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How to improve your analytical skills

Here, we look at analytical skills, why they’re important, and what you might do to improve yours!

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Being able to analyse effectively is crucial to both our personal and professional fulfilment. We can all see that managers, researchers, engineers and data analysts might spend time reading reports and poring through data. But plumbers, electricians and other tradespeople are effectively doing the same thing, and often with more complex sets of data.

The same goes for our interpersonal relationships. If we aren’t getting along with a friend, colleague or spouse, then breaking down the issue and working out its cause and effects can often be tremendously helpful. 

The truth is that we’re all analysts of a sort. As such, analytical skills play a huge role in determining our prospects. Anything we can do to improve them, therefore, is something worth doing.

What are analytical skills?

Any skill that allows us to better analyse the world around us can be considered an analytical skill. These skills come in a whole range of forms, but in most cases, it’s about recognising potential sources of error and correcting for them.

Analytical skills definition

We all might think we have an intuitive understanding of what it is to analyse something, and what the skills that help us do that might be. In a colloquial sense, we might consider the term synonymous with ‘figuring stuff out’. 

But for our purposes, we need to get a little more specific than that. We need to get analytical about what it means to be analytical!

What’s the meaning of the term ‘analytical’?

The word ‘analysis’, and all of its derivatives, can be traced back to the ancient Greek term ‘analyein’, which means ‘to loosen up’, or (more helpfully, in this case) to ‘break up’. Analysis, by definition, requires breaking apart a complex problem into smaller chunks. If you haven’t done this, then you haven’t analysed.

Analytical skills examples

To illustrate this, let’s run through an example or two. Imagine waking one morning to find that your car won’t start. You might take this information to your local mechanic, whose job it is to analyse the problem, determine the precise cause, and suggest (and ideally, implement) a solution. 

This requires an in-depth knowledge of the major components in the vehicle and how they all interact with one another to create the desired effect. 

To take another example, you might visit your doctor complaining about a strange rash that you’ve developed on your stomach. They might ask you questions about your lifestyle and background, and perform tests designed to isolate the likely causes. 

In either of these cases, it’s easy to tell the difference between an analysis of a problem and a mere description of it. If your mechanic tells you that your car won’t start because your car won’t start, then they haven’t performed an analysis.

The problem hasn’t been broken apart. If they tell you that it won’t start because the starter motor isn’t turning because it isn’t receiving power, and that the battery therefore needs to be replaced, you’ll probably be more satisfied.

Knowing your limits 

Of course, there are limits to the scope of any given analysis. A mechanic doesn’t need to know about the precise cause of every fault that might develop in the engine, down to the level of the microcircuitry in the on-board computer or the chemistry of combustion. In most cases, this depth of analysis isn’t going to address the problem at hand in a timely fashion.

All of this is a way of saying that a good analysis is one whose scope is well-defined. This usually means conceptualising the task well in the first place, and then taking a deductive approach, starting with the likeliest culprits and gradually narrowing focus towards a solution.

Analytical thinking

Thinking analytically is something that we can learn to do with practice, consideration, and in some cases, instruction. It doesn’t always come naturally, however, and there are a host of common biases and fallacies that decision-makers will need to be conscious of and vigilant against if they’re to succeed in their analyses. 

Often, a quality analysis is one that’s been arrived at collectively, where members of a team are encouraged to critique the assumptions which underlie a particular conclusion. This helps to guard against groupthink and to ensure that a range of possibilities has been considered.

Of course, any analysis is only going to be as good as the data that goes into it. It’s therefore essential that we make the right observations before we apply any analytical thinking. This applies whether we’re diagnosing a malfunctioning car or trying to perform market research ahead of a new product launch. 

What are the most important examples of analytical skills?

Analytical skills can be thought of as a collection of different ways of thinking. They incorporate different thought processes and methods of observation. Below we look at two such skill sets.

Critical thinking

Thinking critically is among the most important analytical skill. Again, we all might think that we have an intuitive understanding of what critical thinking is. Nevertheless, it’s worth settling on a definition. So what is critical thinking?

What is critical thinking?

Several definitions have been put forward over the years, and not all of them are all that easy to get your head around.

According to Michael Scriven & Richard Paul’s address to the National Council for Excellence in Critical Thinking, 1987, critical thinking is “[…]the intellectually disciplined process of actively and skilfully conceptualizing, applying, analysing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action.” 

If that sounds a little bit intimidating (or completely nonsensical), you might think of critical thinking as the practice of thinking about why you believe the things you believe and trying to figure out whether you have a good reason for believing them.

How analytical skills are used in critical thinking 

For example, suppose that you’re talking to a friend about football, and they suggest that the star centre forward should be sold immediately to a debt-burdened club on the continent. You might ask why they think so, and they might supply you with any number of reasons. 

These might include: 

  • “He is nearing the end of his contract and the club must cash in while it can.”
  • “His form is in decline and there are other players available to take his place.”
  • “I had a dream in which it happened and therefore it will happen.”

You might find some of these reasons more convincing than others. But it’s only when you seek to explain your preferences that you’re engaging in critical thinking. When we describe the above reasons as good or bad, we’re really describing their ability to withstand our scrutiny.

