Identifying Good Data Sources (ROCCC Framework)
1. What Makes a Data Source “Good”?
The idea of what is “good” can be subjective, but good data sources can be evaluated using clear, practical standards. High-quality data leads to greater confidence in analysis and more reliable decisions.
Rather than relying on intuition, data analysts use best practices to assess whether a dataset is trustworthy and appropriate for use.
2. The ROCCC Framework
A simple way to evaluate data quality is the ROCCC framework:
- Reliable
- Original
- Comprehensive
- Current
- Cited
If a dataset meets all five criteria, it is considered a strong data source.
3. Reliable
Reliable data can be trusted.
Characteristics
- Accurate and complete
- Free from obvious bias
- Properly vetted and fit for use
Reliable data comes from sources that follow sound collection and validation practices.
4. Original
Original data comes directly from the source where it was first collected.
Why this matters
- Second- or third-party data may be altered or summarized
- Verifying data against the original source improves accuracy
Whenever possible, analysts should confirm data using its original source.
5. Comprehensive
Comprehensive data contains all critical information needed to answer a question or solve a problem.
Key idea
- One positive data point is not enough
- Gaps in data can lead to incomplete or misleading conclusions
Just as you would research multiple aspects of a company before working there, data analysis requires a complete view of the subject.
6. Current
Current data is up to date and relevant to the task.
Why recency matters
- Data loses value over time
- Outdated data may no longer reflect reality
Example:
- A ten-year-old client list is not useful for inviting current customers
The best data sources are refreshed regularly.
7. Cited
Cited data clearly identifies where it came from.
What to look for
- Who created the dataset
- Whether the creator is a credible organization
- When the data was last updated
Citations increase transparency and allow others to verify the data.
8. Common Sources of High-Quality Data
Data analysts often rely on well-established sources known for quality and credibility.
Examples
- Publicly vetted datasets
- Government agency data
- Academic research papers
- Financial and economic datasets
These sources typically meet ROCCC standards.
9. Why ROCCC Matters
Using data that meets ROCCC criteria:
- Reduces the risk of flawed analysis
- Improves decision-making confidence
- Strengthens trust in analytical results
Poor-quality data leads to unreliable outcomes, regardless of how advanced the analysis is.
10. Key Takeaways
- “Good” data can be evaluated objectively
- ROCCC is a practical framework for assessing data quality
- Reliable data is accurate and unbiased
- Original data should be verified at the source
- Comprehensive data covers all critical aspects
- Current data reflects present conditions
- Cited data increases credibility
- High-quality data supports better decisions
One-sentence summary
The ROCCC framework—Reliable, Original, Comprehensive, Current, and Cited—provides a clear and effective way to identify trustworthy, high-quality data sources for analysis.