How to Solve Ambiguous Problems with Simple Data Visualization

Visualization of data often yields better insights that just looking at the numbers.

I routinely encountered optimization problems and I struggled to solve them effectively until I developed this simple framework.

In this post, we’ll work through the example “Where should I live?”
Click here to access the example spreadsheet.

Optimizing for real-world problems is usually challenging because they tend to multivariate. They also tend to impact more than one stakeholder, which often means there are different priorities that should be considered. Additionally, variables can often inversely correlated, qualitative in nature, and/or unique solutions have wildly different values.

Whenever things get complicated in my life, I try to develop a process that I can tune as I gain more experience. Here’s my current approach for optimizing complex decisions.

Steps to Resolution:

  1. Determine Variables
  2. Weight Variables
  3. Collect Data
  4. Visualize Data
  5. Discuss quantified result

Determine Variables

Create a list of all meaningful values to consider when evaluating the problem. Then, ask other stakeholders about what factors they would consider when determining a solution.

As these conversations occur, you should document the entire list of concerns in a spreadsheet.

Example Variables for “Where should I live?”:

  • Proximity to Work, Family, Friends, Food, Activities
  • Rent/Mortgage
  • Pet/Child-Friendly (Booleans)
  • Cost of Living Adjustments
  • Additional Income Possibilities (Rental units, access to a better job market)

Weight Variables

Have each stakeholder (including yourself) put a weight on each variable privately. Then gather together to discuss the reasons for each weighting. Based on the discussions, work as a group to determine your final weightings. This socialization of concerns often helps me achieve a deeper understanding of my own goals, while also getting buy-in from people who will be impacted by the decision. Lastly, generating these “shared ideals” weightings allows you to focus on the most important aspects of the data when evaluating the visualization.

Collect Data

Aggregate your data in your spreadsheet. Sometimes it helps to normalize data if the options are very disparate in their values. One way to normalize is through the use of ratios between two of the variables from the same option.

In this example, we create a ratio of Total Cost of Residence / Take Home Income in order to normalize the Total Cost of Residence for different geographic areas.

Visualize Data

Use conditional formatting (tutorials: google sheets / excel ) to heatmap cells or simply turn cells red/green for boolean answers.

Implementing this visualization technique will usually highlight one or two prime candidates from your data.

While I’m building my visualizations, I also highlight the top three column headers, based on the outcomes of the variable weighting discussion. This additional step highlights the “deal breakers” that sometimes exist in candidates that otherwise look great on paper.

Spreadsheet showing highlighted cell that is a "dealbreaker"
In our example, “Middle of Nowhere” looks like the best candidate, until you realize that it’s the worst candidate in regards to our “shared ideal” distance to family.

Discuss quantified results

Last but not least, bring the visualized results back to the group. They provide a great starting point for discussion among stakeholders. Since you determined your “shared ideals” before you gathered/evaluated your data, it allows you to have a more candid discussion of the options.

One last thing, It’s important to remember that all tools have flaws and the winning result in the rubric isn’t always the best solution qualitatively. You’re not bound by the tool that you created, often the discussion it generates is worth more than the data it provides.

Question of the Day

Do you have a similar process? Where have you applied it in your life?

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