In many cases, the sources of error are more subtle. In Daniel Kahnemann’s influential book, Thinking Fast and Slow, he refers to a number of heuristics (a method that may not be perfect but that will nevertheless allow the user to reach a working approximation) which we often use in place of critical thinking.

Among the more common is the ‘Availability Heuristic’. Rather than calculate the odds of a given event occurring, we tend to rely on how easily we can imagine it.

If we’ve recently seen a news segment about a car accident, a natural disaster, or a disease, then we might assign a high probability to ourselves being involved in the same situation, regardless of what the statistics say.

Critical thinking training helps us to recognise intuitions like this and counteract them, whether the error is coming from ourselves or from others.

What critical thinking training is available?

The truth is that if you’ve gone through any kind of training or instruction, you’ve engaged in some form of thinking about these types of processes. There are considerable benefits, however, to more specialised courses in logic and critical thinking. These can help you to perform your job better, or to get the university place that you’re looking for.


Problem-solving skills are immensely practical. In just about every walk of life, there are problems to be solved. Demonstrating that you can solve problems will make you attractive to would-be employers — and should therefore be a key part of your CV.

What types of problem-solving techniques are there?

An effective problem-solving process can be broken down into distinct stages.

  1. First, we identify the causes of the problem. 
  2. Next, we posit potential solutions to the problem. 
  3. Then, we analyse those solutions to see which ones will generate the expected outcome. What might prevent a given solution from working? 
  4. Having rooted out the ineffective and impractical solutions, we’re left with the ones that will actually address the problem. Sometimes, this might be a single intervention; most of the time, it will be several. It’s time to put the plan into action! 
  5. Of course, the process isn’t quite over. We still need to determine whether the problem has been solved, and the plan is effective.

How can I improve my problem-solving skills?

You can use the above process to solve the problem of how to improve your problem-solving and creative skills. 

First, look at your weaknesses. It may be that you’re great at spotting potential problems ahead of time but less good at coming up with solutions. It may be that you rush toward the wrong solutions, without properly analysing them first. It may be that you declare a problem solved and don’t come back to analyse whether it’s been successful.

Everyone has strengths and weaknesses when it comes to problem-solving, just as they do elsewhere. Consequently, it’s often a good idea to surround yourself with colleagues and collaborators whose strengths and weaknesses offset your own. 

When might you use analytical skills?

We’ve already seen just how important analytical skills can be. They’re used practically everywhere — but there are a few situations in which they’re called for explicitly.

Of course, it’s possible to over-analyse a particular problem to the extent that you spend so long analysing that you delay taking action. But this isn’t an argument against analysing, or the importance of analytical skills — the only way that you’ll be able to find out whether you’re overanalysing, is in fact, to analyse! 

Analytical skills tests in interview situations

When companies conduct interviews, they may well seek to determine whether a given candidate is able to analyse. And they’ll do this by posing questions that might, at first glance, appear strange. They’re not interested in the answer, per se, but in the process through which the answer is determined.

You might be asked how many cubes make up an arbitrarily-large Rubik’s cube. Say, it’s 10x10x10. Since a Rubik’s cube doesn’t have any cubes beyond the top layer, you’re really calculating the external faces.

There are a few ways you might figure this out. You might determine what the total surface area of the six faces are, and add them together. But then, you’d need to make sure that you aren’t counting the cubes on the edges twice.

Another way would be to work out the dimensions of the space inside the cube, by working out what eight cubed is (eight being ten minus two). 

What this question is designed to test is not your competence at basic maths (though that’s an advantage) but your ability to break down a problem and determine the best possible angle of attack.

Data analytics

Nowadays, thanks to modern technology, we’re able to collect masses and masses of data points. To act upon this data, we need to analyse it, and to do that, we need the right technological tools. This means competence in database software like SQL can be exceptionally useful, as can competence in languages like Python.

Organisations today tend to employ different kinds of analytical models. These variously seek to describe the problem, determine the causes of the problem, predict what will happen next, and explain why it will happen.

Big data analytics

Big data analytics involves poring through lots and lots of information very quickly, and with a high degree of accuracy. For example, if you’re working for an insurance company and need to determine exactly what factors correlate with an increased frequency of claims. The postcode we live in, the number of miles we travel, the type of car we drive, and our age might all contribute. 

By analysing data with modern deep learning techniques, we can draw more informed, accurate conclusions about risk. We can also think about types of risk that aren’t yet visible to more traditional methods.

For example, the data collected by consumer-worn heart rate monitors might be able to predict particular health outcomes and spot correlations that would otherwise go unnoticed.

Tools of this kind are highly adaptable and can be applied just about everywhere. Medical institutions, banks, bookmakers, legislators and aeronautics companies might all benefit from analysis of this type.

While big-data and algorithmic analysis isn’t going to supplant flesh-and-blood decision-makers (or at least, not yet) it can provide a valuable tool that will help people to draw better conclusions.

Final thoughts

Analytical skills are highly sought after by employers because they’re extremely useful in just about every kind of human activity. If you’re moving to a new role or planning to go into business as an entrepreneur, then the right analytical skills can also be tremendously beneficial. 

Whatever it is that you’re doing, the chances are good that you might be able to do it better — with the right kind of analysis training.

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