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.

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