Subtitles of the Movie

In this movie we'll cover the risk data quality assessment tool and technique used in connecting qualitative risk analysis. This tool and technique focuses on making sure that the information we use to perform risk analysis is unbiased and credible. The risk management team will ask question such as is the data credible? Is the data used of high quality? Is the data and information accurate and is the risk itself understood? Let's look at a basic example going back to our substitute grass project that was identified in the previous movie and also our identified risk within that project. Let's say that you identified the risk relating to the vendor running out of the grass product as a result of reading an article from a magazine. The article was based on the subjective views and predictions of the author. Would you consider this to be a credible source of information? Now, let's say that we looked at preliminary vendor production reports and that we have used this vendor in many past projects, allowing for a comparison of the production levels. The information here seems more credible in identifying this risk, particularly since based on the information we had assigned this risk an overall high score. So now what happens if the information our data used for risk analysis is deemed unreliable? Or we answer no to any of the questions posed earlier in this movie? In this case, the risk management team has gathered data and information of higher quality. We look at changing the no answers to the set of questions that we looked at earlier to a yes. Well, clearly it's not an easy thing to do. There could be a short of information. You may have all the information that you can gather at this point in time and so forth. If this is the case, then these factors will influence risk management and perhaps this leads to a dead end because the lack of information itself becomes a risk, which brings the level of uncertainty of the project's fate to a higher point. We would evaluate this in terms of what we reviewed in Part 1 of this course series using stakeholder risk tolerance levels and the risk tolerance levels of the organization. We would also determine whether the benefits of the project outweigh the uncertainty and whether the organization can afford the loss of failure. If we proceed to conduct risk analysis using poor quality data, data or information that is not considered reliable or accurate, then the results of our qualitative risk analysis efforts may be nearly useless. If we can not trust our information, how can we trust our findings? My purpose was not to digress with these last comments, but to make you aware of how important the quality of the information we use is. As I described, it can lead to a host of other questions and uncertainties. This is not to say that the information data that you used will always come with a confidence level of 100 percent. In several cases that may be impossible but the confidence level of the information that you use should be to a certain degree that makes the analysis and risk management efforts effective. And that covers everything that you need to know at this point about risk data quality assessment. Remember that this is performed during qualitative risk analysis and that it's important to the usefulness of the process' results.