Data collection is often the foundation of any successful data project, yet it’s one of the most overlooked and challenging stages.
Real-world data is rarely clean or complete information can be scattered across multiple sources, inconsistent, or even contradictory.
Privacy regulations and compliance requirements can further complicate the process, making it difficult to gather the data you need without breaking rules.
Even small issues, like missing values or incorrect formats, can cascade into major problems down the line, affecting model performance and decision-making.
That’s why finding reliable strategies for collecting, validating, and managing data is so important.
We’d love to hear from you: how do you ensure the quality and consistency of your data during collection?
