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Experian Pandora

Data profiling and data discovery

Experian Pandora’s proactive data profiling and data discovery capabilities help you understand what problems exist within your data and identify what actions need to be taken in order to remedy the data issues.

Our profiling and discovery solution allows business and IT users alike to instantly browse and interrogate data, as well as view more than 240+ metadata attributes as soon as data is loaded into the tool. Users can immediately identify relationships, outliers, or format distribution across disparate systems that need further investigation.

The video below is a brief demonstration of Experian Pandora’s profiling and analysis features. 

Hi, I’m Rishi Patel, Strategic Technical Manager at Experian Data Quality. For this video, I’m going to take you through the data profiling and analysis features of Experian Pandora. We’ve already loaded some data into the repository, which is part of the tool, and when that load occurs, the tool automatically analyzes the data as its being loaded. What we are able to do is look at the results of this analysis. We’re going to have a look at the column view, and we simply get access to a whole bunch of metadata such as uniqueness, completeness, null values, and data types. We can also see how many formats occur for each of these values and this is a great starting point to drill into more information about this data.

 

Looking at the first column where we have Customer ID we can see that it’s fully populated and there are no null values, but it’s not entirely unique, which we might expect for a Customer ID. I’m able to drill into the actual values underlying this data interactively, and here I’m only interested in the values that occur more than once. I can quickly filter out the ones that I don’t want, and here I can see some of the values that occur in two or more rows. At any point I can select the ones I’m interested in and actually locate the underlying data. At any point in this process I can save or export this data. So what we’re able to do is go straight from the high-level statistics to the underlying information about each of those rows.

 

As another example, we can have a look at order dates. In this example, we can see that there are a number of null values here. We can right click on there and look at the null values themselves, instantly seeing where null values occur. We can take this a step further, and look at the date formats, and see that there outliers over here. We have the value dates of numeric and we can see that there are some outlying values where the date format doesn’t conform to an appropriate date. This format analysis can be performed on any of the data stores.

 

If we look at another example, we can analyze the postcode field. We can click on here and have a look at the formats. We can see that there are 42 different groups of formats available for the postcode field. Lets say we want to drill into something specific too, like a different country. We can look at the rows, navigate to the different countries that we have available, and profile that information instantly. The most dominant type over here is based in the United Kingdom. If we double click on that it will bring us to those values. Now at this point, we can then profile that information such as the postcode. We can see for the United Kingdom, we have seven different groups—some of which look accurate, and some of which that have data quality issues.


That was a brief demonstration, but I hope it gives you a good idea of the profiling and analytical capabilities of Experian Pandora. 

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