The data team's most important asset is having data that can be relied upon. Decisions made by your organization will be based solely on guesswork or intuition if the data you share is untrustworthy. The validity of data before it is used for analytics and decision-making is checked by modern data teams using automated data quality testing systems. In this article, we will learn how you can conduct automated data quality testing in 2021.
Guide to Conduct Automated Data Quality Testing
Below are the points to conduct automated data quality testing:
Creating a Single Source of True Data
When it comes to data, the data team's primary role is to create a "single source of truth" for your organization's information. Raw data from various sources is fed into the warehouse to produce a single source of truth. The data team is then responsible for defining the transformation logic that combines the source data into usable datasets for subsequent reporting and analytics.
To be useful, the single source of truth must be accurate.
What is Bad Data?
As everyone who deals with data knows, data quality has several difficulties. These can enter your data pipeline at a variety of places, including:
- There may be problems with the apps that create the data in the first place.
- Your warehouse's data intake process may be sluggish.
- The data team's transformation code may have flaws or be outdated.
Inaccurate data can be found in the reports, dashboards, and analytics the data team produces during data quality testing. Due to this, there is a risk of bad judgments and a decrease in trust in the data team.
What is Data Quality Testing?
Assertions and unit tests are two sorts of tests that may be used when working with data. The term "assertion" refers to a runtime check to see if actual data matches a predetermined set of criteria. For instance:
- Revenue should never be in the red.
- There should be no blanks in the user id field
- A user can't have a signup date before the company's inception.
You may use unit tests to ensure that a given data transformation works as expected. Some input data is specified in unit tests and the intended outcome of the transformation. If the two don't match, the transformation code has a problem.
Assertions, rather than unit data quality testing, are the preferred technique of evaluating data quality.
Process of Creating Assertions
Selecting wrong data is the most typical approach to making statements about a dataset. The simplest way to show this is to provide an example:
Assume you're building a table called customers to house all the data you've acquired about your clients. An important column named "user-id" can never be NULL in the table. If it's empty, something's wrong. Using SQL, you'd express this in the form of a statement:
If the query returns zero rows, your test was successful. You may rest assured that your table adheres to the "one row for each client" requirement. Data quality concerns need to be addressed if rows return.
Automated Data Quality Testing & Notifications for Failures
Data quality issues cannot be resolved just by automated data quality testing. Following the definition of your data tests and their execution, any failing tests should be highlighted to your team as soon as they are discovered. When a test fails, you'll be told in one of two ways:
- An update to an existing transformation is being rolled out by a team member during development. All necessary tests should be run before the modifications are made "live.". If any of these tests fail, the new code probably has issues that the developer can correct immediately.
- Your pipelines are probably being updated frequently, possibly once an hour. The risk of a test failure due to incorrect data exists with every update. Having your employees alerted if anything like this occurs will allow you to respond quickly and minimize the damage to your organization.
Which Data to Test?
Assertions require writing time, and data teams are often limited in resources. However, writing tests today will save you time in the future when you're dealing with data quality concerns. In the following sections of your data pipeline, creating assertions will likely pay off the most in terms of time invested.
1. Flaky Source Data
Your source data is the building block of your data model. When it comes to operational databases, third-party service providers like Salesforce or Stripe, or even a spreadsheet held by your finance department, this is typically not controlled by the data team.
Your entire data model will be harmed if the input data is inaccurate or incomplete. Because of this, writing assertions that verify the accuracy and completeness of your data sources will warn you as soon as an issue occurs so that you can focus on fixing them.
Suggested Read: What is Database Normalization?
2. Complex Data
Only a few of the hundreds of changes in your data model will likely be very difficult. Make sure your claims are doing what you intend them to accomplish by spending some time writing them.
3. High Visibility Data
Several datasets are critical to the day-to-day operations of the majority of companies. If they include inaccurate information, the entire firm might be affected. Decide which datasets will have the most exposure and influence, and then construct as many assertions as possible for each of those datasets.
Data Quality Dimensions To Look At
An assertion may be a powerful tool for your data model if you know when to use it: But how do you make these claims?
Data quality has been the subject of much discussion; however, these five factors are often useful:
- Uniqueness: Your dataset should be checked to see any duplications.
- Validity: Is the data consistent with the business rules that are expected?
- Completeness: Does the dataset have all the information it should consist of?
- Timeliness: Is the data accessible when it is supposed to be?
- Consistency: Is the data consistent throughout the warehouse?
Most datasets' primary key, or grain, is a field (or group of fields) that is supposed to make them all unique. The assertion should look for rows with a duplicate primary key to verify uniqueness. It is one of the most important elements in the creation of automated data quality testing.
Most dataset fields will have a predefined format that you may expect to see. There are several fields that must be present at all times. A non-negative numerical number, for example, is something that you may already be familiar with. Therefore the validity of data is necessary for complete automated data quality assessment.
The goal of completeness assertions is to find any holes in your data. For example, an ETL system is commonly used to load data dumps into the warehouse daily. If a day's worth of data is missing, it might be a sign of a problem.
A thorough grasp of your data is essential to finding these gaps, and the resulting statements will differ appropriately.
To build your datasets, you'll need a system that imports data into your warehouse. Alerts will tell you if your data warehouse has ceased receiving new data.
Keeping the data in one table consistent with the data in another. It is possible to create assertions that do a comparison between two tables.
How Can Automated Data Quality Testing Tools Help?
To be successful, assertions must be tested often and warnings issued to the team in the event of a failure. An assertion is included in automated data quality tools, which helps data scientists to conduct proper tests. A few lines of SQLX code are needed to implement some of the tests in these tools.
When your datasets are updated as part of an automatic schedule, these assertions are run. An email or Slack message with an error message will be sent to you when a test fails, and you'll be able to see the specifics of the problem.
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There has never been a time when data teams have had so much responsibility in a business. When making major or minor business choices, there is no better source than the data team. As a result, the accuracy of the data is critical.
An automated data quality testing may alert you to errors in real-time throughout development. Additionally, it allows you and your team to focus on value-added work rather than responding to complaints of erroneous data. This avoids your business from making wrong judgments and frees up time for your team.
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