Data QA Salaries and Rates in 2024

Data QA

What is Data Quality

A data quality analyst maintains an organisation’s data so that they can have confidence in the accuracy, completeness, consistency, trustworthiness, and availability of their data. DQA teams are in charge of conducting audits, defining the data quality standards, spotting outliers, and fixing the flaws, and play a key role at all stages in the data lifecycle. Without DQA work, strategic plans will fail, operations will go awry, customers will leave, and organisations will face substantial financial losses, as well as a lack of customer trust and potential legal repercussions due to poor-quality data.

This is a job that has changed as much as the hidden infrastructure that transforms data into insight and then powers the apps that we all use. I mean, it’s changed a lot.

Data Correctness/Validation

This is the largest stream of all the tasks. When we talk about data correctness, we should be asking: what does correctness mean to you, for this dataset? Because it would be different for every dataset and every organisation. The commonsense interpretation is that it must be what your end user (or business) wants from the dataset. Or what would be an expected result of the dataset.

We can obtain this just by asking questions, or else reading through the list of requirements. Here are some of the tests we might run, in this stream:

Finding Duplicates — nobody wants this in their data.

– Your data contains unique/distinct values in that column/field. Will the returned value be a unique/distinct value in that column/field?

– Any value that can be found in your data is returned.

Data with KPIs – If data has any columns we can sum, min or max on it’s called a key performance indicator. So basically any models which are mostly numeric/int column. eg: Budget, Revenue, Sales etc. If there is data comparison between two datasets then below tests applies:

– Comparing counts between two datasets — get the difference in count

– Compare the unique/distinct values and counts for columns – find out which values are not present in either of the datasets.

– Compare the KPIs between two datasets and get the percentage difference between them.

– Replace missing values – missing in any one of the datasets with primary or composite primary key. This can be done in a data source that does not have primary key too.

– Perform the metrics by segment for the individual column value — that can help you determine what might be going wrong if the count of values in the Zoopla-side doesn’t match the count on the Rightmove-side or if some of the values are missing. 

Data Freshness

This is an easy set. How do we know if the data is fresh?

An obvious indication here is to check if your dataset has a date column, in which case, you just check the max date. Another one is, when the data was pulled into a particular table, all of this can be converted into a very simple automated checks, which we might talk about in a later blog entry. 

Data Completeness

This could be an intermediate step in addition to data correctness, but how do we know to get there if the space of answers is complete?

To do this test, check if any column has all values null in it ­ perhaps that’s okay, but most of the time it’s bad news.

Another test would be one-valuedness: whether everywhere on the column all values are the same, probably in some cases that would be a fine result, but probably in other cases that would be something we’d rather look into.

What are Data Quality Tools and How are They Used?

Data quality tools are used to improve, or sometimes automate, many processes required to ensure that data stays fit for analytics, data science, and machine learning. For example, such tools enable teams to evaluate their existing data pipelines, identify bottlenecks in quality, and even automate many remediation steps. Examples of activities relating to guaranteeing data quality include data profiling, data lineage, and data cleansing. Data cleansing, data profiling, measurement, and visualization tools can be used by teams to ‘understand the shape and values of the data assets that have been acquired – and how they are being collected’. These tools will call outliers and mixed formats. In the data analytics pipeline, data profiling acts as a quality control gate. And each of these are data management chores.