1. Data Quality We’ve Got Your Number
Quality Assurance Process

2. Data Quality License Plate
BD4BD2SL

3. My Main Points Data quality saves lives
Quality is in the eyes of the users
Performance measures Make Sense
Quality assurance as process

4. Importance of Quality Data
Data-driven decisions
GIGO
Data is the foundation layer
Good data saves lives

5. Data Driven Decisions

6. Garbage In – Garbage Out

7. Act Present Analyze Collect/QA Safety Benefits Decisions Information
Deploy this
Enforce that …
Decisions
Present
Information
The Traffic Safety outcomes are at the top
At the bottom, we essentially have the data layer where NHTSA is
BTW, I am giving a talk on Tuesday afternoon where I’ll be delving into this concept in more detail.
Analyze
Data
Collect/QA

8. We Can Do Better Than This

9. Quality is in the Eyes of the Users
Who are your users?
What do they need?
Decisions
Support material
What level of quality?

10. Example of User Needs Who: Wyoming Highway Patrol
What: Improve enforcement focus
Which behaviors
Where
Data Needs:
Integrating Crashes & Citations - comparison
Accessibility – to institutionalize

11. Joe McCarthy - Consultant to WYDOT
The Driver Behaviors
Restraint
Alcohol
Speed
Distraction
Drugs
TRF - Oct 30, 2013
Joe McCarthy - Consultant to WYDOT

12. Results – Behavior Focus
Lots of speeding tickets per speeding crash
Not so aggressive regarding restraint or alcohol
Poor data for distraction, drugs
Restraint
Speed
Alcohol
Drugs
Distraction

13. Results - Enforcement Focus Map
Comparing Crashes to Citations by location
Essentially, # citations / #behavioral crashes
Red:
Citations/crashes is low
Suggest increased patrolling
TRF - Oct 30, 2013
Joe McCarthy - Consultant to WYDOT

14. Crash Data Flow Collect Consolidate Analyze Decide Law Enforcement DOT
Highway Safety
Analysts
(4 E’s)
Exec Staff
Legislature
etc.
Data Flow
Feedback Loop

15. Performance Measures Management Questions Perf Measure Speak
How are things?
What should we do to improve?
Are they getting better?
Are they where we want them?
Perf Measure Speak
Baseline
Corrective Measures
Monitoring trends
Numerical target

16. Location Accuracy Just like realty Where do the errors happen?
Measure at two points in process
How can the errors be
Reduced
Corrected
For more, see Stacey Gierisch’s presentation (3:00-4:30 Monday)

17. Crash Location Accuracy
Collect
Consolidate
Analyze
Decide
Law
Enforcement
DOT
Highway Safety
Analysts
(4 E’s)
Exec Staff
Legislature, etc.
Capture the crash location data
Need to know which hot spots to address
Detect/correct invalid locations
Need valid
locations
Data Flow
Feedback Flow
Measure Point

18. Where should we be? – Target
Users need good locations
Need to be aggressive
Target: 98% valid

19. What Can We Do? – Corrective Actions
Monthly QA validation checks
Revamp incoming process
Quarterly monitoring/reporting
Map-based location 
Not Yet
Launched

20. Crash Data Timeliness Users need timely crash data Dependent on LEAs
Most studies use annual data
But can’t wait too long for last year
Need to know about latest crashes
Dependent on LEAs
Investigations take time
But, glitches happen

21. Crash Data Timeliness Feedback Loop Collect Consolidate Analyze Decide
Law
Enforcement
DOT
Highway Safety
Analysts
(4 E’s)
Exec Staff
Legislature etc.
Data Flow
Feedback Flow
Crash Occurred
Record in Database
Measure Points

22. WYDOT Crash Timeliness
Baseline
Corrective Action
Electronic Reporting
Corrective Action
Monitoring Trend
“On Hold” notices
Corrective Action
Quarterly Reports

23. Quarterly Feedback to Agencies

24. Quarterly Feedback to Agencies
Most Improved Agencies
(reduced delay)
Agencies Lost Ground
(increased delay)

26. For Follow-Up JoeGMcCarthy@yahoo.com
Questions?
For Follow-Up

27. Take-Aways Data quality saves lives
Quality is in the eyes of the users
Performance measures Make Sense
Quality assurance as a process

28. And now…
Bob has got your number.
Again.