Original subtitles

Hello everyone and welcome to session 4 of PMGTH 23, Project Risk Management.

In this session, we will explore both qualitative and quantitative risk

techniques. There are two important tools for understanding and prioritizing

risks in your project.

This is part A of the session where we will mainly focus on qualitative risk

analysis. Let's get started together.

In this part of module, we will focus on qualitative risk analysis, a technique

that helps us evaluate and prioritize risk based on their likelihood and

potential impact.

It is not about exact numbers just yet, but more about understanding which risks

deserve more attention first.

Let's explore how this process works and why it is a key part of risk management

planning.

Okay, let's quickly review the seven key processes in project risk management

again. As you can see, we have already covered steps like planning for risk

management and identifying risk.

Now we are moving to step three, which is performing qualitative risk analysis,

where the goal is to assess and prioritize risk based on their

potential impact.

This step helps us decide which risks need a deeper look or a faster response.

Okay, let me start this section by asking a question.

Why should we use qualitative risk assessment?

Well, the answer is that not all risks are created equal.

Some are more urgent or dangerous than the others. For example, risks that

impact health and safety or those that give early warning signs often need more

immediate attention.

But what happens if we skip this step?

If we skip this step, teams may fall into two traps.

either ignoring important risks altogether or wasting resources by

every minor issue.

This is why making clear distinction between risks is not just helpful, it is

essential.

Let's take a moment to understand what we mean by qualitative data in the

context of risk analysis.

Qualitative risk analysis is all about using non -numerical data to evaluate

risks. Things like interview insights, direct observations, or even written

reports. Unlike quantitative data, which is counted or measured, qualitative

data describes risk in more subjective terms, like frequent, severe, or hard to

detect. For example, if I say I drink coffee every day, it is qualitative.

While if I say I drink 4 cups or 80 grams, it is quantitative.

So what is the key benefit here?

This process, I mean the qualitative process, it helps us focus on high

risks early without wasting time and effort across the project.

So far, we have learned that the goal of qualitative risk analysis isn't to

calculate exact numbers. Instead, it helps us understand risk through human

judgment.

First, we subjectively evaluate the likelihood and impact of each risk based

experience and perception of the project team or the stakeholders of the

project. Then we create a shorter list of key risks so we can make sure that we

don't waste time dealing with things that aren't truly critical.

And finally, in some cases, this process even helps us decide whether to move

forward with the project or not. What we call a go -no -go decision.

The keyword here is subjective.

And yes, that means emotions, intuition, and experience play a key role.

Here is a structured view of qualitative risk analysis process, straight from

the PMBOK guide. On the left, we see the inputs.

These include documents like the risk register, assumption log, and

register. These give us the raw material for our analysis.

In the middle, we can see tools and techniques that we can use to assess

These range from expert judgment and interviews to data analysis,

and visual tools like the probability and impact metrics.

And finally, on the right, we have the outputs, updated project documents such

as the risk register and risk report, which reflect our analysis results.

Remember, the goal of this process is not just to record risks, but to

understand them well enough to act wisely.

Let's take a closer look at the inputs we need for performing qualitative risk

analysis.

Usually, we begin with the project management plan, especially the risk

management component.

The information like rules, categories, thresholds, and matrices are here.

Then we have assumption log, which helps us track assumptions and constraints

that can affect how we prioritize risk.

The risk register is our master list of all identified risks.

And the stakeholder register tells us who might be impacted and who might help

us respond.

We also consider enterprise environmental factors, such as industry

expert reports.

Finally, organizational process assets can give us access to the lessons

and historical data from previous projects.

Altogether, this information can provide what we need to evaluate risks

effectively.

Now let's walk through the tools and techniques used in qualitative risk

analysis.

Usually we start with the expert judgment, which means using the

people who have seen similar risks before, perhaps in the previous

Just remember, different experts might have different backgrounds, so you

always prepare yourself to receive different ideas.

Then we have data gathering, like interviews to explore how likely a risk

how serious it might be.

In data analysis, we look at the quality of our data, estimate probability and

impact, and consider other factors like urgency or how easy the risk is to

control.

Interpersonal and team skills like facilitation help reduce bias and keep

discussion productive.

We also group risks through the risk categorization and visualize them using

tools like the probability and impact metrics and bubble charts.

We will discuss about them later.

And finally, risk workshops and team meetings are great for prioritizing and

assigning owners to each risk.

