. You may try to fix something yourself, but feel like an idiot for not knowing it already. And without buy-in from the organization as a whole, these efforts don’t amount to much.
Your data people may be constantly afraid of making mistakes and feel a total loss of control over their work. Not a great environment, right? It’s one you can change with a solid data up-skilling initiative, of which the data academy is a plank.
What excites you with working with data? What frustrates you?
Goals
For all the data academy can do to improve your organization, don’t discount the intrinsic motivation of your candidates!
A study from the learning & development consultancy Towards Maturity found that employees rated learning for personal development only behind wanting to do their job better and faster as top motivators.
So, try to learn what motivates your candidates coming into the program: do they hope to advance their statistical analysis abilities? Learn to code? Automate particular reports? Or is it out of pure curiousity? You could learn this with as simple a question as:
Why do you want to enroll?
Where to go from here?
A solid skills assessment is crucial to a successful data academy. But what do you do after the assessment?
Please check out the rest of my posts on the data academy to learn more. You’re also welcome to contact me directly or set up a free consult call.
Take the time to assess your candidate data skills and motivations and you’ll be surprised at the talent you’ve got in house. The data academy will cultivate that talent.
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