What is data validation?

Both data observability and data testing play an important role in achieving high data quality. But how do you actually get there? How does data observability and data testing actually work?

You can’t just eyeball your data to make sure it’s accurate and trustworthy. There’s just too many potential errors hidden away that you will never catch: unicode formatting, off-by-one errors, or even time-traveling transactions that happen in the future due to time zone issues. What you need is a little something called data validation. It checks your source data, ensuring it's in ship shape before you integrate it into your broader data collection.

With the right automation in place, you should have all the checks you need to make sure the right stuff gets in and the unwanted stuff stays out. There’s a few ways of going about it and making sure all the data that gets into your ecosystem is well-formatted and correct.

The role of data validation

The whole point of data validation is to prove to yourself and others that data from different sources is ready for your organization. This means the data follows your business rules, is consistent with your structures and models, and works in the expected context. When your data is validated, you know it’s consistent, complete, and correct so it won’t cause any issues downstream.

Like anything else in the world of data, there’s no right way of doing things, but there are a few ways that are better than others. We are always driven towards automating as much as possible in your data validation workflows. When done right, it’s an automated pipeline that keeps things running smoothly while also filtering out anything anomalous or problematic.

Source data validation (aka: “source to target” validation)

Source data validation involves verifying the quality, integrity, and consistency of data at its point of origin, such as a transactional database or external data source. This step is essential because data from different sources may vary in format, structure, or quality, and it's crucial to ensure that it meets the standards and requirements of the target system or database.

For example, transferring data from a source database (say a transactional database like Postgres) to an analytics warehouse like Snowflake can often lead to inevitable data quality issues (bugs in backend databases, broken ETL pipelines, etc.). The data from the source system needs to meet the standards and formats required by your target system, maintaining its accuracy and relevance throughout the journey. You can apply and enforce these standards manually or via automation.

We don’t recommend doing this work manually because you’re likely to encounter some significant challenges. Manually checking data is time-consuming, prone to human error, and gets much more difficult when dealing with large volumes of complex data. You don’t want small, hard-to-spot inconsistencies or anomalies to slip through and cause problems downstream. Even if you’re doing custom SQL checks, there’s no guarantee you’ll catch everything you need.

We absolutely recommend using automation for source data validation. When doing source to target validation, you can use automated cross-database diffs to catch any data shenanigans. You want to make sure you have parity between your source and target systems. Tools like Datafold and dbt have become quite popular for this reason. They come equipped with built-in data validation features (for Datafold, cross-database data diffing; for dbt, source-level data tests), that can test data as it is loaded into your target database. You get greater efficiency while minimizing the likelihood of errors you’d see with manual methods.

However, the journey of data validation doesn’t end with the transition from source to target. You also need to pay attention to how this data is validated as it integrates into your production environment. It’s here that the data’s true integrity and usefulness are put to the test. This is why we’re all about CI/CD checks and data diffing.

Your production data should always be correctly formatted, accurate, and dependable for real-time applications and decision-making. Automation is the best way to get there.

Streamlining data validation with automated tools

When considering different approaches to data validation, you should think about factors like efficiency, accuracy, and scalability. To give you a clearer picture of how manual checks, custom SQL checks, and automated tools like Datafold and dbt tests compare, let's look at a table that breaks down these methods across in a few key ways. Whether you’re doing ETL, ELT, or both, you want the best tools and methods for the job.

While manual checks and custom SQL queries have their place, automated tools like Datafold and dbt tests are much better for larger, more complex data environments. The efficiency, accuracy, and scalability provided by these automated methods make them a compelling choice for modern data workflows. 

Validating Data in production environments

Data validation in a production environment often presents unique challenges. It's here that data is actively used for decision-making, so its integrity and timeliness are important. Let’s explore some specific challenges and techniques that come into play.

• Real-Time Data Validation: In production environments, you need data to be accurate the moment it's used. Real-time validation is key in applications where immediate data accuracy is critical. Think about financial trading platforms or health monitoring systems – a slight delay or error can have significant consequences.
  

• Monitoring Data Quality in Live Systems: Keeping a constant eye on data quality is essential when it’s live. We set up systems that continuously check the data against quality metrics, alerting us to any issues. This ongoing monitoring helps us catch and fix problems early, ensuring that the data stays reliable.
  

• Ensuring Up-to-Date and Accurate Data: It’s not enough for data to be just accurate; it must also be current. In production, we ensure systems are in place for regular data updates and verifications, especially important in fast-changing fields.
  

