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This article focuses on the technical and operational issues that most often break web data collection in projects.
To understand what actually goes wrong, we analyzed 82 discussion threads (questions, issues, and conversations) from Stack Overflow, Reddit, GitHub Issues, Hacker News, niche and regional platforms.
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Let’s say, someone on your team finds a public website with data that looks useful.
But before anyone commits engineering time, there are usually a few questions:
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Let’s say, you run a background screening platform.
You pull data from courts, registries, vendors, and public sources. Some day, you discover that one person shows up three times in your system:
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Every technical leader knows that salary isn’t the full story. What’s less obvious is how far the gap can stretch—and how differently it plays out depending on where and how you hire.
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A 2024 peer-reviewed study in Criminology found that about 60% of people had at least one false positive in their reports, and nearly 90% had at least one false negative. So they’re systemic issues you have to design around.
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Amazon Textract vs Anthropic: PDF to JSON Accuracy, Cost, and Scale
