Data onboarding is a crucial process in any data-driven project. Doing it properly ensures data quality, consistency, and usability. Poor onboarding, on the other hand, can lead to flawed analyses and misguided decisions.
Here are ten (10) dos and don’ts to guide you through the process…
#1 – Do understand the data source.
Before importing a data set, study where it comes from, how it was collected, and what it represents. This context helps you interpret the data correctly and spot potential biases or gaps.
#2 – Do perform data profiling.
Analyze the structure, types, ranges, and distribution of values. Profiling helps identify anomalies, duplicates, or outliers that may require cleaning or deeper investigation.
#3 – Do validate data quality.
Check for completeness, accuracy, consistency, and timeliness. Run automated checks or scripts to flag missing values, formatting issues, or inconsistent coding (e.g., “USA” vs. “United States”).
#4 – Do document everything.
Maintain clear metadata, including definitions of each field, data types, units, encoding formats, and update frequency. Good documentation saves time and avoids misinterpretation.
#5 – Do ensure compliance.
Verify that the data complies with relevant privacy laws (e.g., GDPR, HIPAA) and organizational policies. Anonymize or mask sensitive information where required.
#6 – Don’t skip cleaning.
Even trusted sources can contain errors. Never assume data is ready “as is.” Always clean for duplicates, null values, and formatting inconsistencies before analysis.
#7 – Don’t ignore dependencies.
If the data set integrates with others, ensure that keys and relationships are valid. Failing to align time zones, units, or IDs across data sets leads to incorrect merges and faulty insights.
#8 – Don’t overlook scalability.
If the data set will grow, ensure your storage and processing systems can handle the increasing size and complexity. Test performance on representative samples.
#9 – Don’t forget to version control.
Track changes in data over time. Use versioning tools to preserve historical states and support reproducibility of results.
#10 – Don’t rush into modeling.
Premature analysis without proper onboarding can yield misleading results. Take time to understand and prepare the data thoroughly before feeding it into any model.
The Takeaway.
Effective data onboarding involves more than just loading a data set into a system — it requires careful validation, cleaning, documentation, and understanding. Following these dos and don’ts ensures the data is trustworthy and ready for responsible use.
What about you? What challenges have you faced when onboarding a new data set? How did you overcome them? Please comment – I’d love to hear your thoughts.
Also, please connect with DIH on LinkedIn.
Thanks,
Tom Myers
