Data Quality We’ve Got Your Number Quality Assurance Process

Data Quality License Plate BD4BD2SL

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

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

Data Driven Decisions

Garbage In – Garbage Out

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

We Can Do Better Than This

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

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

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

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

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

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

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

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)

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

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

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

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

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

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

Quarterly Feedback to Agencies

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

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

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

And now… Bob has got your number. Again.