28 08
2025
Data is the fuel of modern business, but if that fuel is contaminated, the engine will stall. Every day, organizations make high-stakes decisions based on data they assume is accurate. But even small errors can snowball into lost revenue, compliance failures, and missed opportunities. In this article, we’re taking a closer look at the consequences of poor data quality, showing you how to prevent them with Collibra Data Quality & Observability.
Collibra Data Quality (Collibra DQ) and Observability is an intelligent, SQL-based, self-service platform that continuously scans, profiles, and learns from your data. Using machine learning and adaptive rule building, it enables teams to find and fix data quality issues – before they impact operations or decision-making.
Data observability is the practice of monitoring the health and reliability of data pipelines. When you pair it with a strong data quality management framework like Collibra DQ, you get end-to-end visibility into your data assets – and company-wide trust in your data.
In short, Collibra Data Quality & Observability helps your team make sure that data remains consistent, accurate, complete, and timely across systems. And it does it with minimal manual effort.
It’s based on the dimensions of data quality, which we talk about in greater detail in our recent article about data quality metrics – completeness, accuracy, consistency, validity, uniqueness, and integrity.
To get more specific, let’s get into a few most common problems we see with data quality in enterprises – and how to solve them with Collibra.
Collibra’s Solution: Automated data profiling, rule creation, and cleansing.
How it works:
Collibra Data Quality’s out-of-the-box profiling helps you understand the shape and format of your data. Think of it as a self-cleaning oven for your data. You set the rules, and it does the dirty work.
Based on the so-called data quality jobs, it automatically identifies anomalies, duplicates, outliers, and threshold violations based on your criteria. From there, Collibra builds rules using pushdown processing and SQL-based logic, allowing for faster and more efficient remediation directly within your data source systems.
The result? Faster analytics, less manual validation, and more reliable insights.
Collibra’s Solution: Data lineage, impact analysis, and quality scoring.
How it works:
Collibra DQ is like a GPS for your data, showing you exactly where it came from, where it’s going, and if there are any roadblocks along the way.bIt provides explainable data lineage, tracing your data pipeline from source to report.
With visibility into transformations and dependencies, you can trust in your data and validate the integrity of every metric. Quality scores give stakeholders a clear view of dataset reliability, while dashboards track issues and trends over time.
Use scorecards in Collibra for a historical view of your dataset performance, visualizing their health, consistency, and evolution over a date range. This way, you can schedule routine health checks more accurately and protect downstream process consumers from getting inaccurate data.
Collibra’s Solution: Proactive anomaly detection and real-time alerts.
How it works:
To use another analogy, Collibra’s like a smoke detector for your data, alerting you to potential problems before they become a raging fire. Its machine learning models learn historical data patterns to spot anomalies (such as minimum and maximum values or unique results) and format inconsistencies (great for spotting typos and other errors).
Real-time alerts notify stewards and engineers of schema changes, duplicate records, or broken pipelines, so they can fix issues before they cascade downstream and affect analytics or reporting.
Collibra’s Solution: Centralized data catalog and data governance.
How it works:
Collibra helps everyone in your organization speak the same data language. And by seamlessly integrating with the Collibra data catalog, Collibra DQ can make sure that data across systems is cataloged, scored, and governed.
The platform supports data discovery and collaboration, helping technical and business stakeholders align around shared definitions, rules, and quality benchmarks. It also identifies sensitive data and categorizes it, helping make sure that the data is stored and processed in compliance with relevant regulations.
It’s pretty easy to predict what a bad dataset can do when undetected (or many bad datasets, for that matter). In data-driven businesses, data (at least theoretically) is the foundation of decision-making. And to make the right decisions, you need good quality, accurate data.
Which is exactly why early detection and proactive addressing of data issues go a long way – straight to the bottom line. Bad data is a silent cost center that can quietly drain resources, damage reputation, and mislead decision-makers.
In fact, according to data cited by Gartner a few years back, poor data quality costs organizations an average $12.9 million every year.
And the impact is not only financial – here are the different ways poor data quality can hurt your business.
When decisions are based on inaccurate or incomplete data, you risk costly missteps. For example:
And when errors add up over time, they will eventually start eroding profit margins and impacting shareholder value.