So what exactly do we mean by qualitative analysis of a risk?

Well, we are trying to understand the quality of the risk, that is, how it

affect the project's schedule, cost, and performance.

We start by estimating two key dimensions.

probability that is how likely is it to happen and impact that is how serious

would it be if it did then we multiply these two values to get what we call a

risk factor that helps us to build a probability impact matrix later the

this number the more attention that we need and remember risks with a higher

score often become candidates for quantitative analysis later on, where we

deeper using numbers and models.

This table shows how we can differentiate between major and minor

combination of probability and impact.

Along the top, we have different levels of impact, from insignificant to

catastrophic. And along the left side, we see probability levels, from rare to

almost certain.

Each cell shows resulting severity rating like high, moderate, or extreme.

For example, even a moderate impact can become serious concern if the

probability is high and vice versa.

This matrix helps us visualize which risks require urgent attention and which

ones can be monitored more likely.

Here is an example that brings qualitative risk analysis to life.

Imagine you are managing the construction of a five -story office

Kansas, and the team has identified several potential risks during early

planning.

Let's look at three of them.

First, there is a likely delay in obtaining municipal permits.

This could significantly affect the schedule, so it is rated extreme.

Second, there is a chance of equipment breakdown.

It is less likely, but still destructive.

So that one gets a high risk rating.

And finally, a nearby resident may file a noise complaint during construction.

This has low probability and minor impact, so we classify it as a moderate

Here is a detailed view of how we can apply the probability and impact matrix

specific project objectives.

Along the top, you will see probability levels that are low, medium, and high,

and are measured numerically.

Along the left, we evaluate impact across four dimensions, scope, time,

and quality.

For example, if there is a high chance of a time delay and the impact is 16 %

more, then that is a major concern.

Or if there is even a moderate chance of the quality of sleep age that would

make the product useless, then we need to act fast.

This table helps teams move from vague risk descriptions to specific measurable

definitions, so everyone can make sure that they are on the same page when they

are prioritizing risks.

Let's apply what we have learned to a real -world example using the

and impact matrix.

In this table, each risk has been evaluated based on its impact and

and we have calculated a significance score by multiplying them.

Take the first risk, that is, team not staffed in time.

With an impact score of 4 and a probability of 5, its significance is

You can see it mapped in the top right red zone, meaning that this is a

risk that requires immediate action.

Next are language misunderstandings and team not experienced.

Both score 15.

These are also high priority and sit close to the red boundary, meaning that

they still need careful planning.

Risks like available resources or testers not available with scores of 4

into the green or yellow zone, meaning they are less severe and might just need

monitoring.

And the matrix on the right visualizes this beautifully.

Larger red color bubbles mean higher risk exposure.

Another helpful tool in qualitative risk analysis is the Fishbone Diagram, also

known as the Cause and Effect Diagram.

It helps us visually explore the root causes of a risk.

In this example, the risk is late delivery placed at the head of the fish.

Then, each major bone represents a category, like people, machines,

materials, and we ask why repeatedly to dig deeper.

For instance, under people, the team may be untrained, over -allocated, or

juggling too many tasks.

Under machines, issues like equipment not working or not being available can

show up.

This diagram encourages critical thinking and teamwork because it's often

combination of small causes across different categories that lead to big

So when your team says, let's figure out why this risk might happen, this is a

great place to start.

After we complete qualitative risk analysis, it's time to make sure that

results are captured and acted upon.

First, we prioritize the risks.

As I mentioned before, not all risks are equally important, and this step helps

us focus on what matters most.

Next, we assign a risk owner for each high -priority risk. That makes it clear

who is responsible for monitoring or responding the risk if it happens.

And finally, we update our project documents, especially the risk register,

report, issue log, and assumption log.

These updates not only keep the team informed, but they also lay the

for quantitative risk analysis if that's the next step.

So please don't skip this part.

Documenting what you have learned is essential to making risk management

To wrap up this part of the session, here is the key takeaway.

If your team already has a good understanding of the key risks from

analysis, you may not need to go further.

You can jump straight into planning risk responses and assign owners.

But if there is still uncertainties or disagreements about certain high

risks, that's when we use quantitative techniques to dig deeper.

So qualitative risk analysis doesn't always lead to numbers, but it always

to clarity.

And that brings us to the end of part A of our session on qualitative risk

analysis. If you have any questions, feel free to reach out.

Thanks for your attention. And when you're ready, I'll see you in the next