Thinking About Thinking

Thinking about thinking, we don’t usually think about that, but that’s what dominated my head this week. The way we think has a massive play into how we see things, how we analyze, and how we come to conclusions. Now this skill, analytical thinking, I see it a lot in job ads, skills in resumes, and sometimes a prerequisite for some courses, but what is this skill? And how can one know if they have it or not? This is what I want to explore today, and let’s start with what is analytical skills?

What is meant by Analytical Skills?

True it revolves around looking at something be it data, a situation, a problem and evaluating it, coming up with hypotheses, patterns or a possible solution. And I believe the course I’m taking defined it as qualities and characteristics associated with solving problems using facts.

Okay what does that mean? Let’s start with qualities, what qualities can be associated with analysis?

• Curiosity: you need to be intrigued, wanting to explore more about a topic, research and which brings us to the second point
• Understanding context: by looking into something (the data) and understanding the condition in which it exists, by seeing, and listening to fully understand the picture, and once you have the context you bring out your
• Technical mindset: don’t let the label confuse you, it has nothing to do with technology but more on how to approach a problem/situation as in breaking things down into smaller steps or pieces and work with them in an orderly and logical way

Now you can look at these and think, isn’t that just common sense, and you’re not wrong at all, I thought the same thing. While studying, I thought to myself “hey, are you talking about intuition? Common sense?” because we do this all the time. Let’s take some examples, to illustrate the default nature of these qualities in us.

You’re reading a mystery novel (one of my favorites are Agatha Christie’s), while doing so, you try to gather as much as you can about  the characters, and try to get an idea about their personalities (curiosity), how each of them relates to the other and the situation they're in (context), now you're on a race with time (or pages) trying to build or rather predict the outcome and culprit by breaking it down into steps from things like “they mentioned that brother in law practiced medicine,, maybe there is a backstory there that ties with what happened” and all the while trying to figure out the culprit before detective Poirot points the culprit and plot.

Maybe a better example would be you having a couple of errands to run, you need to pick up your dry cleaning, get groceries, and pass by the notary office. So you ask yourself, can I do all of these today after work? (Curious) So you check when each place closes, how far they are from your location, estimate the time it would take you to complete your task in each. With all those information you saw that based on your current location and your home as well taking time into account you can achieve it (understanding context) You then broke it down into the steps you’ll take by going to the  notary because they close around 8 p.m and you’re not sure how long they’ll take but you’re comforted by the fact there is a grocery store right across the street, which will be your next stop, and then you’ll head over to pick up your dry cleaning since their close by to your house and open till 12 a.m (technical mindset)

So as we can see, we do this all the time, and that’s great news because, we kinda have analytical skills innate in us, the only missing bit is that at times we use hunches and intuition to guide us, but if we look back at the definition, it mentions facts, which is data. What we need to develop these characteristics to make them complete is using data to guide these qualities.

To complete the analytical skill set we need to add two more points to the 3 we already mentioned which are

• Data design: thinking about how to organize data and information. Think of it as how will you connect these data points and present them in a way that explains the results
• Data strategy: thinking about the people (which is you or the group you’re with), processes (steps you take to gather data or instructions to follow), and tools (sheets, resources, graphs) used in data analysis

How to think Analytically?

We explored the characteristics of analytical skills in the previous section, and since I mentioned that thinking is second nature to us, why am I asking how to think analytically? Yes, it’s second nature, but there are different types of thinking, some think visually, others creatively, some analytically and others think in abstract ways.

I was watching a movie with my sister the other day, and I remember pointing out why I think a specific scene happened and that the following event is most likely to be this. She looked at me skeptically and decided to ignore my comment. Then what I predicted happened, and she looked at me, and said, “How’d you know that?!”, I told her that it’s obvious based on this and that, and she said “I didn’t think of that”.  I know the story is vague because I honestly don’t remember the movie, but what matters was the last sentence “I didn’t think of that” which illustrate the point that we think differently, but that doesn’t mean one can’t think in that way. Just like anything in life it needs practice.

Which brings us back to the point of how to think analytically. We do so by combining the five characteristics mentioned earlier with these aspects  associated with analytical thinking

1. Orient the problem: in this phase you approach it with asking questions that will in turn be used to identify, describe, and solve the problem. Curiosity comes in handy in this phase.
2. Decide on a strategy: next you move on to the planing stage where you strategies, with all the data around us, having a strategic mindset is key to staying focused and on track. Strategy helps you see what you want to achieve and how to get there. It also helps in improving the quality and usefulness of the data you collect. Technical mindset can be used at this stage as an aid.
3. Look for correlations: with your problem defined, strategy all set, and data in hand, it’s time to look at the data and analyze it, and one approach is spotting correlations which is the ability to identify a relationship between two or more pieces of data. With that said, make sure to understand that correlation does not equal causation. Both data design and data strategy can help in this step.
4. Approach data in Big picture and detail oriented thinking: While looking and analyzing your data, make sure to approach it from two points. Big picture, where you understand the problem on a broad level, by evaluating it in its entirety, connecting ideas and finding patterns. Also, consider the details, where you evaluate a situation by separating it into detailed parts, and identifying individual steps to take and implement to execute a project, overcoming obstacles and finding information. Powered with both approaches you’ll get some very insightful data.
5. Visualize your findings: Now that you have your insights, and conclusions, it’s time to express it, and what better way than visually. You can represent the data as graphs, Maps or other design elements. It’s important because they can help in understanding and explaining information more effectively, and in my opinion they don’t have to be graphs, they can be doodles, sketches, because how did the saying go “a picture is worth a thousand words”

Helpful techniques

We now have a framework we can use in our analytical journey (if you have another approach please share it with me, I’m eager to learn) but sometimes we can get a bit stuck or we can have a blank thought? (Not sure what’s the equivalent for blank canvas syndrome) anyways, there are aids that can help us start, let’s explore them.

• Root cause. This can be very helpful in the orient the problem phase. It’s basically you try to figure out the root cause of a problem/situation and you can do that with the help of the 5 why’s. Basically you ask why 5 times to reach the cause of the issue.
• Gap analysis. It’s a method for examining and evaluating how a process works currently in order to get where you want to be in the future. The general approach is to understand and identify where you are now, compared to where you want to be. Then you can identify the gaps that exist between the current and future state and determine how to bridge them. This is perfect for the strategy phase.
• Asking, what did we not consider? I believe the question explains it’s self, and it’s a good technique to use in the looking for correlations step.

Takeaways

I wrote this in an effort to help me, and hopefully others recognize that technical thinking is innate in us all, after all, we think around 70,000 thoughts per day. But identifying the characteristics help clarify the process we go through to come up with conclusions, and using those characteristics with the framework outlined, hopefully with practice we get to get better at making decisions and conclusions based on data rather than gut instincts. I want to work on developing my analytical skills, and in a later post I’ll talk about how I plan to do so, but for now, lets enjoy the fact that we have skills that we weren’t aware of, and now mindfully using the framework with the techniques we can now make data driven decisions.