This is the question that Eva Murray’s latest book “Empowered by Data” aims to answer. And it certainly does that. Eva’s book is the latest in my list of books to read and review throughout 2021, but in many ways it’s an “odd one out”. It’s probably least relevant to most of my ambitions in this blog, where I usually pose questions around visualisation, design and unorthodox ideas. But it’s probably most relevant of all to my “day job” – as an analyst leader in a BI team I’m passionate about building analytics communities and it’s something I’ve been fortunate to be involved with in my previous and current employer.
So for me it’s a book to confirm what I’m doing right (or at least right-ish), to remind me what I could be doing but am not, and a book to advise what to look out for in the future. It featured many friends, acquaintances and even colleagues, and not least Eva herself who I have looked up to and admired for her drive, commitment, example to others and success. Now she is in a position where she can justifiably say that where internal analytics communities are concerned, she wrote the book on it! For the rest of us, there are many practical examples to follow – it just so happens that in my case many of those examples have closely mirrored my own career.
Building internal analytics communities can be hard! As an aside, there is a specific Tableau User Group for such Community Drivers (shortened to CDTUG) which meets virtually each quarter to discuss different elements of building analytics communities – I’m proud to co-lead this group with Amy Ryder and Charlotte Nickells which can be accessed here https://usergroups.tableau.com/communitydrivers – almost all of the topics we’ve discussed over the last year (Centres of Enablement/Excellence, training, community and more) are covered in this book.
All this leads me to make full disclosure – I know Eva well and the work of two of my current closest colleagues (and friends) form the basis of a full chapter. But that’s not really relevant, it’s the subject matter and content of the book in its entirety that made me want to buy, read, and review it.
The first section focuses on defining a community (an analytics community in particular) and giving examples, before moving on to emphasising their benefits in subsequent chapters (whether for the individuals concerned or for organisations as a whole). The main community used as an example is the online #MakeoverMonday community, drawing from the author’s personal experience in heading up the initiative for the last several years. It might be that #MakeoverMonday is similar to the kind of community you are trying to build, or it might be that your own very specific example of an analytics community you are involved with is somewhat different, but that’s not to say the lessons aren’t relevant and transferrable. The personal stories throughout, where Eva has interviewed community members and featured their responses in full, act as an endorsement of #MakeoverMonday specifically and analytics communities in general, with the personal accounts adding to the authenticity of the endorsements. And I would add my own, too, for what it’s worth.
The key chapter for me in the main section is the chapter referring to the risks of building an analytics community. Covering the topics of distraction, echo chambers, lack of buy-in and disruptors, it’s reassuring to know that many obstacles you might face along the way aren’t unique to you. For example, the advice given is to to build diversity and introduce external contributors to your community in an effort to keep things fresh and avoid “groupthink”. And as for lack of management buy-in, a problem that very much resonated with me (if my employers are reading this, I’m absolutely not talking about my current role!) the answer isn’t clear. But, importantly, Eva offers detailed templates as a solution in the very next chapter. When faced with top-down resistance, the best answer is to build from the bottom up, and do so in such a well-structured and way that forces the hand of those in positions of influence. Or at least shows success stories that make it hard for your community-building exploits to be ignored.
So – no diagrams or data visualisations in this post, just an endorsement from me of a particularly useful primer to advise anyone in a similar role building analytics communities. I now work with this book literally at arm’s length from me in my bookshelf, in the knowledge that it is full of advice and examples in my professional field. The book itself ends its final chapter (entitled “where to go from here”) with a section entitled “Ask for help”. You could do a lot worse than coming to this book when you need to help – it will offer you just that.
Questions in Dataviz 