Tracking progress towards your goals with scorecard snapshots

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.

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Analyzing time spent: Time management with box and whisker plots

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:

  • streamline a process for yourself, or
  • identify when it’s time to schedule a training or other intervention for your staff.

If you can find the middle thing in a list, you can do this time analysis.

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How to design dashboards for data insights for business decisions

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.

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3 ways to use surveys to quickly figure out your next step when you don’t know what to do

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.

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Improve data integrity, data quality, and business insights with standard operating procedures (SOPs)

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.

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Choosing the “right” visualization: A diversity, equity, and inclusion example

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.

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What counts as clean data? It depends…

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.

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Vision to Action: Turning ideas into actionable and measurable steps

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.

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Hone strategy: Analyzing customer reviews and other open-ended feedback

Star ratings are an easy way to measure customer sentiment. But if that’s where you stop, you’re missing out on some rich data. Chances are that you’re sitting on an untapped source of data – the text of customer reviews and other open-ended feedback.

Why is it untapped? It’s complex, and it can be hard to know where to start, and hard to know what to do with all that new information you’ve just untapped.

Why is it worth the effort? It can give you insights that your number data cannot, and you can learn more about what your numbers mean. Even better, it can help you identify the things that will let you address multiple issues at once, because you’ll have more granular “why” data.

I cover two frameworks – sentiment analysis and thematic analysis – to get you started on tapping into this rich text data and figuring out what to do about it. If you’re into jargon, what I’m talking about is performing qualitative analysis on unstructured data.

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Decision Log: A tool to turn your decision-making into auditable 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.

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