1. I’ve Got Your Number (again) Presented by Bob Scopatz
Data Quality
I’ve Got Your Number (again) Presented by Bob Scopatz
August 6, 2017
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2. Goals Everyone cares more about data quality than when they got here.
Everyone knows how data quality “fits” with:
Data management
Data governance
Stakeholders
Data integration
Everyone understands the “enterprise” view of data quality

3. What is Data Quality in Traffic Records?
NHTSA guidance: 6-pack
Timeliness
Accuracy
Completeness
Uniformity
Integration
Accessibility

4. Why Should We Care About Data Quality?
We support the best in safety decision-making.
Bad data supports the worst in safety decision-making.
Bad data leads decision-makers to abandon Data Driven decision-making.
We’re professionals and it’s our job.

5. Data Management, Data Governance, & Stewardship
Management: Top-down rules and decisions
Governance: Implementing the Management Decisions
Stewardship: Doing the actual work of data collection, storage, translation, extraction, and analysis
Stewardship
Governance
Management

6. Data Integration A “special” case? No!
Crashes-only analysis no longer the standard.
Bad data will out.
Missing records
Errors
Incompatibilities

7. Enterprise Data: This View of Data
The Traffic Records Unicorn
Spatial Data, Enterprise GIS, and Integration
Non-Spatial Data
The Everything Bagel of Data
Building the Unicorn

8. Formal, Comprehensive Data Quality Management
Fits right in with Data Governance.
Data Quality is a process. It includes:
Measurements
Reviews and Audits
Feedback (from users, to data collectors)
Defined methods to correct errors
Continuous improvement

9. Implications of Data Governance
Data Quality Measurement is just another source of metadata.
Measurements themselves can be “good” or “not so good.”
If you just measure, and do nothing…expect nothing.
You really do have to do it all.

10. What Makes a Good DQ Measurement?
It measures a real data quality issue or concern. (User perspective is key)
All the stakeholders agree it is a valid measurement of performance.
It changes in response to real differences in quality.
It changes in response to our fixes.
It gives users confidence in the numbers.

11. Does Every System Need Every Measurement?
Crash and Injury are the “perfect” examples. All six DQ attributes matter.
Driver and Vehicle are different.
Overall timeliness is practically unmeasurable and it’s never a problem.
Timeliness of record updates matters a great deal.
Integration with citation & adjudication matters as well.
Roadway data is different too.
Accuracy is extremely important.
Integration (with Crash and traffic volumes) is extremely important.
So…what should you measure? First? Most?

12. The Future of Data Quality
Part of Data Governance
Executive-level Attention
Formalized Processes
Cooperative Among Stakeholders
Quantify Improvements

13. Bob Scopatz | bscopatz@vhb.com | 919.334.5624
Offices located throughout the east coast