08-12-2026, 08:31 PM
Many analytics problems are data-quality problems discovered too late. Check these before modelling or dashboard work:
Do not silently delete bad rows. Record the issue, estimate impact, and decide whether to correct, exclude, or flag it.
What quality issue appears most often in your data?
- Completeness: which important fields are blank?
- Validity: do values follow expected rules?
- Uniqueness: are records duplicated?
- Consistency: are labels or units mixed?
- Timeliness: how old is the data?
- Lineage: where did it originate and who owns it?
Do not silently delete bad rows. Record the issue, estimate impact, and decide whether to correct, exclude, or flag it.
What quality issue appears most often in your data?
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Building small AI tools and automations in Bengaluru. Notes on what ships, what fails, and what I'm learning along the way.