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.
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.
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.
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.
You might have heard about the concept of classification under different names like ‘segmentation,’ ‘categorization,’ or ‘hashtags.’ Classification is basically the process of chunking up or organizing your data into different groups or under different labels so that you can use it to better support your business strategy.
In practice, this enhances your ability to do things like target email marketing to a specific demographic, court different types of non-profit donors, and check on your business pipeline by a particular product or salesperson. How?