Asking questions is an essential way to find meaning in information. Many leaders want to ask questions but don’t know where to start. Ideally, you ask questions based on your existing knowledge and the current context or situation. Therefore, every situation will have its own unique set of questions. However, to get started, some basic questions will work in any situation. They follow a simple question formula:
- What is it?
- What should it be?
- What patterns do I see?
- Does it matter?
All of these questions can help you find meaning. However, the latter questions will drive greater meaning. You generally can’t make a decision or take an action until you reach at least the third level. Therefore, I recommend that leaders spend most of their time on the third and fourth questions.
What is it?
The most basic way to derive meaning from information is to understand what that information is. I’m often surprised how little many leaders know about the basic mechanics of their metrics.
For example, one company had a “customer satisfaction” metric on their scorecard.
I asked the managers what that metric meant. Their answer seemed to make sense, it measured how satisfied their customers were. They said it was based on the customer satisfaction survey. But the survey was based on a one to five scale and their customer satisfaction score was 85. I asked how that could be.
Most of the managers didn’t understand how the score was calculated. In addition, they didn’t understand what else was done to it.
They were surprised when I told them that it did not measure customer satisfaction.
The metric was a percentile ranking. It was telling them was how their satisfaction score compared against similar companies.
This means that if all other companies were very bad, and they were just bad, they would be in a high percentile. Similarly, if all other companies were great, and their company was good, they’d have a low ranking.
For the first time, many of them understood the metric and were able to derive meaning from it. One manager said, “My boss kept telling me that my customers weren’t happy because our score was below target, but when I talked to customers they seemed happy. Now I understand why. They are happy, they just aren’t as happy as they could be at our competitors.”
By understanding, what the metric was telling them, the managers were able to change their messages, decisions, and actions.
Understanding what a metric is specifically measuring is the first step toward creating meaning. Here are a few other questions you should ask to answer, “what is it”:
- What is included and excluded from this metric?
- How frequently is the metric calculated?
- How is it computed? What are the factors that go into it?
- Is it a direct measure or a “proxy” measure
- Is it a relative or absolute measure?
For a detailed explanation of these questions, check out How Well Do You Know Your Metrics?
What should it be?
Once you understand what the measure is, the next step is to understand what it should be.
Suppose I told you that I purchased a car for $5,000.
By itself, that information is meaningless. You can’t make a decision or draw a conclusion from it.
To create meaning from a data point, you need to have an idea of what it is supposed to be. If I tell you that the car was a 2014 Lexus you’d draw one conclusion. If I told you it was a 1981 Chevy Chevette you’d have a very different response.
Leaders should never look at information without an idea (which might be proven wrong) about what they expect to see. Several sources will help determine what the number should be:
- Company goals/targets
- Historic performance
- Benchmarks
- Current context (e.g., if you’ve just invested in a marketing campaign, you might expect that sales would be up)
The problem that many leaders fall into is confusing which number they should use. I like to look at this question from three perspectives: 1) What is a realistic expectation for our current performance (goals/targets, historical performance, context), 2) What have we committed to (goals/targets), 3) What is possible (historic performance, benchmarks, context).
Each one of those questions drives different decisions. The first question, realistic expectation, is probably the best for diagnosing and taking action. The second question, commitment, is best for understanding potential impacts on other issues (and impact on your bonus!). The third question helps you understand where there are opportunities for improvement.
By creating an expectation for what you should be seeing, you create a trigger point for taking action.
What patterns do I see?
Understanding what something should be prompts action. But it doesn’t tell you where or what action to take. Understanding patterns will help isolate the problem.
There are two types of patterns to look for to create meaning: causal and categorical.
Causal patterns, as their names suggest, are patterns that show how one thing influences another. By understanding they key levers of your business, you can take action. For example, one organization found a pattern between time per service call and number of return service calls. They discovered that their technicians were cutting corners to hit their “time per call” target, which was driving down quality.
Categorical patterns highlight common themes. For example, a company discovered that most of its complaints came from stores in the same region. Replacing the regional manager had a huge impact on performance. Sometimes a categorical pattern is also causal. Sometimes it’s just a simple correlation. But even simple correlations can help drive decisions. The insurance industry found a correlation between credit scores and underwriting risks. That helped them make better decisions.
By understanding patterns, you will be able to determine where to focus your actions.
Does it matter?
“Does it matter” is the ultimate “meaning question”. It is the question that should be consulted first before launching a response to a problem. It’s also the question that I often see overlooked.
One organization was having a problem with parking in its facilities. Customers regularly complained to management and, on satisfaction surveys, rated parking extremely poorly. However, additional analysis showed that, although customers were frustrated, it didn’t influence their buying decisions. They were so satisfied with the quality of the company’s product that they were willing to put up with the poor parking situation. It didn’t matter (in the grand scheme of things).
Had the company simply reacted, it could have wasted a lot of money investing in improving parking with no overall benefit. Of course, most companies want to make the overall customer experience as good as possible. So, why not improve parking? If everything else is working, perhaps this would be a good investment. However, if an investment in parking offsets an investment in something that matters more to customers, the organization has made a poor decision.
Ask questions
These four questions can provide the basis for better understanding your business and finding meaning. These questions aren’t just limited to interpreting data. They can just as easily be applied to discussions about career development, strategy, or any aspect of your business. The more meaning you create, the better your decisions and actions.
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Brad Kolar is an executive consultant, speaker, and thinking coach with Avail Advisors. Avail’s Rethinking Data workshop can help you find more meaning in your data and information. Contact Brad at brad.kolar@availadvisors.com.
