How to automate your data quality checks

No data engineer wants to spend their days staring at line after line of SQL while setting up data quality checks. Not only do they take up a huge amount of time, but they're seemingly never-ending, as there's always new data to monitor

Bogging them down in a network of manual checks isn't only demoralizing for them but also a poor use of resources. Your data team could be doing so much more value-adding work. Why not hand off the work that could be done by an automated tool?

Not only will automated data quality checks give your engineering team more time for high-impact work, but they're also more accurate and reliable than manual checks—leading to greater trust within your organization.

Here’s the rundown on automated data quality checks—including what they are, what to monitor, and how to set them up.

What are automated data quality checks?

Data quality checks are the techniques and processes used to make sure your data is accurate and reliable. Traditionally, these checks involve manual processes like writing SQL scripts or using dbt checks that enforce hard, predetermined rules. Or worse, simply spot-checking random datasets manually.

While manual checks can get the job done, they fall short in a couple of ways.

1. Creating and maintaining manual checks is difficult and time-consuming.
2. They're rigid and don’t take into account trends like seasonality.

Automated data quality checks, on the other hand, continuously scan your data to make sure it’s accurate, consistent, and up to standard—all without manual work.

Automated checks can run on a schedule or kick in when new data arrives, flagging everything from missing values to duplicates to format errors and more.

When deployed properly, automated data quality checks can seriously streamline your data processes.

Benefits of automated data quality checks

Automating your data quality checks will benefit your team in more ways than one. Whether it’s more reliable data, a more streamlined data quality process, or more time back for your team, the ROI of automated checks is easy to see.

Easier to create and maintain

Writing manual rules or running dbt tests is difficult. It’s also time-consuming and susceptible to human error. Automated testing tools, on the other hand, are far easier to set up and require very little maintenance.

Accounts for trends and seasonality

One of the downfalls of manual rules and testing is they don’t take trends or seasonality into account when monitoring your data.

If you worked in data for an e-commerce company, for example, and you had an `orders` table tracking your purchases, you could create a dbt test or a standalone SQL script to look for spikes in your table based on how many orders come in on average.

But what if, on your company's anniversary every year, you experience a huge influx of orders? Your manual rules wouldn’t know to expect a spike in rows, but an automated system using machine learning would.

Trained on your metadata, automated checks know when to expect ebbs and flows and change their thresholds accordingly. Plus, if you use Metaplane, you can further train your ML model by providing feedback every time you get an alert—letting it know if a sudden spike or drop is normal or if it is an anomaly

Faster time to detection

When you use an automated tool to run your data quality checks, you’ll receive a notification as soon as an issue occurs. That means you and your data team can get to the bottom of issues before hearing about them in an angry Slack message from an upset stakeholder.

Not only do real-time alerts help you detect issues faster, but the right tool will have column-level lineage, related pull requests, and change history to help you identify the root cause and resolve your issues quicker.

Better scalability

Relying on manual data quality checks means having to set up the same checks every time you have a new data set you need to monitor. This process is extremely tedious to scale, as it involves the same repetitive tasks over and over again.

Automation makes setting up your tests as easy as a few clicks. So, even as the number of tables you’re monitoring grows, there’s no increased workload on your team.

Improved data compliance

Regulations like GDPR and HIPAA have necessary standards for accuracy, completeness, and integrity that your data needs to meet in order to comply. Automating your data quality checks will help you feel a lot more confident that your data quality standards are being met.

Key data quality checks that organizations should implement

Data quality checks is a broad term. The reality is that “quality” is relative, and it all depends on what, specifically, your team needs out of your data.

With that said, here’s a list of specific data quality tests you can begin running to make sure your data fits the needs of your team.

Common types of data quality checks

• Completeness test: Checks to see if all the required fields within a set of data are populated—catching missing data or NULL values. For instance, a rule could mandate that every customer record has a phone number, email, and address.
• Consistency test: Checks to make sure that incoming data from different sources aligns and is consistent. For example, a consistency rule might check that a customer's unique ID matches between two systems.
• Accuracy test: Checks the accuracy of your data by referencing external resources. For example, a rule might require that an email address contains a valid format (e.g., name@example.com).
• Integrity test:  Checks that relationships within a data set are properly maintained. For example, a typical rule could verify that a foreign key in one table correctly references a primary key in another table.
• Validity test: Checks that data conforms to the required formats and types. For example, if a date should follow a particular format (e.g., MM-DD-YYYY), or whether a phone number should contain a certain number of digits.
• Deduplication test: Checks for and removes duplicate records within a dataset. For example, if you need to make sure each product ID in a dataset is unique.
• Timeliness test: Checks that data is up-to-date or within a valid time frame. For example, a rule could require that inventory records be updated within 24 hours.

How to implement automated data quality checks

Implementing automated data quality checks is far easier than setting up manual ones. Since you don’t need to write any code, most of the work revolves around knowing what you need to check and then syncing the right tool with your data stack to make it happen.

Step 1: Define your objectives

To get the most out of your data quality checks, it’s important to be as specific as possible about what problems you want to address, as this will better inform what checks you need to put in place on which tables.

For example, if a table informs a key dashboard that you know will be used in an upcoming board meeting, you should set up as many checks as possible to make sure that the data in that dashboard is accurate.

