It’s the start of a new year and everyone seems to have an opinion on setting resolutions and goals. Some are repeats of previous years’ attempts. Some are new and outside of conventional thinking. All are anchored on a time of renewal, transformation, and growth. 

By February, however, many of those good intentions have fallen away and we are back to our old habits and routines. That was so much of my thinking before listening to the December 31, 2022, DataFramed podcast with Gary Wold, Co-Founder of the Quantified Self.

There was talk about setting goals, measuring performance and outcomes, and all of the analysis that can go with realizing our personal goals. 

Yet, I could not help but take away a theme that Gary drove home: setting goals and tracking results does not need to be an overly complicated data exercise. In fact, wearables and other trackers are not even requisites to get started. 

Gary talked about design and data collection around simple questions and observations. For example, if you are dealing with anxiety or depression, note at the same time every day how you are feeling. You can decide what you measure (such as a scale from 1 to 10) and when (before going to bed). Doing such an activity even for a few weeks can be incredibly telling. Not only will you see trends, but you will also start to get an appreciation for potential contributing factors, such as the amount of sleep, what you eat, or how stressed you are on particular days.

This simple approach should be in everyone’s toolbox when using data to drive change. It does not matter if you are setting personal goals or managing your team. The process of framing the question, tracking observations, analyzing the results, and applying the insights can be as basic or complex as you like.

I tend to break it down this way...

  1. Start with a curious mindset

    We all have things we want to change, improve, implement, and accomplish. In many cases, we lead with the end state, such as getting to a certain weight or making a sales target. By their nature, these are straightforward numbers to put on paper. Your target may be more ambiguous such as wanting to be happier or getting a certain client’s business. The issue for any of these examples is they are often mid-to-long term results oriented and our feedback loops require something more near-term to know if we are on track.

    The idea of the curious mindset is that we don’t often know which factors directly attribute to a specific outcome. We have a set of patterns, practices, and processes we rely on to get us there. Leading with a curious mindset changes the discussion to observations about what is ascertainable at the moment. What can you see/feel? What can you compare it to? Explore questions like these to get started.

  2. Devise a simple question to track

    Gary talks a lot about the complexity that people often put into their data collection process design, which can become overly engineered and hard to measure. The desire for the perfect data inclusive of all the factors leads to debilitation and ultimately undermines the opportunity. 

    Consider answering the same question every day; just one that you can easily capture for the next two to four weeks. 

    Was I tired at the end of the day?
    How would I rate my mood?
    How did I do following the process? 
    Was I productive today? 

    And by “capture”, I mean to collect your observations in an easy-to-use tool. It could be a tally sheet on your nightstand, the notes app on your phone, or a few columns in a spreadsheet. The intent is to get your “data” in one place with as little friction as possible.

  3. Plot the initial results

    By keeping the questions about the situation simple, you start to build observations and an understanding that can be charted and explored over time.

    Pick a way to visualize your two-to-four weeks of observations. It may be a bar chart. It could be a color scale. Whatever it is, take some time to see what has been happening against your questions.

  4. Review the observations

    It’s easy to become critical too soon and make conclusions. Instead, take a step back and observe the distinct data points and the time period as a whole. Do you see a relationship across the points? Do they reflect a trend? What might that trend imply?

    In some cases, it may warrant you to think about specific data points. Do you recall anything from those observations? Was there something special about that event worth noting?

    A few things happen at this point: You may find the initial assessment meaningful. It may give you enough to start to make decisions to either continue or modify your plan.
    Another thing you may determine is that there are underlying factors for your data points which beg further questions. For example, if you are tracking your mood and you know that you did get enough sleep, perhaps it’s worth tracking your hours of sleep and your mood for a few weeks. Another example might be if you are tracking your sales performance and you see a decrease in your call volume. Are you aware of events prohibiting you from getting in your requisite number of calls? Perhaps this is a better item to track.

    Lastly, you may determine that while this particular study is interesting, it can be concluded if you accomplished your goals or if you are not able to align the results to your desired outcomes. Your next step may be to start a new study based on your next set of questions.

Take Away

The conclusion here is that we can become overwhelmed when it comes to data and measuring results. Many clients we talk to don’t know where to start. Taking the observations from the podcast and our own experience, we would suggest just starting somewhere with a question and capturing one or two data points. The exercise alone will help frame how you approach your goals and track progress. 

Want to get more of our insights and learn about how our clients do this? Let’s set up a call. 

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