You want data insights at a glance, but it’s hard to digest and process a large volume of data, and you’re creating and collecting more data all of the time. You’re suffering from DRIP: you’re data rich but information poor. To get information, you turn to dashboards, but, if you don’t design them correctly, you can become dashboard rich but information poor, what I’ll call DRIP 2.0.
In this post, I’ll talk through 3 steps, with guides, for designing dashboards to generate insights for business decisions while all forms of DRIP.
How do you choose your next step? You’d like to do more of what’s working, but sometimes you’re not sure what parts of what you’re doing are working, or whether that pivot you’re considering might resonate with your target market, so you’re not sure what you should do more of or where to focus your efforts. In this post, I demonstrate three ways to use surveys to quickly get the data you need to make an informed next step.
When we talk about Diversity, Equity and Inclusion (DEI) efforts, we frequently turn to the data to see how we’re doing relative to our own internal goals, and, as possible, how our efforts and relative success rates compare to those in our local communities and others in our industries. That is, we typically talk and think in terms of benchmarks and progress towards a target percentage. But we don’t just need to wrangle and analyze the data. We also need to communicate the findings of the analysis so that we can figure out what to do next, and this is where choosing the right visualization comes in. In this post, I discuss how different visualization choices enable different understandings of the data, and different conversations and decisions around the data.
When it comes to your decisions and your data, one tool I like to use is called a Decision Log.
By recording your important or ambiguous decisions in this format – the game changers, the ones where your decision-makers disagree, the ones where it’s not so clear what the best decision is or what the influencing factors might be – you provide for yourself a means of auditing, reflecting on, and redirecting yourself towards your goals by getting really clear on how you’re using data, both in the moment of decision-making and as part of post-process analysis.
This can be particularly useful for times when the result of your decision was unexpected, you want to repeat results, or you want to better understand the context in which you’re working.
Has it ever occurred to you that you could be headed to a bizarre and macabre death caused by getting tangled in your bedsheets, and that controlling our collective cheese consumption could be the thing that saves you from that fate? Per capita cheese consumption correlates with the number of people who died by getting tangled in their bedsheets.
“Per capita cheese consumption correlates with Number of people who died by becoming tangled in their bedsheets” by Tyler Vigen is licensed under CC by 4.0
But that’s not really a thing. It’s a spurious correlation – factors whose variation makes it appear that they’re related to each other, even though they’re not. And it highlights why being able to get a number or plot a graph might not be giving you the business insight you think it is.
This might sound like something that just needs better math to sort out, but really, this problem is rooted in the very nature of data, and complicated by the very human nature of decision-making.