How to scale automated data quality checks
Once a reactive, error-prone process, data quality checks have been transformed into a strategic asset thanks to automation, preventing downstream problems earlier.
Does it ever feel like the traditional methods of data quality assurance — think meticulously crafted SQL queries and endless lines of validation rules — are a bit like bringing a knife to a gunfight? These methods, while great for being precise, don’t scale well in the face of the vast, unruly datasets that modern businesses juggle daily.
Machine learning-based approaches that can automatically detect and address data quality issues are emerging as the solution. Imagine a system that doesn’t just follow validation rules but learns from data to predict and fix its imperfections.
They adapt, they learn, and they scale. This means that as your company grows and your data becomes more complex (because, of course, it will), these systems adjust and continue to ensure that your data quality is not just maintained but continuously improved.
What do these systems do that’s so much better than automating Python scripts or SQL queries to perform data quality checks? Watch below as our co-founder Mona Rakibe explains to John Kutay the What’s New In Data? Podcast not only the immediate benefits of automation but also its long-term implications for businesses aiming to thrive in a data-driven future.
Benefits of automated data quality checks
“Data quality needs to be automated,” Mona explains. “I do not feel that any data engineer wants to sit there doing incident management.”
The solution? Automated data quality checks that rely on specialized software tools to continuously monitor data quality indicators such as accuracy, completeness, and timeliness, correcting errors and ensuring data standards are consistently met.
The advantages of automating data quality checks include:
- Increased Efficiency: Automation speeds up the process of checking data, reducing the time and labor traditionally required to ensure quality.
- Greater Accuracy: Minimizes human error, ensuring data is consistently correct and reliable.
- Cost Savings: Reduces the costs associated with manual data corrections and the potential financial impact of data errors.
- Scalability: Effortlessly handles increasing volumes of data as businesses grow without the need for proportional increases in human resources.
- Consistency: Maintains uniform data quality standards, applying the same rules across all datasets without bias or variation.
- Proactive Problem Solving: Identifies and resolves data issues before they escalate, preventing downstream problems and decision-making errors.
Key automated data quality checks to implement
Data quality issues can occur at any point in the data lifecycle. Some of the most common problems you should automate monitoring:
- Schema drifts
- Freshness
- Anomaly detection
- Cross-system consistency
- Uniqueness
- Completeness
- Accuracy
- SLAs not met
The earlier that issues are discovered, the better. “It’s very important for teams to think like this,” says Mona, “and to have a very deep, intuitive understanding of the upstream data they’re working with so that they can better provide value to those who are consuming that data in whatever manner it may be.” It’s always costlier and more difficult to fix poor-quality data by the time it impacts decision-making downstream.
Scale data quality automation with Telmai
Furthermore, data quality should not just be automated but enable non-technical users to understand the issues, too. “Reports which the business teams can understand,” as Mona plainly puts it.
Enter Telmai, a machine learning-based platform that can ingest data from various sources and identify inconsistencies, missing data, and other problems. All in an easy-to-use, no-code interface, empowering data professionals of all skill levels.
With Telmai, setting up automated data quality checks is easy:
- Create an account within minutes
- Connect your data sources
- View data health insights and profiling reports
- Get alerts when data issues occur
- Learn about your data
Discover how Telmai can revolutionize your data ecosystem – Try Telmai today.
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