Do you ever overthink or overanalyze to the point where you stop making decisions and taking action? Where you’re afraid that the choice you’re about to make isn’t the BEST choice, so you wait it out a little more, and maybe research a little more, and maybe gather together more data … just so you can be SURE? That’s called analysis paralysis.
Debunking the myths of being data driven and being successful can set you free.
If you’ve ever targeted an email marketing campaign to a specific demographic, assigned a category to your blog post, or chosen a hashtag for your social post, you’ve used data classification. Classification is basically the process of chunking up or organizing your data, into different groups or under different labels, so that you can quickly isolate and bring together all of the things that belong to that group, so that you can do something with that group:
In this post, I’ll discuss the problems that arise from unclassified or improperly classified data, and give some pointers on how to create and apply your own classifications.
In order to effectively close the gap between where you are now and where you want to be, you need to know that the gap exists and how big or small it is. One way to do this is with a reporting tool called a scorecard. Much like the checked and unchecked items on a to-do list, a scorecard gives you a snapshot of the gap between where are you now and a target, where the target can be a growth goal you’ve set for yourself, or a projection of where you think you’ll be by a certain date, all things considered.
We all know that there are only so many minutes in a day, and that goals like being more productive and effective hinge on things like better time management, that is, working smarter not harder. This is particularly true if you think you’ve got a pretty standard process in place. In this post, I’m going to demonstrate a visualization tool called a box and whisker plot. This tool will help you determine how long you can typically expect your standard process to take, and how to spot when there’s enough variability to say that it’s time to reexamine what you’re doing, so you can:
If you can find the middle thing in a list, you can do this time analysis.
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.
Is your data worth tracking? This is a question about the quality and integrity of your data – its accuracy, completeness, and fit with your use cases. And it’s an important question to consider, because low quality data can mean that your business insights are ill-informed. I’ve covered some of these points in my posts on clean data and data classification. In this post, I’m going to focus on how standard operating procedures, or SOPs for short, can help you improve the quality and integrity of both your data and your business insights.
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.
We’ve all been told, ad nauseum, that it’s important to have clean data, and, if you’ve looked into it, you know what it can cost to have someone clean up your dirty data. But what does it mean to say your data is clean? In this post, I provide a definition, and discuss why someone else’s clean data might not be clean for you.
It can be hard to figure out how to turn big picture ideas and goals into actionable and trackable steps. For many things, the “figuring out” lies in a mixture of clarity, and then reframing and rephrasing. Here we take a monetary target and some other information from a business model, and turn it into something that’s doable, measurable, and actionable. If you’re stuck at this part of the process, this post might help.