Building Data Leaders: Pitfalls and Possibilities
We’re living in a moment when data is having a significant societal impact that will shape nearly every job role. But as a tide that lifts all boats, data will also require social and political skills to get the most value from it in organizations.
Post-pandemic, it’s clear that people understand data a lot more than they ever have. Even if data wasn’t a part of their job description two years ago, looking at charts and graphs on social media or the news became a daily activity and helped build their data literacy. People also became accustomed to following trend curves. They now understand more about how they can use data in their lives and how important it is for them to be able to answer questions outside of their jobs.
Beyond all these possibilities lie pitfalls, however, especially for data leaders who are managing colleagues with incomplete data knowledge. Here are a few strategies I recommend for building data leaders so they can strike the right balance between business knowledge and data fluency.
- Validating data at every level. Data travels so quickly today, often with so little validation or forethought, that a chart posted on a Twitter thread often will be seen as gospel. Sound data can help to identify and mitigate the fake news phenomenon out in the world, but it’s up to data leaders to educate colleagues in the workplace about how to discern the differences between what’s well-founded and what is playing fast and loose with assumptions and facts.
- Balancing data with lived experience. The challenge with having so much data is that you can overuse it in decision-making. Data leaders need to strike the right balance between supporting data-driven decision-making processes and respecting their employees’ experience. If you’re working with an engineer who brings 20 years of experience, someone who can just look at a panel and know what's wrong based on the sound of how it’s operating, there’s probably no need to look at a chart and three weeks’ worth of trends to solve the problem.
Rely on data too much, and you may see a rise in what I call armchair specialists. These people may not really understand the business or the real world, but they’ll go where the data leads them. You can’t create a senior engineer in an armchair (or an Aeron for that matter). They have to bring lived experience and at least have spent some time in their business unit working through issues rather than just saying, “Oh, here’s a dataset I’m going to use to understand the issue.” That becomes a problem because data isn’t always accurate and correct.
- Developing a working knowledge of AI and AR. Data scientist is a buzzword right now and companies are hiring because everyone thinks they need to have one in their arsenal. But if you look at a data analyst today, they spend a lot of their time prepping data and building visualizations. If the tools move into the augmented and conversational analytics space, as I believe they will, where you won’t need somebody to build visualizations, the data scientist agenda needs to change. They will be looking more at how AI and machine learning work to enable predictive and prescriptive analytics. They’re also going to be looking at drones, VR and AR, and other ways of consuming and telling a story with data. You need to become conversant in these areas as well.
- Managing real-time data. When you use real-time data to inform decisions, the risk is that it hasn’t gone through cleaning processes and transformation. This will increase the percentage of erroneous data, which of course elevates the risk of making major decisions based on factually incorrect data. So you need to have the tools at your disposal – both established technologies and new cutting-edge tools – to ensure that you hit as close to accurate as possible in your decisions.
As we recruit and train people that are coming in at a grassroots level, advancing fluency in all these areas should be part of our core skills training. Of course, building data leaders is not like a flick of a switch and suddenly everyone becomes data fluent tomorrow. All this takes time. But if we as the data leaders of today are helping the generation of tomorrow, then as that group moves up in organizations you’ll see an exponential increase in the fluency of people understanding what data is, as well as with partnering with your business colleagues to take them on that journey with them.