Analysis Paralysis and Premature Optimization: What It Is and How to Avoid It

Recently, in one of my interactions with the dev community of Twitter, I came across something that I had not heard of before. So, naturally, I googled, asked and found out more. It occurred to me that perhaps, many self-taught coders and programmers out there may not have heard of those terms either. So, I compiled a blog post to explain my latest learnings briefly.

Besides writing code that solves a problem, one essential skill is to incorporate good practices while doing so. While self-learning, I think some of these things are lost upon us; at least, they are for me.

In reality, the issues we are about to talk about are not unknown to us. We have dealt with and seen them up close many times. We are talking about analysis paralysis and premature optimization.

Analysis Paralysis — a known plague

If, like me, you wrote the IIT-JEE exams, for that matter, if you have written any competitive exam, the issue of analysis paralysis is not foreign. Analysis paralysis is when we overthink or overanalyze a situation. There have been so many times when I have looked at a math problem, physics or even a logical coding problem and rack my brains to find the solution, only to find that the solution was simple. I, on the other, was finding the most complex route out of it.

Analysis paralysis has a relatively rich history to it. Though the Oxford English dictionary only acknowledges the earliest presence of analysis paralysis to the 1970s, the concept has surfaced in many other pieces of literature. The Fox and the Cat, for example, is an ancient fable in the stories of Aesop. The story shows that the fox has many escape routes, but the cat has only one, but the fox gets caught up in confusion when they have to escape. “Better one way than a hundred on which you can not reckon.”
Similarly, there is a philosophical concept regarding free will known as Buridan’s ass. It talks about an ass( a donkey) who is equally hungry and thirsty and is placed between a haystack and water. But the dilemma causes it to die of both because it could not choose though the assumption was that it would move to whichever is closer. Shakespeare’s Prince Hamlet from the play Hamlet is said to have had the moral flaw of overthinking, and we all know how that ends.

The search for perfect or the perfect solution often leaves us more paralyzed than anything else. The truth is we can’t have all the answers or have an ideal solution. Paralysis by analysis is generally a response to fear. Fear of not wanting to get it wrong. It’s not uncommon. We’ve all been there, but let’s look at its impact on tech and software development, specifically.

In software development, analysis paralysis generally manifests through the waterfall model with exceedingly long phases of project planning, requirements, design and data modelling, with little to no space for revisions. This model, over a long time, becomes bureaucratic as opposed to being functional. If the development is not contributing to the creation of high-impact work, then the system needs revision. That’s why most companies use some form of agile methodologies that allow flexibility and imbibe the cyclic and revision-oriented system.


Analysis paralysis can also occur when we lack experience or come from rigid and formal training — the gap between industry and formal education. However, extensive expertise can also cause the same problem. Too many opinions appear at every decision point.
As young, self-taught learners, how can we combat analysis paralysis to ensure high-quality and efficient work?

Finding ways to let go of paralysis due to analysis

Here are some of my tips that I have gained through conversations with others and my personal experience:

• Prioritize. Prioritize. Prioritize.

What is the importance of your task? How will it affect the outcome of the final project? Do you have to make the decision now? What’s the worst that could happen if you make the wrong decision?

As a young learner, mistakes often lead to invaluable lessons. Progress >> Perfection. Lessons, not mistakes or failures.

• The Why

I’m sure we’ve heard this many times, but it is crucial to understand why you are doing what you are doing. As they say, once you know the whys, the hows become clear. Whether it’s because it will solve a real-life problem or help you demonstrate your skills, or even add a line to your resume that showcases your skill application, determine your why. The answer to that question doesn’t need to be sophisticated; it just has to be clear to you.

• Have Chewable tasks

Being ambitious is good, but having a big task is always daunting. I do this all the time; I take on something and get overwhelmed by the sheer vastness of it.

Let’s say you want to build an app that does face recognition. For someone who is starting, that sounds scary. But if you think about it in chewable sizes, say: first getting the data, creating and training a model, then testing it, then hosting it to get real-time input. Each of these steps separately doesn’t sound too scary now, do they?

• Start with easy, and then tackle the big ones.

It’s easy to get overwhelmed by hard things. Doing the easiest task first creates the momentum of action. From there, tackle the bigger problems little by little.

• Don’t spend too much time on small details.

Being detail-oriented is needed, but if you waste too much time, you lose out of time and energy to focus on building functionality and quality.

Premature Optimization — the evil that haunts?

“Premature optimization is the root of all evil”.

It’s not that you shouldn’t optimize, but optimization is a powerful tool, so you have to pick the right time and place to do it, in addition to knowing how much of it to do.

Essentially, premature optimization means that we worry about things too far in the future, as most of us tend to do, instead of prioritizing the present scenario. For instance, handling 100 million users on a website before you’ve even hit 500 or more. The thing about coding and technology we forget in general is that it is an iterative and cyclic process. We put out something, we test it, we find issues, we fix it, put it out again, find the scope of improvement, build, test, debug- over and over again. It’s what makes tech so great. It’s not absolute.

Premature optimization attempts to optimize performance when first coding an algorithm or before benchmarks confirm you actually need it or perhaps at a lower level than your project needs. It’s an anti-pattern, with only cost and no benefit.

We’ve been lied to. Optimization is a science and an art. The trick is finding the balance in identifying what to optimize. Optimization isn’t the problem; doing too much too fast might be.

How do we avoid the “too much, too fast” and “the wrong time, wrong place”

Why do you want to optimize?

Think about whether it’s necessary and how it will add value to the functionality.

• Cost and Benefits analysis

You have to think about the costs and benefits analysis of optimizing. If it’s going to be an anti-pattern( only cost and no benefit), there is no point in optimization.

• What are the advantages and disadvantages of optimizing?

Nothing helps decision-making more than a pro-con list. So think about how optimization is going to benefit and harm you to make a more informed decision.

In the end, you need to take these considerations, so you are making high-impact, high-quality and functional products. Be deliberate about what you need, when you need and how much of it. Balance is key!