Empowered by Data: How to Build Inspired Analytics Communities by Eva Murray
If you’ve followed my work for any amount of time, you’ll have heard me muse on the data academy — an organization’s institutionalized approach to helping everyone level up their data skills.
The data academy is certainly among us — I can’t go a week on LinkedIn without noticing another organization start a data literacy program — but it’s relatively early days. That’s why it was so exciting to come across a whole book on the topic of building an analytics community of practice.
Eva Murray’s Empowered by Data gave me a lot to think about how best to build a data academy. In particular, it got me thinking: what can a data academy take from a data schoolhouse?
The #MakeoverMonday one-room schoolhouse
We’re so used to large, structured, grade-based education that it’s hard for us to imagine how a one-room schoolhouse with students of all ages learning together would work. As it turns out, this older model had a lot going for it: rather than keeping “experts” siloed from “beginners,” everyone worked together to grow at their own pace.
Much of Empowered by Data has to do with #MakeoverMonday, an online community which Eva has co-run for many years giving individuals the opportunity to redo existing visualizations each week. Like a one-room schoolhouse, individuals of all skill levels share, teach, and learn about data analytics and data visualization through this program.
As Eva states, “…#MakeoverMonday hosts a diverse community of people and sees a constant influx of new members who are supported by existing, more experienced participants. These invisible and very real support structures bring a dynamic of reliability and transparency where everyone can find their place and become part of the larger group.”
This is such a cool opportunity, and it’s amazing to read about all the outlets Eva and team have offered through this program (and sobering to learn about all the ups and downs that happen while setting up something of this scale).
#MakeoverMonday may be a cool online community for everyone, but let’s get real: transparency and shared growth is not the way analytics education is treated within most organizations, as we can see in the following case study.
The first rule of being good at something should be sharing it
A couple years back, the Wall Street Journal ran an article titled “The First Rule of Microsoft Excel—Don’t Tell Anyone You’re Good at It.” It never sat well with me: my advice as a blogger is to share your knowledge freely.
This article, perhaps unconsciously, exposes the worldview that most organizations and employees hold about knowledge: there’s a fixed amount of it for which a salary is paid to put it directly into practice. The idea of sharing knowledge is unfair to employees (who get taken advantage of by request-happy coworkers, as detailed in article) and unproductive for employers (who are paying for employers to bring their skills to work with them, not to grow them there).
As Eva puts it in the book: “As knowledge workers, we are often tempted to define our skills in terms of technical expertise, measurable output, intellect, and our ability to quickly gain new knowledge and understand new concepts.” There are some real problems with this worldview, which a data academy needs to address — and can do so by borrowing from the field of knowledge management.
Knowledge management has entered the academy
So, how do skills enter an organization? Or, more importantly, how are they sustained and improved? I think we know what happens already when data talent isn’t given permission or even discouraged to share what they know: they leave for lack of growth opportunities, and then there’s a scramble to decipher what exactly it was they were up to, and how they did it.
It’s better to see skill-building as a fluid, social process which needs to be cultivated and reproduced. And the field of knowledge management (KM) exists to figure out how to do this. In fact, the idea of a community of practice (referred to frequently in Empowered by Data) comes from KM and has found itself recycled into data literacy programs.
A community of practice exists to help people share, grow and codify their knowledge about some topic inside an organization. They’re designed to be informal and grassroots: think lunch meetups, Slack channels, and so forth. A community of practice makes it safe to share that you’re good at Microsoft Excel. It gives you scalable ways to impart that knowledge which are seen as a cultural good in the organization.
After all, learning has always been a social process — even highly technical learning like data analytics. “To be most effective in their work, analysts and businesspeople working with data will need to be trained, must have their skills enhanced… and must be able to collaborate with others,” Eva reminds us. “This is where community comes in and become sthe support structure for your data culture.”
This idea of the community of practice alone is such a powerful insight into how to grow data capacities of an organization. To make it even better, Eva provides actionable steps and ideas for actually building an analytics community of practice, rather than just discussing the benefits of it. Few technical learning groups are as long-lasting as #MakeoverMonday, so you’ll learn tons from her experience in the field.
If you build it… you should read this first
While more is being written about data education, I find much of it so far tends to be either too specific (written for technicians looking to nerd out rather than set a strategy) or too general (written at a super-high level for executives assuming someone else will handle the granular tactics).
This book provides concrete steps on how to build an analytics community. Not only that, it does a brilliant job understanding the why. I finally understand why that WSJ Excel article still makes me cringe, and why the attitudes purveyed there are ultimately a sign of poor knowledge management in an organization.
As I mentioned earlier, much of the work on how to grow and sustain analytics competencies in an organization borrows heavily from KM — knowingly or not. Because of this, I suggest you also read Working Knowledge: How Organizations Manage What They Know by Thomas H. Davenport and Laurence Prusak for a primer on the field.
The upshot: every organization needs a data academy, and every data academy needs to be part data schoolhouse: a community of practice for people of all skill levels to learn and teach at their own pace.
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