Teams often spend hours, even days, manually fixing data quality issues, reconciling mismatched records, or validating information across systems.
This goes beyond operational costs – it can also delay project timelines and divert skilled talent from strategic, high-value work. In some cases, entire initiatives stall because the underlying data can’t be trusted.
This pretty much goes without saying, but in regulated industries, poor data quality can lead to non-compliance with reporting requirements, privacy regulations, and industry standards.
As we already mentioned, this exposes your business to fines, legal action, and increased scrutiny from regulators. But beyond the financial impact of penalties, compliance failures will ultimately also undermine your relationships with partners and customers.
Customers expect personalized, accurate, and timely interactions. If they receive duplicate bills, irrelevant offers, or incorrect account details, trust can erode quickly. Rebuilding it often costs far more than maintaining high-quality data in the first place.
Not to mention, it directly impacts your brand credibility and might result in lost sales, depending on how serious your data quality issues turn out to be.
Poor data quality can obscure valuable insights hidden within datasets.
You might not be able to identify emerging trends, underperforming segments, or new market opportunities simply because the data isn’t reliable enough to act upon. For a “data-driven” enterprise, that’s not something you can really afford.
Ultimately, bad data distorts the reality decision-makers rely on – after all, you make your decisions based on incorrect or obsolete information.
And the consequences? Flawed strategies, investments in the wrong areas, and missed growth targets. In competitive markets, the cost of a single wrong decision can be significant, and the root cause often lies in data that was never validated properly.
(And it’s so easy to fix with the right tools like Collibra Data Quality and Observability.)
Collibra’s platform with the Data Quality & Observability module in place is more than a piece of software and a tech upgrade – because it can help you avoid the issues early, before they grow and actually impact the business.
Here are a few ways it can do that.
With Collibra, your data team gets the right tools to:
You’re investing in preventative maintenance to avoid the issues instead of waiting for accidents to happen and only then taking action to fix them. (And you know “prevention is better than cure” is true every time.)
At Murdio, we love Collibra because it’s really amazing to see how much of a real-world difference data quality can make, while many businesses might still consider data management a nuisance.
When you have visibility and governance processes in place, your organization can move and grow faster. You can think of it as building on a solid foundation – your business needs a solid data foundation to scale consistently and predictably.
Different departments use different tools and operate on different definitions – most of the time, it’s a recipe for chaos. And while everyone has been using the “silo breaking” analogy for years, it still seems to be the reality of many enterprises when it comes to data – especially as the amounts of data have grown exponentially.
Collibra breaks those barriers by creating a shared language and workspace for data, with automated workflows that help keep it consistent and healthy, bringing everyone from IT to business leaders onto the same page.
Don’t think about investing in Collibra Data Quality & Observability (or any other data quality tool) as just a business expense – it’s an actual investment in the clarity, agility, and team alignment your business needs to make better decisions, and make them faster. It’s not really optional if you want to continue growing consistently while building your brand reputation.
At Murdio, we specialize in helping data-driven organizations get real value out of Collibra’s ecosystem, from strategy and setup to custom workflows and governance frameworks. So, if you want to upgrade your data quality and turn your data into a business enabler, reach out, and let’s talk.
Collibra Data Quality & Observability is a comprehensive software solution for monitoring, measuring, and improving data quality across systems. It scans your data sources, profiles datasets, and uses machine learning to detect anomalies, duplicates, outliers, and other data quality issues.
Over time, it learns your data patterns and adapts, so you can make sure your data is reliable and compliant.
Collibra DQ uses automated data quality rules, anomaly detection, and historical profiling to validate your data against defined thresholds.
It makes sure your data remains consistent, accurate, and complete, giving stakeholders the visibility they need for better decision-making. Plus, the platform integrates with your data catalog for end-to-end governance and data discovery.
According to Collibra, the six dimensions are:
You can read more about them in our article on data quality metrics.
Ocena wg. Neuronwritera. Bardzo dobrze to wygląda – tymbardziej, że to treść która nie ma bezpośredniej konkurencji, bo na tą frazę są tylko docs’y Collibry.
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