It’s easy to understand why AI feels magical. Type a question and within seconds you get a fluent answer. But behind the illusion lies a mix of model design, training goals, and something far less mysterious: data quality.
AI doesn’t “understand” truth, it predicts patterns. But if those patterns are built on incomplete, inconsistent, or biased data, the outputs will be equally flawed.
High-quality data has several characteristics. Here are three:
- Accuracy: correctly and reliably represents what it describes
- Completeness: includes all required values and metadata
- Consistency: remains uniform across datasets and over time
Data quality isn’t everything, but without it, even the most advanced AI can’t see clearly. Read more.
GIGO: Garbage in → Garbage out
Our very own Satoru Hayaska, Data Scientist on the Education Team at KNIME, explains GIGO in terms of data quality in just 30 seconds.
Mind Your Dashes (and Your Data)
The em-dash obsession in AI writing isn’t style but another example of the importance of data quality. In this instance: bias. Language models learn from books, articles, and essays. Never flagged as something special to avoid, AI doesn’t just mimic the em-dash habit, it overuses it. Read more about the GPT-ism.
P.S.
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