Most people have purchased something used at one point or another. If you’ve ever bought something used online, you’ve likely seen the product described based on its quality: Like New, Very Good, Good, Acceptable, Fair, or (worst of all) Poor. Look familiar?

While it depends on the product, most shoppers tend to stray far away from any products labeled Poor, Fair, Acceptable, or even Good condition. You want something you’re using to be the best possible quality. Data is no different. 

When it comes to data quality, data teams want to ensure that their data is the highest quality it can be, and that means it needs to score the highest within the six dimensions of data quality: accuracy, completeness, uniqueness, timeliness, validity, and integrity. 

Ignoring these aspects of data quality will result in reduced data reliability and trust – and increased data downtime downstream.

So, how can you assure your data quality is in tip-top shape? Let’s dive into the components of data quality assurance and best practices.

What is Data Quality Assurance?

Data quality assurance (DQA) is a systematic approach to ensuring your data is accurate, fresh, up-to-date and reliable. 

To make sure your data meets these standards, data teams leverage a data quality assurance framework that follows the six dimensions of data quality:

1. Data Accuracy: The degree to which data correctly represents the real-world events or objects it is intended to describe.
2. Data Completeness: Describes whether the data you’ve collected reasonably covers the full scope of the question you’re trying to answer, and if there are any gaps, missing values, or biases introduced that will impact your results.
3. Data Uniqueness: Ensures that there is no duplicate data that’s been copied and shared into another data record in your database.
4. Data Timeliness: The degree to which data is up-to-date and available at the required time for its intended use.
5. Data Validity: How well does data meet certain criteria, often evolving from analysis of prior data as relationships and issues are revealed.
6. Data Integrity: The accuracy and consistency of data over its lifecycle.

Measuring your data quality across these dimensions enables teams to operationalize their data quality management process and simplify their data quality assurance. 

For any data quality assurance framework, it’s also important to consider your data governance workflow, including the roles, responsibilities, processes, and tools your team uses to manage your data, data quality, and data movement.  

How to Do Quality Assurance on Data

There are several key steps in a data quality assurance workflow that are important to complete. These include data profiling, auditing, cleansing, enrichment, monitoring, and maintenance. 

Data profiling and auditing

Auditing and profiling your data can help your team to identify issues in the data that needs to be addressed, like data that’s out-of-date, missing, or simply incorrect in any way. It can also help to surface issues in your pipeline. 

Data cleansing and enrichment

Once you’ve profiled and audited your data, the next step in a data quality assurance process is cleansing and enriching the data to correct the errors that have surfaced and enhancing the data for use. That can mean removing duplicates, cleaning up formatting inconsistencies, and more.

Data monitoring and maintenance

Data quality assurance is an ongoing process, and that’s why building an effective data quality management strategy for continued monitoring and maintenance is essential. 

For most enterprise data teams, manual data quality management isn’t a sustainable practice for effectively maintaining data quality. That’s why tools like data and AI observability are so important – they automate the data quality management process so data teams can spend less time in the weeds and more time driving insights. 

Best Practices for Data Quality Assurance

While data quality assurance can often look different for each data organization based on data products, data usage, data access, and more, there are several best practices to implement that can ensure that stakeholders are on the same page. 

Establish clear data quality metrics and KPIs

There are many data quality metrics, and it’s important to determine the most relevant metrics for your organization. The following 12 data quality metrics are the most common and effective measurement tools that we typically see: 

1. Data downtime
2. Total number of data incidents
3. Table uptime
4. Time to response (detection)
5. Time to fixed (resolution)
6. Importance score
7. Table health
8. Table coverage
9. Monitors created
10. Number of unused tables and dashboards
11. Deteriorating queries
12. Status update rate 

Implement robust data validation rules

Data validation rules and checks help to verify that the data meets predefined standards and business requirements to help prevent data quality issues and data downtime. Common data validation testing techniques include: 

• Range Checking: If a field is supposed to contain an age, the range check would verify that the value is between 0 and 120.
• Type Checking: If a field is supposed to contain a date, the type check would verify that the value is indeed a date and not a string or a number.
• Format Checking: If a field is supposed to contain an email address, the format check would verify that the value matches the standard format for email addresses.
• Consistency Checking: Checks if the data is consistent across different fields or records. If a field is supposed to contain a country, and another field in the same record contains a city, the consistency check would verify that the city is indeed in the country.
• Uniqueness Checking: If a field is supposed to contain a unique user ID, the uniqueness check would verify that no other record contains the same user ID.
• Existence Checking: If a field is supposed to contain a non-null value, the existence check would verify that the value is not null.
• Referential Integrity Checking: If a data value references an existing value in another table. For example, if a field is supposed to contain a foreign key from another table, the referential integrity check would verify that the value exists in the referenced table.

Encourage a culture of data quality within the organization

Change management is just as essential a step as any new tool or process. If you’re implementing a new data quality assurance workflow, you’ll need to scale your strategy as your team gets up to speed. We like to think of it as the “crawl-walk-run” method.

By familiarizing team members with the importance of data quality from the start, you’ll be able to build a culture around the importance of reliable, trusted data that only grows as your data quality management strategy becomes more robust. 

The Future of Data Quality Assurance

The data and AI landscape has evolved significantly over the past decade. However, while modern data estates have scaled, changed, and evolved, data quality management has somehow stayed the same for most data teams. 

As Barr Moses, Monte Carlo CEO, put it, “In its simplest terms, you can think of data quality as the problem; testing and monitoring as methods to identify quality issues; and data observability as a different and comprehensive approach that combines and extends both methods with deeper visibility and resolution features to solve data quality at scale.”

Build a Data Quality Assurance Framework with Monte Carlo

As we plunge ahead into the world of generative AI and beyond, data teams will need to focus on data quality assurance via modern methods, like Monte Carlo’s data and AI observability. 

Data and AI observability offers an end-to-end, AI-enabled approach to data quality management that’s designed to answer the what, who, why, and how of data quality issues within a single platform. It leverages both data testing and automated data quality monitoring in a single system with coverage in data, systems, code, and models. 

As data teams continue to build data-driven cultures, solutions and AI models, data quality assurance will play a major role in driving data reliability and trust. 

To learn how Monte Carlo can help you build an effective data quality assurance workflow, speak to our team.

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