Risk qualitative analysis. How much complicated ?

When it comes the time for risk qualitative analiysis, project manager and his team are required to express qualitative estimations of some risk attributes, tipically probability and impact, by means of subjective values or judgment.

These estimations are mainly based on project insight achieved by team members until that moment and some kind of a priori knowledge about the nature of the risks themselves.

Obviously the more these informations are reliable, the more this subjective qualitative analysis is trustworthy.

In the next of this post I will give some little tips that I think could ease this process.

Keep it simple

The objective is to have a simple and effective way to establish which risks worth a more specific quantitative analysis and which must be simply checked from time to time, rendering this information in a way that almost everyone can understand in a glimpse of an eye. 

So use simple indicators as numbers, letters or definitions. Don't use long wordy descriptions. These can find more suitable space in the risk description documents.

Figure 1. Simple is better...and understandable even after months.

Few Attributes

Two attributes are best. 

People normally find easy taking decisions based on two variables charts, conversely can be dismayed coping with multi-dimensional space. 

In figure 2 are represented a 2D scatterogram on the left  and a 4D scatterogram (colors are the fourth dimension) on the right. As we can see the scatterogram on the right is plenty of information but it is very difficult to understand. Conversely it is straightforward taking decisions based on the 2D graph.

I have seen indicator systems so complex and so difficult to read, that even the team who created them loosed the grip on their meaning after less than one month.

Probability and Impact are everything you need to know in this stage. 

Figure 2. On the left a 2D scatterogram. On the right a 4D scatterogram. The 4D graph is far richer of informations but it is difficult to understand.

Linearly spaced values

Logarithmic scales, Modified Fibonacci series, Repfigit numbers...well...are we still talking about project management or did we switch to astrophysics ? 

Methods mentioned above are surely useful in many ways but are also very difficult to familiarize with.

I think that for simple qualitative analysis nothing can be better than a plain old linear sequence.

Figure 3. Everyone can easily manage linearly spaced values.

Even integer numbers

Use even integer numbers every time you can. 

Decimal number are a useless complication. 1 to 10 in 0.1 step have the same meaning than 1 to 100 with step 1.

If you use odd numbers the mean is among possible values and this tend to polarize the estimation, you will end up using 3 more than the dued if you values are between 1 and 5. Guaranteed.

Take a look at Figure 4. 

I guess your attention has been dragged immediately to the middle, right ?

Now take a look at figure 5.

See how different is your perception ?

Using integer numbers you are every time compelled to make a choice. Under the mean or above the mean. No neutrality.

Figure 4. What are you looking at ? The one in the middle ? 

Figure 5. And now ? What are you looking at ? 

Few values

What is the point in having a scale that goes from 1 to 10 if people use 90% of the time values between 4 and 8 ? Too much values can be confusing. 

It is sometimes difficult to perceive and assess effectively an attribute value on a long scale based on subjective considerations only. Especially if the team is not accustomed to it or not extremely experienced.

In figure 6 we can see a 10 values bar chart on the left and a 4 values bar chart on the right. What is the difference between 7 and 8 or 8 and 9 ? Are you sure you can state it just basing your judgment on subjective data only ? Which graph is more intelligible ?

Two values are too little, four are best, six are even better if the team is experienced, eight are definitely too much. 

The only exception is if you intend to use values assigned to risk attributes during qualitative analysis to infer a suitable probability density function to be used in quantitative analysis, as I stated in my 12 November 2012 post A structured approach to qualitative risk analysis.
In this case you do need to use many values in your evaluation scale.

Figure 6. On the left a 10 values bar chart. On the right a 4 values bar chart. Which one is more intelligible ? What is the real difference between 7 and 8 and 9 ?

Standardize the values

2 is a number. It doesn't have a meaning by itself. You have to provide that meaning.

For example :

1. Risk impact less than 200000 USD
2. Risk impact between 200000 USD and 400000 USD
3. Risk impact between 400000 USD and 600000 USD
4. Risk impact above 600000 USD

Best is when the standardization is at a corporation level, so that every project manager and team member could immediately understand what is at stake.

Judgment or numbers ?

Sometimes number can intimidate people that could feel more at ease using judgment as Very low, Low, High, Very high. 
Still it is necessary  a standardization at a corporation level.

...In the end 

My point is that using a sequence like 1, 2, 3, 4 or 1, 2, 3, 4, 5, 6 should be best for risk qualitative analysis in most projects in most organizations and that you shouldn't consider more than two risk attributes at this stage. Any values you decide to use must be provided with a clear, unmistakable and standardized meaning at corporation level. 

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