Section Summary – Qualitative Risk Analysis

In the previous few chapters in this section, we took a detailed look at what qualitative risk analysis is, the inputs that are required to perform this analysis and the tools & techniques that we will be using during this analysis. Let us now quickly summarize what we have learnt in this section so far:

• The purpose of qualitative risk analysis process is to prioritize risks in order to determine which risks require additional analysis. This helps the risk management team to focus on the higher priority risks.
• There are 4 inputs to this process:

o Risk Register
o Risk Management Plan
o Project Scope Statement &
o Organizational Process Assets

• There are a total of 6 tools & techniques that we learnt in this section. They are:

1. Risk Probability & Impact Assessment
2. Probability & Impact Matrix
3. Risk Data Quality Assessment
4. Risk Categorization
5. Risk Urgency Assessment
6. Expert Judgment

• Risk Probability & Impact Assessment provides us with the initial risk rating for each of the risks that we have identified so far. To arrive at this risk rating, we will be using the definition of risk probability and impact that we defined when the Risk Management Plan was created
• The Risk Probability & Impact Matrix is used to assign a risk score to our risk and categorize it as “High-Medium-Low” priority
• Risk Data quality Assessment focuses on making sure that the information we are using to perform the risk analysis activities is unbiased and credible. This is because; conducting risk analysis using poor quality data may result in results that are useless. Frankly speaking, if we cannot trust our data or information, how can we trust the findings that were made based on that data or information?
• The idea of Risk Categorization is to uncover areas of risk concentration so that we can create effective responses to handle them. This is because; dealing with sources of risks is easier and cost effective than dealing with each risk individually. In fact, it can have a greater level of effectiveness as well
• The purpose of this risk urgency assessment technique is to identify near term risks. We are trying to determine which risks are to be considered urgent. In other words, we are trying to identify those risks that require our immediate attention
• Expert Judgment refers to the decisions or suggestions given by knowledgeable experts during the various activities in qualitative risk analysis. The individuals who provide us with their expert judgment are called as “Experts”

By now you should have a very good idea and understanding of the Qualitative Risk Analysis process. To wrap up this section you need to:
• Remember what tools are used in this process
• Remember what each tool does and produces
• Understand that not all tools are used in each process

Trivia:

Are you wondering that I haven’t touched upon the topic of what is the output of this whole Qualitative Risk Analysis process? If you did then you deserve a big pat on the back. Every process creates some sort of output and our Qualitative risk analysis is no different. But, I haven’t covered it in this section because; the whole of the next section is going to be dedicated just to cover that.

If you did not think about the output of this process, no worries. Just brush up your PMBOK basics and re-read some of the initial chapters to refresh your memory and you will be on your way to being a Risk Management Professional.

Prev: Expert Judgment

Next: Updates to Risk Register after Qualitative Analysis

Risk Data Quality Assessment

In the previous two chapters we had learnt a couple of the tools and techniques used in qualitative risk analysis, namely Risk Probability & Impact Assessment and Probability & Impact Matrix. The next item in the list of tools is “Risk Data Quality Assessment” which is going to be the topic of discussion in this chapter.

Risk Data Quality Assessment

This tool focuses on making sure that the information we are using to perform the risk analysis activities is unbiased and credible. This is because; conducting risk analysis using poor quality data may result in results that are useless. Frankly speaking, if we cannot trust our data or information, how can we trust the findings that were made based on that data or information?

During this step, the Risk Management team will ask questions like:

1. Is the data credible?
2. Is the data used of high quality?
3. Is the data and/or information accurate?
4. Is the risk itself understood properly?

Let’s go back to the formula one race track example we covered in the previous chapter and the risk that we identified “TAR Supply”. Let us say you read a blog article about race tracks by some author on the internet that gave you some pointers about TAR supply. You also read an article in a reputed newspaper about shortage of TAR for high-quality road laying work. Now, which source of information would you consider credible?

A reputed newspaper or some random blog on the internet? I guess by now you have gotten an idea of what data credibility means.

What would happen if the information we are using is not credible or accurate or reliable?

The answer is very simple “Our Analysis will be incorrect”.

In cases where there is not enough information or when the answer to either of the questions above is a “No”, the project team will have to go back and gather additional data and information in order to make the answer to the above questions to “Yes”. Lack of information in itself is an uncertainty which can lead to risks. So, the team has to ensure that they have all the data they need in order to conduct an efficient risk analysis.

In some cases, the cost or effort that needs to be spent in order to fix data quality issues could be far too much if we compare it with the impact the risk could have if it materializes. In such cases, the risk management team could evaluate the benefits versus the cost of uncertainty and take a judgment call. Only in those cases where the benefits outweigh the cost will we take up additional effort.

Before we wrap up this chapter, let me tell you that having 100% certainty in data quality is not practically possible in many cases. In such cases, we must at least have a good degree of confidence or certainty on the data we are using in order to be confident over the fact that our analysis and its outcome will not be useless. This confidence is something that comes with experience and can’t be learned overnight.

Prev: Probability and Impact Matrix

Next: Risk Categorization