# Detecting Data Quality Issues by Identifying Outliers

March 2020 | Dios Kurniawan

In my previous post on Data Quality Framework, I discussed about the concept of DQ (data quality) Test Point. The idea is to detect data quality issues by probing the data. In these DQ Test Points, DQ metrics are extracted, calculated and then compared against a baseline.

To bolster the DQ metrics, a method called outlier detection is added to the DQ Test Points. This is a pretty simple check to see abnormalities in the data by looking for outliers. Outliers are, as you must have guessed, values which are very different from most of the population. For example, if most of Telkomsel subscribers make 5-6 voice calls per day, then those who place 100 voice calls per day are clear outliers. Outliers can be legitimate as in the previous example, but they can also be invalid or unwanted data. For example a person whose age is 250 years is certainly an invalid data, something that we want to erase from our datasets.

Most outliers are hidden inside the dataset. There are almost always outliers, but when we see a lot of them in our tables, or a sudden change in the number of outliers, we can see it as a sign of a potential data quality issue. It is important to remove outliers because they generally have negative effect on analysis and training a predictive model.

To identify outliers, one of the most common methods is to calculate the distribution of the data using Inter-quartile Range (IQR). In the simplest words, IQR is the area of the data which represents the “middle” values where half (50%) of the data belongs. Anything that falls beyond a certain distance from the IQR will be marked as outliers.  According to “Tukey’s Rule” (named after John Tukey, an American mathematician), the distance is 1.5 x IQR (see diagram below).

To perform the computation, first we will have to sort all rows in the dataset from the lowest to the highest value, then divide the data into four equal parts (hence the name “quartile”). The boundary for the first 25% of the data is called Q1 and the last 25% is called Q3. To get the IQR, subtract Q3 from Q1. After that, find the “min” and “max” limits by calculating 1.5 x IQR from Q1 and Q3, respectively. Once you have these “min” and “max” numbers, you can start counting the outliers which values are less than “min” and more than “max”. There you get your outliers.

Finding IQR like above is a computationally heavy process, especially when involving large number of rows. Luckily, our friend Spark has provided us with the tool for this. Using approxQuantile() function which allows us to compute IQR without going through the whole dataset, and this can be done quite easily. You may want to see the snippet of the PySpark code as shown below:

To give an example of how it works, this script was run on one of the tables in the ABT schema, with around 5 million records. Outliers are gathered from all numerical columns. The script produced the following statistics:

As we can see, the percentage of outliers is generally small. Anything under 2% will be considered normal. However, in the above example, there are some particular columns which have large percentage of outliers, even up to 15%. This is something worth investigating as this can lead to potential quality problems.

What’s Next?

At the moment the IT Data Quality team is working on putting the outliers count in our BI DQ Dashboard (see below) alongside with other DQ metrics. By comparing the outliers statistics with historical data, we may be able to detect issues before they present a problem in the consumers side.

The method mentioned above is far from perfect. It only captures outliers in one dimension, which is called univariate outliers. To get outliers which lie in two or more dimensions, called multivariate outliers, different methods employing ML techniques must be prepared. Stay tuned for more posts on this matter. Our journey to improve data quality in BI keeps going on!