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What are the most common data analytics mistakes small businesses make?
Common mistakes include starting with tools instead of questions, building too many dashboards, ignoring data quality, leaving metric definitions vague, hiring a data scientist too early, giving no one ownership, and relying on a supplier who keeps the code and data. Each leads to wasted spend and low trust.
Other frequent errors:
- Trying to connect every system at once
- Skipping user feedback during design
- Forgetting ongoing maintenance and monitoring
- Not documenting what was built
- Treating analytics as a one-off project
Most are avoidable by starting small, agreeing definitions early, checking data first and planning who will look after the system after launch. A short written plan and a named owner for each item usually prevents most of these problems.
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