If there’s a table that lives way upstream, however, you probably don’t need to apply as many checks—just enough to make sure the data is fresh and there are no sudden anomalies.

Step 2: Establish data quality rules

Data quality rules are a set of criteria that your team develops to make sure your data is accurate and reliable.

These rules generally align with the different types of data quality checks listed above. For example, an e-commerce company might have a validity rule that mandates every new transaction entering its data warehouse has a product ID that matches an entry in its `products` table to ensure a transaction is valid.

Or, a marketing analytics team using Fivetran to pull data from their CRM to their data warehouse might implement a completeness rule that makes sure every lead coming from their CRM has a company name, contact name, and email address. That way, they know fake leads aren’t making it to their sales team.

Establishing your data must-haves early on will help inform what checks you need to put in place to maintain data quality.

For a deep dive into data quality rules and metrics, head here.

Step 3: Select an automation tool

Once you know what you want to get out of your data quality checks and what, exactly, you want to monitor, it’s time to select the tool for the job.

To do so, here’s a list of questions you should answer when evaluating a potential tool for automated data quality checks:

• Does it integrate with my existing data stack?
• Does it check what I need it to check?
• Can I train its machine learning model on my historical data to give more accurate dimensions?
• Will it give me alerts that are actually useful versus just adding to the noise?

To learn what it’s like to get started with Metaplane and how soon you can start getting alerts from your monitors, head here.

Step 4: Integrate automation into your data pipeline

Once you’ve selected your data quality tool, you’ll sync it with your data stack and set up the monitors that perform your data quality checks.

You can (and should) set up monitors throughout your entire data pipeline—from ingestion to transformation to final output—to make sure your data is consistent no matter where it’s being used.

Metaplane even provides suggestions for monitors to set up based on your data. Plus, you can always add your own custom SQL for your known issues.

Step 5: Set up alerts and reporting

Monitoring your tables is one thing—actually doing something about your data issues is another, and alerts will help you do just that.

Once your automated checks are set up, you’ll want to customize your alerts to notify the right people when a problem is detected—ideally, through a channel you know they’ll see it.

With Metaplane, for example, you can send alerts via Slack, Email, or Microsoft Teams, letting you notify your team through the channels they use most.

Step 6: Monitor and iterate

The beauty of automated data quality checks is that little maintenance is needed. Machine learning trains on your data and improves over time—adjusting thresholds and sending more accurate alerts as you go.

If you use Metaplane, you can even provide feedback to our model for each notification—letting it know if an issue is normal and expected or if it was, indeed, an anomaly.

Other than that, the only things you’ll need to look out for are:

• Are my data quality checks solving the problems I outlined?
• If not, are there additional monitors I need to put in place?

Best practices for implementing data quality automation

We’ve covered the steps to actually adding automated data quality checks to your data stack, but there are also a handful of best practices to keep in mind that will help you get even more out of this already powerful tool.

Focus on what matters to the business

Everything your data engineering team works on should be traced back to a specific use case that aligns with your business needs. Otherwise, data quality becomes a Sisyphean task where you constantly roll a boulder uphill, only to do it over again.

If you’re trying to track users across Facebook and LinkedIn, for example, and constantly finding duplicates, you should prioritize setting up a nullness or uniqueness monitor to make sure your data isn’t missing key values.

The data can always be cleaner, more consistent, and more timely, but establishing a baseline need and solving it will help your team deliver results more effectively.

Apply the right depth of automated data quality checks

When setting up data quality checks, it’s essential to apply the right level of monitoring based on each table’s importance and the potential impact of errors.

Applying the same level of checks to a less important table versus one that you know is critical to your data lifecycle doesn’t make sense. So, rather than applying blanket checks to all your tables, wait to understand what’s essential to monitor, then work from there.

Optimize signal-to-noise ratio

Not receiving alerts for your data quality issues is a problem, but receiving too many alerts is equally problematic. If your team gets bombarded with notifications that don’t matter, they’ll start tuning them out. This means they will inevitably miss something important, defeating the point of data quality checks altogether.

Applying the right depth to your checks helps with this. And if you’re using Metaplane, so does providing feedback to the platform’s ML model so that only high-impact alerts get through.

Establish ownership for when something goes wrong

When one of your data quality checks catches an issue and sends you an automated alert, the last thing you want is a group of people standing around like the Spiderman meme, waiting to see who’s going to go fix it.

Instead of waiting until something goes wrong to discuss who should fix it, assign those tasks ahead of time so that your team can resolve issues quickly—not just spot them.

Use data lineage for impact and root cause analysis

Once the right person has been alerted, you want them to be able to quickly see what’s wrong, determine the root cause, and find out what else has been affected downstream. For this, data lineage is your best friend.

A benefit of using an automated tool like Metaplane for your data quality checks is that data lineage is included in the platform. With a visual layout of your data flow, you can easily determine where issues started and what else has been affected. This allows you to proactively reach out to stakeholders and keep them in the loop when things go wrong.

This goes a long way in maintaining organizational trust in your data.

Automate your data quality checks with Metaplane

As your data practice scales, automated data quality checks are non-negotiable. Not only will they be more effective and less error-prone than manual checks, but they’re far easier to set up and scale—freeing your team up to focus on more important, higher-impact work.

With Metaplane, you can start monitoring your data and get end-to-end data observability in just a few minutes—all without writing any code. Give it a try for free, or talk to our team to learn more.

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