How Computational thinking can accelerate productivity in NewGen workplaces?

The 21st century is distinguished from its previous millennia by the technological breakthroughs of the past decade. The internet, the biggest invention of this era, is what triggered a paradigm shift in people’s thought process and even defined new rules of communication. It brought people together from different realms onto a common platform to initiate collaborative conduct for a better future. It broke stereotypes and made possible the accessibility of information and knowledge to a large community, bringing them closer. Such capabilities were made possible by the computational prowess amassed by us and how the silicon revolution brought in a new wave of powerful hardware resources that was within reach for everyone.

The tech industry is currently the biggest employer and investor in all major countries. Millennials constitute the netizens of today’s population and they are the biggest consumer community, be it for technology or the products of pop-culture. They define the industrial standards by setting new rules for consumerism. In this new battlefield governed by technological innovation, anyone who is slow to adapt may be left behind and potentially, lose the battle. This calls for a new direction for an individual’s thought process. They need to think from a computational perspective. This institution of thought is called Computational Thinking. It is an essential skill one needs to develop, regardless of their area of interest or their discipline of study, be it arts, literature, engineering or medicine.

What is Computational Thinking?

Computational thinking is often mistaken for a skill set that a computer science expert should master in order to develop solutions via computer code. While that is true, this skill set need not be limited to them. Incorporating computational thinking to science, arts, medicine etc. brings in vast possibilities and problem-solving capabilities so that newer, more complex problems can be tackled with ease. Computational thinking enables a person to formulate a solution for any task at hand. For achieving this, they incorporate knowledge from a multitude of domains, incorporating maybe one or a network of computing resources to effectively remedy the problem. It makes them a good strategist and a capable problem solver.

The concept of computational thinking is applied by researchers, leaders, businessmen and several other top-tier personalities knowingly or unknowingly to tackle everyday problems and this helps them rise above the rest of the crowd. A typical computational thinker has two inherent thinking skills: logical reasoning and critical thinking. A good computational thinker analyses a problem critically and formulates a logical solution. In other words, a person who thinks computationally can break down a complex problem into simpler, more legible format that could be understood by both humans and computers.

Consider computational thinking as a pyramid. It could be broken down into four fundamental layers from the foundation all the way up to the top. These four main layers or foundations are,

1. Decomposition:
   This is the initial step, where you break down a more complex problem or process into easily understandable and manageable blocks.
2. Pattern recognition: 
   It involves observing the data or process for patterns, general trends or some forms of irregularities or perturbations and use to devise plans to manage it wisely.
3. Abstraction: 
   Understanding, analyzing and identifying the factors influencing the former patterns and comprehend the general principles that govern it.
4. Algorithmic thinking: 
   Finally, a proper algorithm needs to be developed, which outlines the step-by-step instructions to solve the problem in an automated fashion and it should be capable of handling similar problems in the future.

To explain these components effectively, let us consider a real-life scenario and how computational thinking helps you overcome a challenge. Imagine that you are on your way to the office. On reaching your parking spot you realize that you have trouble with your car; it has a flat tire and is low on fuel. Now you have a problem that needs to be solved and that too quickly as you don’t want to be late to work. Identifying what’s wrong is the first step towards devising a solution. this is the decomposition phase – you identify that the car is low on fuel and that it has a flat tire. You know that you can’t move the car until you fix the flat tire. Pulling information from experience to reach that conclusion is your pattern recognition ability at work. You have had this car for a while and therefore, you know how much gas mileage it gives. You can then conclude that the current fuel level is enough to get you to the next gas station. Now you decide to prioritize – flat tire needs to be fixed first; so you focus on that. Here, you are devising an abstraction to the whole problem. Finally, you use a jack to lift the car up, loosen the lug nuts and change the flat tire. This is algorithmic thinking, where you define the rules and execute them in order. See how seamlessly you were able to solve a problem within a short period of time by putting on your computational thinking cap.

Why computational thinking?

Developing models to solve problems is a critical part of computational thinking. Computational thinking is driven by the fact that there are immense computing resources at our disposal than ever before which when used effectively can open up new frontiers of discoveries and inventions. Computational thinking is so hardwired to these computing resources that the power and type of the systems and its real-world constraints influence our thinking process. Any real-time system is constrained by the physical laws of nature and may not perform ideally. There are several aspects of the system we may take into consideration while devising a solution like the instruction set parameters, the environment where the system is deployed or its computing prowess. In a nutshell, computational thinking breaks down and abstracts a complex task that could be solved by a computer or a human or a mix of both these computing engines together through reduction, transformation, and modeling.  

Drive your business with computational thinking

The modern workplace is all about problem solving. It is the underlying aspect of every business which ultimately defines its success or failure. Computational thinking has become a necessary skill for the 21st century workforce and it enables them to deliver solutions in a smarter and more economical way. Gone are the days when the workforce had to work harder, now they need to think sharper, work smarter and deliver solutions faster. Equipping your workforce with computational thinking skills can help accelerate your productivity. We live in a world where everything is data-driven. Many jobs demand its employees to work their way through complex problems and break it down into forms that a computer can comprehend. For this, they need to think creatively from a computational perspective. This really helps the business, as now the workforce is working smarter and they are pushing solutions faster thereby saving time and money. The time spend on developing one solution reduces drastically and the resources can be re-allocated to the next within a shorter time span.

At Accubits, we take this process seriously and it helps us drive innovation at an astounding rate. The workforce here embraces and nurtures every essence of this thought process thus increasing productivity which is key. It doesn’t matter how hard or smart you work, all that matters are the results. A smart workforce gets the job done faster and better than a hardworking workforce. Computational thinking helps a workforce become smarter by helping them acquire the right skills and enables them to identify the right tools for any given task. To understand more about how we can help your business grow by increasing the productivity of the workforce through computationally advanced tools, visit us at www.accubits.com.