Root cause analysis: what it is, what it is for and how does it work?
We tell you what root cause analysis is, how and when you can use it.
By María Juliana Rodríguez Urbano. Published: August 15, 2021.
Finding the solution to a problem or detecting a failure in a process is more complicated than we think. The cause may not always be apparent, or we may not detect errors in our line of thinking at first glance. It is in situations like these that we need proper tools for sorting through complicated tasks. One of them is root cause analysis, a problem-solving method used to identify the principal cause of the problem and prevent it from happening again.
Therefore, in Datasketch, we tell you what this method is, its benefits and how you can use it to solve any problem in your research processes. This method will facilitate data visualization in your future research.
A brief introduction to root cause analysis
The most generalized explanation of this method says that the best way to understand root cause analysis is to think about how we analyze and solve common problems. When we can’t remember where we left our house keys, we start to remember every activity we did before that moment and when we last saw them.
Root cause analysis works in the same way. This method allows you to determine what the root causes of a problem are. Root cause analysis also tells you how to fix them properly and prevent them from recurring. The main goal is to show what the specific fault is within a process.
By its very nature, root cause analysis is a reactive method. This means that, when faced with a problem, the study starts with a failure that has already occurred. After you implement it, in future processes or in that same process, it becomes a predictive method. It involves an iterative investigation, i.e., it is relevant to understand that you are not going to identify the root cause from the beginning. For this, you must continue investigating until you find a factor that implies an actual change in the process.
The exercise can be extensive, and it depends on the size of the problem and the number of causal factors you find. For your analysis to be successful, you must have a team to help you find them more quickly. Your team will also allow you to identify possible biases in your observation.
Practical methods for your root cause analysis
There are several techniques and methods for root cause analysis. The two most popular are the fishbone diagram and the five whys technique.
It is also known as the Ishikawa diagram. This method can help you visually identify the root cause by inviting you to follow a series of branching category paths. These paths can lead you to other related principles until you reach the root.
The diagram works as follows. First, you must place the problem in the center. This would be the fishbone. Then, you brainstorm to determine the different categories of causal factors and distribute them in branches coming out of the mainline, which would be the ribs of the fish. You group these categories and repeat the previous process until you reach the root cause. An illustrative example is this diagram of an office branch that is not working as it should.
The five whys
It is a simple method that consists of asking a " why" question and answering another “why” question. You do this to go deeper into the different factors found in the process and find the root cause. You only need five questions to find the root cause, but this is not a mandatory parameter. You may need to ask more than five questions, or you may need to ask fewer. The number of questions depends on the depth of the problem and the causal factors you want to visualize.
You can visualize your five “whys” in a simple way if you elaborate a scheme like this one, which seeks to find the root cause of why a customer doesn’t want to pay for a leaflet they ordered.
Root cause analysis is a practical data visualization tool that allows you to get to the bottom of a problem and understand what the root causes were. It also helps you identify what actions you should take (or not take) to prevent the problem from recurring and improve your analysis and investigation processes.
If you want to read more about data visualization, visit our blog and explore our tools.