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Jun

03

2022

Eliminating Data Warehouse Errors

By: Brian Vinson

At Teknion, we’ve been working with a large worker’s compensation insurance agency that needed help migrating their systems from on-site infrastructure into the cloud. Interestingly, they’re also using one of our partner tools, Validatar, for data quality automation.


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The insurance agency has used Validatar to set up automated testing jobs every time data is deployed to production. Additionally, with the use of template testing across their databases, they are reproducing tests at scale while using automation to run tests on a nightly/hourly schedule as new data is added to their systems.  As a result, they have maintained data quality and have not experienced any production data errors in the last 120 days while using Validatar’s suite of tools.

So... why is data quality so important and how do you validate data quality? In this blog, we'll be exploring these questions in more detail. 

Why is high-quality data important?

For many businesses, data quality is not a chief priority or concern. Sometimes it is hard enough to keep the stream of data flowing, to even speak of running thorough and rigorous testing. And when this happens, it can have a devastating effect on the business's bottom line. So, with that in mind, let’s first look at why data quality is so important:

  • Decision-making ability. When you analyze your business data, you’ll gain valuable insights into your business processes, your profitability, and your business strategy. Based on these insights, you can make better business decisions. Conversely, if you have bad quality data, your decision-making abilities will be negatively impacted.
  • Efficiency. One of the main benefits of analyzing your business’s data is that it gives you insights that enable you to make your business processes more efficient, no matter if it's marketing, sales, or operations. Thus, with low-quality data, you won't be able to do this and it will lead to business inefficiencies. Moreover, many business processes rely on quality data, and low-quality data will cause these processes to bog down.

  • Reliability. Once low-quality data impacts your decision-making abilities and doesn't give you proper insights into your business processes, the result is that you can't trust these insights. Building up trust of data in your organization happens in small increments over time. Lost trust because of bad data can happen in one fell swoop and have lasting impacts. In other words, low-quality data leads to a lack of reliability in your data processing and analysis processes.

  • Missed opportunities. By analyzing your business’s and customers’ data, you’ll be able to identify trends and patterns in the market. These trends, in turn, can show you the opportunities you can capitalize on to generate more revenue. Therefore, when this data is not reliable, you could miss out on valuable opportunities in the market.

  • Loss of revenue. If you fail to capitalize on opportunities, it could lead to a loss of revenue. There are also many other cases where low-quality data could impact your revenue. This can include, for instance, incorrect customer data that could lead to marketing campaigns not reaching their audience.

How to VALIDATE DATA QUALITY

The concept of data quality was developed to help organizations ensure the reliability of their data. And you've now seen what effect it could have if your data quality falls short. Now the question is: How do you validate data quality in your data warehouse?

At Teknion, we use CUVCAT to validate data quality. This is an acronym for the necessities of quality data:

  • Completeness. It's crucial that your data is complete. If it's not, the insights you’ll get will be incomplete too, which could be misleading or damaging for your business. When you validate completeness, you’ll know that the data is available for your business.

  • Uniqueness. Data duplication is often the main reason why you have too much data. This causes a significant problem that could result in inaccurate reporting and analysis. For this reason, it's vital that you ensure your data is completely unique.

  • Validity. You’ll need to ensure that you establish a set of guidelines that govern how your data should be structured, its type and format, and which values it should have within certain constraints. Any data that comply with these guidelines will be valid. If it does not, it will be invalid and could result in errors in your data set.
  • Consistency. Data should be presented to users in a way that’s consistent and true without the structure of the original data changing. If there are inconsistencies it could result in compromised data integrity.
    Accuracy. Accuracy represents the extent to which an item is correctly represented in the context of the data as a whole. In other words, accuracy reflects that the data is right.


  • Timeliness. Timeliness refers to the ability to provide the right people with the right data at the right time. It’s important because the value of a dataset is determined, to some extent by how quickly the data can be ingested and available for use.

DATA QUALITY AUTOMATION

Validatar was developed to help you validate the necessities mentioned above and help you solve the challenges relating to providing trustworthy and quality data. To do this, the platform helps you with:

  • Data discovery. The tool helps you discover what data you have. And when you know what data you have available, you can use it in innovative new ways to gain deeper insights from it.

  • Data profiling. Validatar allows you to see how your data changes over time. It does this by tracking data profiles which, in turn, are used by data professionals to ensure that the data pipelines they build are robust and accurate. As a result, data consumers can trust the data they use for reporting and analytics.

  • Data testing. Validatar contains a test repository that you can use to create and store test cases. It also contains an execution engine to run those tests and a results repository that stores the test results for easy retrieval. It thus also allows you to create data quality rules, comparison tests, and regression tests that you can use to validate your data. You're also able to create jobs and schedule tests to run automatically as part of your modern data stack workflow. Ultimately, automated testing ensures data quality at all times without you spending a lot of time and resources to test it.

  • Data monitoring. Validatar’s monitoring feature notifies you of any significant changes in the profile of your data that could indicate an anomaly or potential issue. In this way, you'll be able to identify and solve data quality problems earlier.

Ultimately, Validatar is the all-in-one data quality platform that you need to ensure data quality and that you build trust in your data. To learn more about the platform and how it can help you, request a demo today.


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