storm trooper teaching a class with a bell curve example
PHOTO: Daniel Cheung

Some tasks sound like work. Some tasks sound like work even to the people who enjoy them. And then there's improving organizational data literacy.

Before diving in, what do I mean be data literacy? For the purpose of this article, I’m talking about the understanding and effective use of data by non-data scientists, quants, techno-literati. If you want to (mostly) painlessly improve the ability to effectively use data at all levels of your organization, this article is for you.

The good news is improving data literacy does not have to be on par with a root canal. Here are some simple steps you can take that through prolonged repetition will lead to a data aware organization.

Start Small

Really small. As in one person or one team small. Do a fantastic job educating them. Make it your mission to help that individual use data to improve their daily work life, decision making, and ability to impress their manager. You get the idea. Create a champion for your cause. 

As this person excels, more folks will want what you’re selling.

Related Article: Stop Looking for Unicorns: Building Your Big Data Team

Think Even Smaller

Don't throw the kitchen sink at this person on day one! Yes, everything will be better when this person understands averages, standard deviations, the importance of sample size calculations, data quality, A/B testing and all things SQL. Focus on one of these. Accept that not all topics will be relevant. 

When starting out, be flexible about the order you work through them. While some concepts build on earlier material, this is not always the case. An energizing business case will make the concept easier to grok and retain.

Don’t Be a College Professor

This will make you an irritating bore. Allow me to pause and apologize to all the folks who suffered through this with me. Lecturing will put your audience to sleep rather than improve their data literacy.  

Improving data literacy is about helping folks make better business decisions. The more closely you can tie a concept to a specific business need, the faster literacy will improve.

Tell Stories

Stories engage us at a deep level, significantly increasing retention.  

For example, when working on critical thinking around key performance indicators, I enjoy telling the story of how England football was ruined for decades by focusing on most goals coming from short passing sequences with no thought given to how frequently short pass sequences occur. Though well intentioned, leveraging a faulty KPI, one documented and “proven” by the data, demonstrably hurt England's performance on the international stage.  

Telling this story helps folks understand why they need to spend time thinking deeply and critically about the KPIs they use, as well as the implausibility of stand-alone, 'comes up with the answer by itself' statistical packages.

Related Article: Why Data Insights Remain Elusive

Set the Example. No Cheating

As Mark Twain popularized: “There are lies, damned lies, and statistics.”  

When working with team members be a paragon of consistency and transparency. No switching data sets, switching variables or switching calculation methods from one discussion to another to match your point of view. Help team members become aware of when this happens. Doing so increases the trust everyone places in you, the data and the team.

Drive Fear From the Workplace

This is great general advice from W. Edwards Deming, one of the pioneers of using data in the workplace. Eradicating fear is particularly critical in the sphere of data literacy because so much success comes from asking questions.

For many years I worked in an organization with a rigorous interview process. We were fortunate and constantly received applications from some of the smartest and most educated data folks in the world. Part of the interview involved reading a line graph that 80 percent of candidates failed to interpret correctly. What I learned from this is that reading graphs is hard! Much harder than anyone realizes, because we have all been reading them since we four years old.  How hard can it be?  Just a picture?  Without freedom from fear everyone will be too embarrassed to ask the obvious question.

Everyone in your organization needs to be so free from fear they are comfortable asking a presenter to explain the business meaning of a line graph.

Final Thoughts

Increasing data literacy is a marathon, not a sprint.  Take your time, start small, continue pushing, and before you know it, you will have the data organization you desire.