Irrespective of your data science vocational expertise, when data scientists met a new hitch or obstacle, then they were required to step back and understand the root cause which led to the emergence of such a technological hitch. Through this practice, data scientists can view the bigger picture and eradicate the problem without investing lots of time and effort. It is an effective methodology. Well, it is fixed that data scientists working professionals accompanied by distinct experience might utilize different methods to solve a similar problem. After completed several mega projects aligned in a row, aspirants build their understanding of the core concepts. Because as a fresher they have to indulge in problem-solving tasks.
But whether you are a novice or pro in this field and you might be looking forward to some more new tactics to solve the problem effectively. If you are one of them, then this article is crafted for you only. This article will focus on discrete structured thinking tactics that can guide you to eradicate the problems in the data science field or actual life.
Structured thinking is defined as the problem-solving set of steps and tactics that work by breaking large problems into minute ones to conquer major obstacles instantly and more effectively.
Structured Thinking In A Nutshell?
Structured thinking is an outline that focuses on resolving unstructured hitches which data science problems embody. With the support and utilization of structured methodology an individual can effectively and efficiently solve any problem further, it catches the hitches or parts which require extra attention. You can understand the structured thinking approach as the map of the entire new city where you are traveling.
Without a map, you will be unable to find out the destination. After reaching distinction also feel unsatisfied because of the efforts you invested unnecessarily. This statement simply highlights the significance of maps so, in context to data sciences, a structured thinking outline is pivotal. Well, this approach is not fixed because of its flexibility, and as per the urge, it can be modified to solve any problem. This article will cover 5 structured thinking techniques which can be used in the next data science projects by a candidate.
Six-Step Problem Solving Model
We will begin our list of tactics with the most simplified and basic 6-step problem-solving model. This tactic relies on the analytical process to eradicate any offered hitch. The name itself implies that it encompasses 6 steps to resolve issues, which are:
- Defining problem clearly and concisely
- Study the main force behind the emergence of the problem
- Conceptualizing techniques to solve obstacles.
- Examine and select the most suitable solutions.
- Execute the solution.
- Analysis and determine the outcomes.
This structure follows the inclination or mindset of constant betterment and creation. If you don’t get desired results at level 6 then you can start again from the 4th level where you get an opportunity to select an alternate solution. This is the easiest method because you can rectify and alter things based on a particular problem.
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The Drill-Down Technique
Our next tactic is the drill-down technique which is considered the best method to eradicate rigid and multifaceted problems on which multiple people will be toiling. The main aim behind the use of this technique is to break hitch into smaller chunks to search best solutions. To utilize this tactic, an individual should create an eloquent table.
The first section of the table must incorporate a defined problem accompanied by the second column where factors are written. Last but not the least, in the third column an individual must represent the cause of the second column’s contents. And then you will drill down the root cause to reach the main cause which led to the glitch. After reaching the problem you can create a solution to fetch a big fish.
Eight Discipline of Problem Solving
The third main solution can be eight disciplines of problem-solving that provide a practical strategy to resolve any issue. It is an extended version of 6 steps problem-solving model. Each step is crafted in a well-articulated manner to support an individual to reach the problem. Here are the 7 steps which should be followed while implementing this method.
- Work as a team together
- Understand the Problem
- Create a working plan
- Select and verify permanent rectification
- Execute action plan
- Evaluate outcomes
- Congratulate your colleagues
The Cynefin Framework
This technique is similar to most of the above-mentioned methods. In the cynefin framework, data scientists approach the problem based on 5 distinct inclinations.
- Obvious Context
- Complicated Contexts
- Complex Contexts
- Chaotic Contexts
- Disorder
The 5 Why’s Technique
Our last tactic is known as 5 why’s a technique or it can be perceived as the well-versed natural method to resolve any problem. This methodology just follows a simple rule, where an individual must ask why 5 times in a row to understand the problem, its occurrence, and its solution. Other than that, the subject can rely on this method to extract out root cause also.
