Welcome back to The Dog People Dig Data. Last time, we talked about the self-service data culture we have at Rover, where business units fetch and interpret their own data independently. In this installment, we explore some of the techniques we use to maintain and improve our Organizational Data Literacy even while we grow rapidly.

Make yourself available for others

First, we invite people to join us at weekly “Data Brunch” and “Data Happy Hour” events. These are collaborative co-working environments, where data engineers, data scientists, and marketplace analysts are on hand to help people with anything at all in the domain of data. In addition to these events, we also moderate an active data-focused Slack channel, where users can get help or a code review.

Hone your teaching skills

Secondly, we practice pedagogy consciously and deliberately in interactions with our colleagues. Pedagogy is the method and practice of teaching. Part art, part science, teaching ability isn’t some innate trait that you either have or don’t. Rather, it’s a skill that can be acquired by study and practice, just as a technical skill would be learned. Learning and teaching are going on constantly in the workplace, and every interaction can benefit from decreasing the associated friction. If you’ve never brought your pedagogical toolkit into conscious consideration, here’s an easy opportunity to reflect and improve.

Quick tips for teaching success

As a former math teacher, I’ve had a lot of practice with pedagogical troubleshooting. If something doesn’t work, try a different approach! Nothing can replace study and practice, but these tips make a great foundation.

  • Create Instructional Scaffolding
    • For example, start by having someone modify an existing query or dashboard. Show them the power they can get just by understanding how to fiddle with a “where” clause.
  • Increase your wait time, the amount of silence you allow after asking a question.
  • Engage in frequent Checks for Understanding
    • This is what I see missing most often from the average tech-industry workshop
    • Don’t assume that someone “got it”
    • Ask a student to demonstrate that they have the skill
    • This requires interactive engagement
  • Encourage Participation
    • Entice people with goodies and rewards for participating!
    • Take the time to get to know someone.
  • Be Hands On, limit the “Lecture” component of whatever you are doing
    • Less talking, more listening.
    • Be a “guide on the side” instead of a “sage on the stage.”
    • This can be one of the hardest things to do, but it’s essential to the development of truly self-directed learners.
  • People are different—be flexible
    • Try explaining it a different way.
    • Have them explain it back, have someone else explain it.
    • Draw a picture, have them draw a picture.
  • Visualize your knowledge bases as overlapping circles, and think about what’s in the intersection, and what the other person knows that you don’t.
  • Keep your cool, don’t get frustrated
    • It can sometimes be difficult to get an idea from your head into someone else’s.
    • If you need to take a break, take a break.

Create a safe learning environment

The most important part of teaching isn’t the resources or materials that you use—it’s your ability to create a safe learning environment where people aren’t afraid to try new things, and aren’t afraid to fail. A feeling of psychological safety is necessary for our culture since stress can prevent learning and memory. If someone is afraid they’re going to “look dumb in front of the class,” their ability to learn is dead on arrival. It is our job to make sure that our colleagues feel safe.

Most people have a perfectly decent aptitude for learning basic analytics, but many have suffered traumatic experiences in their schooling, causing them to give up preemptively. This situation takes care to unwind, and the necessary relationships don’t happen overnight, but seeing the changes in someone’s confidence and watching their abilities blossom is so worth the effort. If dedication is there, everyone can increase their level, regardless of the starting state.

This is how we provide opportunities, resources, and mentoring to encourage and develop a wide range of data users at Rover. Next time, we’ll discuss how we face challenges common to organizations that democratize their data.

Sarah Johnson is a data engineer at Rover. Prior to joining Rover, she was a high school math teacher, actuary and business intelligence analyst at a leading pet insurance company. 📈📇🐶🤓✨