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What is computational thinking?

Computer scientists, mathematicians, and engineers need to approach problems in a logical and systematic way. This is called “computational thinking”, a problem-solving approach that primarily consists of four techniques:

1. Decomposition
2. Pattern Recognition
3. Abstraction
4. Algorithm

However, while developed by computer scientists, these techniques can help any person running a business to do it better. Let’s get started!

Technique #1: Decomposition

Decomposition is breaking down a problem into smaller more manageable parts. The problems we face in business on their face are very complex. But by breaking them down we can tackle simpler pieces one by one.

Example: Training customer service with ADDIE

Problem: A company’s customer service team is receiving numerous complaints from customers about slow response times and poor communication. The company wants to improve the customer service team’s skills by providing training that will help them respond more quickly and effectively to customer inquiries. Fortunately, there is already an established instructional design model called ADDIE that helps an instructional designer decompose the problem.

Decomposition Steps:

1. Analysis: Properly analyze the problem
   1. Identify which customer inquiries cause the most problems
   2. Examine the current process for responding to such problems, and uncover anything that contributes to slow response times
   3. Identify knowledge or skill gaps that contribute to poor communication
2. Design: Design the training program
   1. Developing learning objectives
   2. Select the  appropriate instructional strategies and methods (e.g., online modules, role-playing exercises, etc.)
   3. Designing assessments to measure learning outcomes
3. Development: Create the materials
   1. Develop videos, handouts, role-playing scenarios, and other instructional materials
   2. Develop the training modules
4. Implementation: Give the training
   1. Schedule the training
   2. Delivering the training (either in-person or online)
   3. Provide feedback to trainees
5. Evaluation: Determine the effectiveness of the training
   1. Conducting pre-assessment
   2. Gather feedback from trainees
   3. Conduct post-assessment
   4. Analyze the impact of the training on customer service metrics (such as response times and customer satisfaction scores).

By using ADDIE to break down the process of developing and delivering customer service training into smaller, more manageable steps, the instructional designer can create a more effective training program.

Technique #2: Pattern Recognition

Pattern recognition is identifying similarities and patterns in data. Before you begin using data, it’s important to make sure you have clean and valid data, understand the context of the data, and have identified and removed outliers. Once you’ve done that, you’re ready to solve your business problems.

Example: SaaS Email Marketing

Problem: A SaaS marketing team is sending out email campaigns to promote their product, but they are not getting the open rates they need to generate leads and sales. They want to use pattern recognition to identify factors that are impacting the open rates of their email campaigns.

Pattern recognition:

1. Analyze the subject lines of high-performing email campaigns:
   • Identify common themes and language that resonates with the target audience
   • Look for patterns in subject line length, use of emojis, capitalization, and other factors that may impact open rates
2. Analyze the content of high-performing email campaigns:
   • Look for patterns in the structure of the email, such as length, formatting, and use of images and other media
   • Identify common themes and language that resonates with the target audience
3. Analyze the timing of high-performing email campaigns:
   • Look for patterns in the timing of email sends, such as time of day, day of the week, and frequency of sends
   • Identify patterns in the behavior of the target audience, such as when they are most likely to be checking their email and engaging with marketing messages

By using pattern recognition, the marketing team can identify factors that are impacting the open rates of their email campaigns and develop targeted solutions to improve performance. For example, they may find that including a specific keyword in the subject line of their emails consistently results in higher open rates, or that sending emails at a certain time of day leads to better engagement. This approach allows them to leverage the data they already have to optimize their email campaigns and achieve their marketing goals.

Technique #3: Abstraction

Abstraction filters out irrelevant details, focusing on the factors that are most relevant to the problem. If you’ve ever heard “The map is not the territory”, this is a phrase that illustrates the concept of abstraction. The purpose of a map is not to replicate all the details of an area precisely and comprehensively. Instead, it will only point out the details that are relevant to the purpose of the person reading the map. By not including all the details, the map is more useful and it will be easier for the person to achieve their goals.

Example: Electrical wiring for construction

Problem: A construction team needs to develop a plan for the electrical wiring of a new building.

Abstraction:

1. Create a high-level electrical wiring diagram that shows the overall layout of the electrical system in the building, including the main power source, distribution panels, and major electrical components.
2. Break down the diagram into smaller, more detailed diagrams that show the electrical components and connections in each room of the building.
3. Create a list of all the electrical components that will be needed for the building, including light fixtures, outlets, switches, and circuit breakers.
4. Develop a standard set of procedures for installing each type of electrical component, including safety protocols, wiring diagrams, and testing procedures.

By using abstraction, the construction team can develop a plan for the electrical wiring of the building that is organized, efficient, and scalable. The high-level electrical wiring diagram provides an overview of the entire system, allowing the team to identify potential issues or conflicts before they become problems. Breaking down the diagram into smaller, more detailed diagrams helps the team to focus on the specific wiring needs of each room. Creating a list of all the necessary electrical components and developing a standard set of procedures for installation ensures that the team has everything they need to complete the job efficiently and effectively.

Technique #4: Algorithm

For our purposes, an algorithm simply means to create a step-by-step plan for solving the problem.

Example:

Problem: A compliance officer needs to ensure that all employees in the organization are completing mandatory compliance training courses.

Algorithm:

1. Collect a list of all employees who are required to complete the mandatory compliance training.
2. Create a schedule for when each employee should complete the training based on their role, location, and other factors.
3. Set up a system to track employee progress on completing the training, including deadlines and reminders.
4. Develop a system for identifying and addressing any issues that arise during the training process, such as technical difficulties or employee questions.
5. Monitor the completion status of all employees.
6. Identify any individuals or departments that are falling behind.
7. Develop a plan to address any gaps in completion.
8. Ensure that all employees are fully trained within the required timeframe.

By using the algorithm technique of computational thinking, the compliance officer can develop a step-by-step process for ensuring that all employees in the organization are completing mandatory compliance training.

I think it’s fun to think that when Colin Chapman, designer of the Lotus 25, said “Simplify, then add lightness” it was a nod to computational thinking. Abstraction, decomposition, and pattern recognition are methods of simplification, and algorithm could probably be used to “add lightness”. I hope this article helped you understand the role computational thinking can apply to solve a variety of business problems. If you want to be notified of the next posts in this series, please subscribe for updates! 

Interested in learning more?

I learned about these concepts in the Coursera course Computational Thinking for Problem Solving by the University of Pennsylvania.