Data has become a critical strategic asset. Even so, a study by the Business Application Research Center shows that many organizations still struggle to make data-informed decisions. There are a variety of reasons for this struggle, including challenges with data governance and data skills.

Look for non-obvious in your data see what you don't see

See what you don’t see

Critical thinking – defined as the analysis of facts to inform judgement – is an essential skill associated with using data. It entails being able to make a logical connection between information and ideas. It requires the ability to ask probing questions like, “How do we know?” or “Is this true, and if so, under what circumstances?”

Good critical thinkers are skeptical and challenge assumptions. Machines can be taught to identify patterns in historical data, but it takes a different (human) skill to see what isn’t in plain sight and understand whether it is valuable.

To illustrate the idea, we can use art examples (like the ones shared in this post) that leverage foreground and background to create optical illusions. In these pictures, the foreground is obvious to most people. The “hidden” images, for example the seated man image that forms the nose, may be harder to see. Optical illusion art manipulates the rules of perception. It requires the viewer to be able to decipher the painting to see more than what is obviously there.

This occurs with data as well. Why do we see the obvious in data so easily? Context. Context puts things into perspective. Meaning is derived from context. As a result, context partly determines what is obvious. But, like the painting, sometimes what you don’t see is as important as what you do.

How to Think Like a Data Scientist

Insights from the non-obvious data are valuable to companies who want to grow. How can you develop critical thinking and skills to see what isn’t obvious in your data? Start with these three ways.

1. Be aware of your assumptions. Your assumptions greatly influence what you see and what you don’t. Consider your assumptions and those of your team and company. The same data can yield different insights because of unstated assumptions. As a result, people can and will make different conclusions, and therefore different decisions, depending on how they “slice and dice” the data.

2. Apply context. Context can completely change how you look at your data. It can help you decide what the numbers represent, how to interpret them, and what is missing. To see what isn’t obvious, you need to learn how to shift context. Practice. Avoid approaching your data the same way every time. When you become competent at switching context, new insights can emerge.

3. Focus on generating insights that produce good ideas. Data is not an insight. Observations are not insights. An insight is a discovery. Insights cause us to reexamine what we think we know. They are the fuel for ideas and decisions around “How might we…?” How might we better engage with customers? How might we tap into new markets? Looking beyond the obvious can often lead to greater insight.

Non-obvious data science analytics

Spot it! Can you find the 7 people and 1 cat?

Spotting the non-obvious is a critical skill for anyone working with data. If you focus only on what you see in the data to make decisions, you might miss the bigger picture. When you can see both what is and isn’t in your data, you can make more informed decisions. If you’re too close to the data and/or company-wide assumptions are too ingrained to achieve needed growth-oriented insights, consider tapping external experts who can help you shift perspective.

 

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