There is no question, in my mind, that data analytics has a role to play in helping governments respond to complex challenges. However, the current approach to data analytics in many govt agencies appears to have two significant shortcomings (a thread)

The first is that governments tend to rely on quantitative data to feed analytics tools which, by definition, are flattened and simplified to fit into predefined categories. To make sense of complex challenges, we need to capture messier, less uniform and less objective data too.

People’s stories feel like a good place to start. The Sensemaker tool is supporting govts to do this.

The second flaw is that the insights generated tend to be used to create a single point solution (eg let’s give these houses smoke alarms).

Instead, the insights should be treated as *signals*, which help to identify, in a more targeted way, where a challenge is most acute, and which parts of a community it’s impacting most profoundly.

Once that is identified, governments should be pursuing systemic approaches to engaging with the complex issues surrounding the challenge at hand, rather than silver bullet solutions.

Of course, the overarching issues around data biases also still hold true, and create very real risks for all analytics initiatives. There’s also, without question, lots I’ve missed here. Would welcome other views!