Even having platforms with well-designed data structures, systems may have troubles. It doesn’t matter how good your modeling and data architecture skills are, as the probability of bugs is high, which can affect the accuracy of the results.
This situation does not prevent you from using various ways to emerge data quality, such as schema change management, circuit breakers, or data asset certification. They can be used as security for less specific but still significant problems which can’t be solved with the help of testing. As developers say, alert today, alive tomorrow.
How to cheat the universe?
There are some valuable tips for solving problems with source data: Of course, you can try to create a system that will never break, but it is impossible to foresee all the variants of violations and breakdowns and, consequently, their consequences.
So, the most typical causes of failures of even the most advanced data systems are:
Human factor
People make mistakes; they are not robots. These errors can occur when incorrect manual data entry is made. For example, triggering a manufacturing error or adding a problematic filter.
Third-Party Influence
No system is entirely independent and uses a variety of data sources. For example, if your partner does not meet the deadlines for sending data, your “ideal” system will not work.
New trends
Data, like fashion, is highly volatile and constantly transforming. The introduction of new trends is always associated with possible errors. An example is the introduction of cryptocurrency as a new financial instrument in a company.
Test scaling
Let me emphasize that it is unrealistic to foresee how data will enter the funnel. Testing shows only a small percentage (about 19%) of data problems. For example, even careful monitoring of quality with many rules in the pipeline did not prevent bugs and conflicts. Read more about What Is Big Data and How Does it Impact You?
Tips from colleagues
In the world of cybersecurity and software development, programmers don’t try to create a perfect system but invest resources in methods for detecting, alerting, and fast fixing them.
Automated monitoring is the choice of modern investors
Here are three major data software engineering development trends that allow software applications to measure their reliability.
Close cooperation. Previously, developers were left out after launching applications. Because of this, mistakes were repeated and repeated constantly. As a solution, the concept of DevOps emerged, an approach that defines the constant interaction of developers and operational teams in the process of developing and debugging software. Like this, there is DataOps – is an approach that brings together teams of data scientists and analysis engineers.
The second trend is specialization. Professional skills upgrading serves to advance reliability through expertise improvement and best practices. This specialization is coming on the scene now with the data reliability engineer’s appearance.
Last but not least, there is increasing investment in monitoring and performance management tools. For example, DataDog and New Relic allow non-stop monitoring of anomalies. So, the most advanced approach is combining people, processes, and technology to improve reliability.
One doesn’t need to play a round game
Investment funds are designed to influence the effectiveness of decisions, which in turn are based on data. You can build trust and scale processes if your data is reliable. If not, the investment will turn from a source of income into an unwanted expense.
This right path can be taken by using valuable tools such as monitoring, data observability, and genealogy. Be collected but not tense, and then all parties interested in success will thank you.
Types of Bad Data
| Type | What It Looks Like |
|---|---|
| Incomplete | Missing fields, such as a customer record with no email |
| Duplicate | The same customer listed twice with slightly different names |
| Inconsistent | The same value written in different ways, such as USA and U.S.A. |
| Outdated | Old addresses or phone numbers that are no longer correct |
| Invalid | Values that break the rules, such as a future date of birth |
How to Check Data Quality
Start with data profiling. This means looking at your data to find missing values, duplicates, and odd values. Set validation rules for each field. For example, an email field must contain an @ sign. A date field must be a real date. Run these checks each time new data arrives. Track the number of errors over time, so you can see if quality is getting better or worse.
Steps to Clean Bad Data
First, find the bad data with profiling and validation rules. Next, fix what you can: fill in missing values where the true value is known, merge duplicates, and standardize formats. Then, check the source of the errors. If a form allows bad input, fix the form. If a partner sends bad data, tell them about the problem. Last, document what you fixed, so the team can learn from it.
Tools That Help
Great Expectations is an open-source tool that tests data quality with rules you define. dbt lets you test data as part of your data pipeline. Apache Airflow can schedule and monitor data jobs. Data observability tools watch your pipelines and alert you when something breaks. Pick tools that fit your stack and your team size. Start small with one tool and a few key checks.
Frequently Asked Questions
What is bad data?
Bad data is data that is incomplete, duplicate, inconsistent, outdated, or invalid. It can cause wrong results and bad decisions.
What causes bad data?
Common causes are human error, third-party sources, new trends, and poor testing. People make mistakes when they enter data by hand.
How do I find bad data?
Use data profiling to find missing values, duplicates, and odd values. Set validation rules for each field and run checks on new data.
What is data profiling?
It is looking at your data to find missing values, duplicates, and odd values. It helps you see the quality of your data before you use it.
Which tools test data quality?
Great Expectations, dbt, and Apache Airflow are popular tools. They test data with rules and watch pipelines for breaks.
Disclaimer: This guide gives general information only. Data tools and best practices can change. Check the official docs for the tools you use.
Last Updated: 6 November 2026