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?

When working with petabyte-scale datasets using distributed frameworks like Hadoop or Spark, what strategies, configurations, or code-level optimizations do you apply to reduce processing time and resource usage? Any key lessons from handling performance bottlenecks or data skew?

I have tried my best to collect data from surveys, questionnaire, interviews and group discussions. What else can be my choice?

I follow the above model. Please suggest a better framework to better represent the collected data.