• Incorporating Tools Like Datafold and dbt Tests: There are several tools that are effective in both pre-production and production. Datafold, for example, isn’t just for pre-production. In the production phase, it helps us perform data diffs, ensuring what’s in production matches what we expect. It’s great for cross-database validation, ensuring consistency across different systems.
  

Similarly, dbt tests aren’t just a one-off pre-launch task. We use them continuously to verify that our production databases meet the criteria we set during transformation. Automating these tests lets us routinely check the quality and accuracy of our data across all eight dimensions of data quality.

Data validation in various industries

Data validation isn't a one-size-fits-all process; it's tailored to meet the unique demands of different industries. Each industry molds its data validation practices to fit its specific context, but at the heart of it all is the universal goal of ensuring data accuracy and integrity. Here are some examples of how data validations impacts specific industries:

• Healthcare: Make sure patient IDs are correct so medication and dosage information can be correctly captured and implemented for patient care
• Finance: Verify the accuracy of customer data, transaction records, and compliance reports to safeguard against fraud, errors, financial discrepancies, and legal risks
• Retail: Correct and holistic customer data enables personalized product recommendations and customer service, bolstering loyalty programs and preventing issues like over- or under-stocking
• Education: Student enrollment data corresponds to course assignments and grades are accurately recorded and reported
• Manufacturing: Checks specifications of manufactured components against quality standards to prevent defects and ensure product safety

Data validation is super important for industries that face regulatory compliance and have strict data governance processes. Without automated data validation, it is difficult — if not impossible — to quantify whether a company is in or out of compliance.

Challenges in data validation

Tackling data validation can sometimes feel like navigating a minefield. Each step presents its own set of challenges. Some of the most significant issues in validation include:

• Volume of data: The sheer amount of data generated and collected by companies today can be overwhelming. Validating this data for accuracy and consistency becomes a huge task, especially when it needs to be done in real-time or near-real-time to support decision-making processes.
• Data complexity and diversity: Data comes in many forms — structured, semi-structured, and unstructured — from a multitude of sources. Ensuring this diverse data meets quality standards requires sophisticated validation techniques.
• Evolving data sources: As companies adopt new technologies and platforms, they encounter new data sources. Each source may have its own format and quality issues, requiring ongoing adjustments to data validation processes to accommodate these changes.
• Real-time data processing: With the rise of IoT devices and real-time analytics, companies often need to validate data as it arrives (e.g. live tracking of mail and packages). This requires highly efficient validation processes that can operate quickly without causing bottlenecks.
• Regulatory compliance and data privacy: GDPR, HIPAA, and CCPA impose strict rules on data handling and privacy with data validation process regulations, adding another layer of complexity to data management.
• Scalability: As companies grow, their data validation processes need to scale accordingly. Developing scalable validation processes that can adapt to increasing data volumes without significant resource investments is a challenge.
• Human error: Manual data entry and validation processes are prone to human error. Minimizing these errors while maintaining efficiency and accuracy in data validation is a constant struggle for many companies.

Dealing with these challenges requires a combination of advanced tech, skilled data engineers, and effective processes. Building a strategy around it is the source of many headaches 

Solidifying data integrity through continuous validation

Data integrity aims to keep your data in tip-top shape through its entire life - when you store it, get it back, or use it. It helps you follow rules, keeps users trusting you, and stops data from getting compromised or lost. Think of it as making sure your data doesn't get changed by accident or on purpose, so it stays complete and consistent. In the end, you want your data to match up everywhere it's stored. 

Continuously validating data is key to this goal. Here, the focus is on the correctness of your data. It's a subtle distinction. It may be tempting to think it resembles a simple spell check. However, it's more like a regular health check for your data. It involves constantly running checks to make sure your data hasn’t gone off track. It's especially important because data changes, gets updated, and shared all the time, and you don’t want small errors snowballing into big issues.

Data validation isn’t merely a quality check. It's about understanding where your data comes from, how it’s used, and what to do about it if it's wrong. In short, it's about being proactive. That way, you’re more prone to recognize when your data doesn't just look right. It's key to having your data stay valuable over time. 

Ultimately, data validation requires a commitment to maintain its integrity and value. It's not just about preserving data accuracy. It's about making sure your data is dependable so that it instills confidence in others who rely on it to make informed decisions.