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If you are a modern organization, you already know that bad data quality is a costly threat. According to Forrester, 9 out of 10 professionals rate data quality as a critical aspect of information governance. And so should you! This blog covers five data quality monitoring techniques every enterprise should deploy to secure high-impact insights, performance, and regulatory compliance.
What is data quality monitoring?
Data quality monitoring is the process where you continuously assess, validate, and track your data across different systems. This is done to ensure that it meets your organization’s standards for accuracy, completeness, consistency, timeliness, and reliability. Data quality monitoring means setting up a system of checks that scan data pipelines, storage, and interfaces for issues such as missing values, duplicates, or outdated records.
Data quality monitoring matters a lot to your organization. You can perceive it as the early-warning system that provides your teams with the visibility and control needed to take timely corrective actions.
Best tips for data quality monitoring
Tip #1 Consider automating data profiling & analytics
Automated data profiling is the systematic analysis of datasets across tables, columns, and systems. This is done to capture metrics like null rates, pattern diversity, value ranges, and distribution curves. You can utilize data profiling tools that can scan large datasets quickly and consistently. Over time, these checks help you learn what “normal” looks like for your data! If anything changes like strange data formats, you’ll catch it early.
To make this work:
- Run these checks on a regular schedule. It could be done daily or weekly, depending on your needs.
- Keep a record of past results so you can spot long-term trends or issues.
- Set alerts to notify you when something looks off.
LumenData helps enterprises implement profiling strategies using Informatica Data Quality, offering full visibility into data fitness and lineage.
Tip #2 Enable real-time & streaming data validation
Instead of waiting until later to clean up bad data, it’s smarter to catch it right when it enters your systems. This method is often called a “data quality firewall.” It checks if the incoming data matches the right format, type, or rules before it’s saved or used in reports and dashboards. Wondering why does this matter? Because the earlier you identify a problem, the cheaper and easier it is to fix. Plus, you avoid passing bad data into your analytics or AI models.
We recommend implementing the following practical steps:
- Add data checks into your APIs, data pipelines (ETL/ELT), or streaming tools like Kafka and Snowflake.
- If a record fails the checks, send it to a separate queue, called a quarantine area, and let your team know.
- Keep updating your data quality rules as new data sources are added or when schema changes.
You could also consider using tools like Informatica Cloud Data Integration and Fivetran. LumenData can help enable real-time rule enforcement at ingestion points. This will not only help you reduce downstream quality issues but also strengthen end-to-end trust.
Tip #3 Leverage machine learning models for anomaly detection
There are chances that you might be relying only on fixed data quality rules for anomaly detection. For instance, alert if nulls are over 10%. But things are becoming more advanced now. Modern enterprises are using machine learning to figure out what’s normal for their data and spot when things go off track. With smart ML models, you can detect unusual trends in things like volume, delay, or value ranges, even when the changes are subtle. Fixed thresholds can be too rigid. A spike in missing values during a system upgrade, for instance, might get missed if the rule wasn’t designed to expect it. Smart models, on the other hand, adapt to the data’s natural rhythm.
Tip #4 Utilize centralized dashboards
We have a question for you – how can you manage something that you can’t see? You cannot, right? That’s why it’s practical to use centralized dashboards. Dashboards that can bring together your most important data health metrics such as how complete, accurate, or fresh your data is When everyone on your team, right from data engineers to business analysts, can see the same live data quality insights, it becomes easier to coordinate, respond to issues, and improve overall trust in the data.
At LumenData, we can help create executive-ready dashboards using Snowflake’s Snowsight, Power BI, and other leading modern data platforms.
Tip #5 Recognize the power of metadata management
Metadata is the “data about your data.” It offers powerful insights that go beyond raw values. By tracking where data comes from, how it’s structured, and how it moves through systems, metadata management becomes a key monitoring layer for ensuring data quality. Be it a shift in schema or a broken data lineage, modern metadata tools can catch these changes before the data starts producing bad results.
When you monitor metadata regularly, it helps detect silent failures like a field quietly dropping from a source feed or a data transformation rule getting skipped. It complements value-level checks by answering a different question: “Is the structure and flow of this data what we expect?” LumenData helps clients implement metadata-driven monitoring using tools that integrate Snowflake, Informatica, and data catalogs. We help you detect structural issues before they become downstream data problems.
Stay on top of data quality monitoring with LumenData
If you’re ready to embed intelligence into every layer of your data architecture, LumenData is ready to guide the way. Our expert consultants work across industries to integrate best practices into cloud-native architecture. We help you align data quality monitoring efforts with broader data governance and AI-readiness objectives.
Top reasons to choose us:
- 270+ Certifications by Informatica, Fivetran, Snowflake, dbt Labs & other leading data platforms.
- Access to flexible, on-demand, and scheduled support to implement and optimize your data quality.
- SaaS extensions and automation frameworks that accelerate your data projects.
Contact us today.
About LumenData
LumenData is a leading provider of Enterprise Data Management, Cloud and Analytics solutions and helps businesses handle data silos, discover their potential, and prepare for end-to-end digital transformation. Founded in 2008, the company is headquartered in Santa Clara, California, with locations in India.
With 150+ Technical and Functional Consultants, LumenData forms strong client partnerships to drive high-quality outcomes. Their work across multiple industries and with prestigious clients like Versant Health, Boston Consulting Group, FDA, Department of Labor, Kroger, Nissan, Autodesk, Bayer, Bausch & Lomb, Citibank, Credit Suisse, Cummins, Gilead, HP, Nintendo, PC Connection, Starbucks, University of Colorado, Weight Watchers, KAO, HealthEdge, Amylyx, Brinks, Clara Analytics, and Royal Caribbean Group, speaks to their capabilities.
For media inquiries, please contact: marketing@lumendata.com.
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