A Tale of Dirty Data
A while back, our Accounts Receivable team asked for help tracking down a past-due invoice. After two weeks or so, we finally received a check for $500,000. Then, two days later, a second check for the same amount arrived.
Upon investigation, we noticed a small but costly typo—one check referenced INV5867, while the second referenced 1NV5867.
After arranging for the duplicate check to be returned, I reached out to the client’s CIO. I pointed out the typo and noted that they already owned our Data Integrity tool, which—if used correctly—could have caught and prevented this issue.
The next day, I got an irate call from one of my champions at the account:
"What are you doing? You've got our CIO all over me to track down this issue. I don't have time for this! Do you know how often things like this happen? I can’t go chasing every crazy mistake!"
This story highlights a critical problem in IT—data integrity failures—and why businesses need a better way to detect and prevent them before they become costly mistakes.
Data Integrity Challenges in the Enterprise
As data moves across your enterprise, it faces multiple risks that can compromise decision-making, compliance, and financial accuracy.
Common causes of data corruption include:
? Bit rot – Data degrades over time in storage
? Synchronization errors – Systems fall out of sync, causing discrepancies
? Schema mismatches – Changes in database structures lead to incompatibility
? Data truncation – Fields cut off important information
? Encoding issues & bit flipping – Corrupting data during transmission
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Many companies try to solve these problems with manual "stare and compare" methods, but they are slow, costly, and prone to human error. Others attempt patchwork Python scripts that provide only partial coverage of the data pipeline.
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Why Tricentis Data Integrity (DI)?
Tricentis Data Integrity testing is the only solution that provides end-to-end coverage of your entire data landscape.
Our approach automatically validates every row and column in your complex enterprise data environment—no more spot-checking or hoping for the best.
? Pre-Screening Tests – Verify whether your files contain the expected data
? Vital Checks & Field Tests – Ensure data integrity, completeness, and correctness
? Reconciliation Tests – Compare datasets across systems
? Report Testing – Validate presentation and content
? Profiling – Ensure logical consistency from a business perspective
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Our purpose-built in-memory database allows us to validate massive amounts of data efficiently, while seamlessly integrating with SAP, Oracle, ServiceNow, Salesforce, and over 160 other enterprise platforms.
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The AI/ML Problem: Garbage In, Garbage Out
Today, 60% of a data scientist’s time is spent wrangling data, leaving little room for meaningful AI/ML innovation. How can companies unlock AI's potential if they can't even trust their data?
Without Data Integrity, even the most advanced AI models are built on shaky foundations.
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The Cost of Bad Data: Real-World Horror Stories
We’ve all seen what happens when bad data goes unchecked:
?? Knight Capital (2012): A software glitch caused a $7 billion unintended stock purchase—leading to a $440M loss in 45 minutes
?? COVID-19 Reporting Failure: 16,000 cases went unreported due to Excel data truncation
?? TSB Bank Data Migration Disaster: Customers saw other people’s bank accounts—costing ￡330 million
?? Canada’s Phoenix Payroll System: Data migration errors caused $2.2B in losses and years of legal battles
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These risks are avoidable. Tricentis Data Integrity gives you the confidence that your data is clean, accurate, and ready for action.
Let’s stop chasing $500K mistakes and start fixing data at scale.
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#DataIntegrity #DataQuality #Tricentis #Automation #AI #EnterpriseData
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