In brief: Big data describes datasets whose volume, speed, or variety exceed ordinary processing methods. Size alone does not guarantee a useful conclusion.
How I use Big data in practice
I first choose a narrow task with clear input and controllable output. I record which data the system may use, what it must not decide alone, and who approves the result. I compare a small sample with manual work; only after finding typical errors do I address broader automation.
What to watch out for
A demo is not production. A few prepared examples may work while edge cases, Czech language, or new inputs fail. Without real test cases, human review, and cost measurement, scaling is premature.
Questions for decisions
- Which narrow task should technology improve?
- What data may it use and who checks the output?
- On which real cases will we measure accuracy and cost?
- How will we stop or roll back the process on error?