First posted on the Harvard Data Smart Cities here
I’ve just told another partner organization “Don’t silo your geeks!” It’s about the tenth time this year that I’ve conveyed this message.
The way most organizations utilize research, mapping and data is the same whether the analysis comes from an internal group or a contracted partner. You ask the data folks to do some analysis, with a well formed plan to give them, then get the results and go do the thinking work to implement a new plan or improve an existing effort. So what’s wrong with this model? Everything. This model not only serves to perpetuate a gross misunderstanding, it also serves to devalue your own staff and to rob your organization of valuable insights.
When you take your broken car to a mechanic for repairs you tell them the symptoms and then leave them to do their thing. All good. Unfortunately with data analysis you’re not just following a formula model like: issue+data+geek=result. By handing over a specification or formed plan for analysts to follow, you’re missing the fact that the analysts know an enormous amount about what is possible, best practices for indicators, methods and communication styles, and about how to frame a research project to ensure your goals are met. Data geeks actually happen to know a lot about your work, your issues, and how to effectively think through a problem. We’ve long treated data folks as simple number crunchers who know magic tricks that we leave alone to do their thing. That’s a serious misunderstanding.
Involve your data partners in your thinking, strategy, and planning and you ensure a higher chance of success for your project.
This approach sends a message to your data team or consultant that you really only consider them useful for doing the geekery, that they cannot possibly understand your problem or the end application of the data. When organizations maintain the stigma of data analysts being simply geeks who like tech, you ensure those very talented individuals will never truly reach their potential in your organization. Given the average analyst possesses traits including problem solving abilities, critical thinking skills and rare creativity, do you really think we’re using them best by siloing them away and perpetuating the geek stereotype?
More importantly, you ensure that your analysis is never as good as it should be by isolating the data folks from your initial thinking process, from your planning and brainstorming phase and your research formation efforts.
Would you take your car to the mechanic with a detailed procedure to follow? Not likely, you’d consult with them and develop a plan that includes their detailed knowledge and your broader mission (namely keeping your car reliable). Then they execute, you receive the results. By engaging with your data folks in the early phases of a project you add valuable perceptions and insights, you allow for perspectives on what can be done, what would be problematic and how best to frame the plan. You gain from having the folks who will execute your plan helping to form it, ensuring that your ask is reasonable and that your ideas can be executed upon. A weak plan is nearly impossible for some research group to turn into a useful end product. Involve your data partners in your thinking, strategy, and planning and you ensure a higher chance of success for your project.
Likewise, when you get your research report, data outputs, maps or other results, don’t consider the role of your data geeks to be over. I’ve witnessed so many planning and implementation meetings where the folks in charge butcher the data analysis or misinterpret the maps, leading the effort down a bad path with less chance of the desired impacts. Take the data geeks out of this stage and your chances of making similar mistakes are seriously amplified. Keep your data partner engaged in this crucial last stage. Allow them to help form the end result, expect that you will raise up further data questions that will require more work to go back and answer.
A final benefit in keeping your data team involved at all stages is that you’re building the capacity and skills of your data folks, giving them insights to better guide their phase of the work, strengthening your team, and allowing for more diverse, experienced voices in your efforts. That’s rarely a bad thing.