Experian Pandora’s defect monitoring and resolution features allow you to have round-the-clock control over the quality of your data. This is especially critical for organizations who are embarking on data governance or data management initiatives.
Experian Pandora can help monitor and manage your data over time with automatic alerts, audit trails, issue resolution, financial impact analysis, data policy enforcement, and more.
The video below is a brief demonstration of Experian Pandora’s monitoring and resolution capabilities.
Hi, I’m Rishi Patel, Strategic Technical Manager at Experian Data Quality. For this video, I’m going to take you through the data monitoring capabilities of Experian Pandora. I’m going to show you how easy it is to apply data quality rules, add business measures to them, and track them over time.
As you can see, I’ve already created a few business rules and have some scores against them. At any point I can edit those rules and change the thresholds against them. Here we’re fairly strict, so I can loosen that. Identify which records will fail, which will be marked as amber, or will pass. At any point, I can also edit any of the existing business transformations.
Creating new rules is very straightforward. You can add a new rule over here. In this example, I want to check for valid zip code formats within my data. We have full access to all the functions available within Pandora, or any that you create. For this example, I want to make sure that zip codes match the expression or the pattern of a valid US zip code format. And rather than looking across my entire data set, I can create filters and limit them only to a subset; in this example, USA only. The column that I want to check will be the postcode, and the expression that I want to search against will be USA zip code. Creating that rule will create a score, and we can interactively see which rows failed to highlight those data quality issues.
One thing to note, however, is the scores are identifying here are the count of the values or rows that failed or passed within the data set. But that doesn’t give us any real context around how valuable those effective customers are and what they mean to my business.
Let’s look at the example of email addresses. Having a look at the dashboard we can see that there are rows that have failed, but we don’t have any context into how valuable those customers are. However, within Experian Pandora, we can add that context. Editing this rule and this table, we can put a measure against these rules. In our case, we do have the number of sales that were made over the course of a year. If I go save that and look at our rules, we can see that the overall data quality score has dropped from a 99% to a 97%. Looking at this particular example and the failed rows, we can see that there are some very high value customers that don’t have a valid email address. With this information, we can prioritize which elements of our data we want to fix first.
Another important feature is better understanding how our data quality is changing over time. Looking at our email example, we can view those changes every time data is loaded into Pandora and whether the data quality is increasing or decreasing over time.
That was a brief demonstration, but I hope it gives you a good idea of how Experian Pandora can help you monitor your data quality.
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