Data Driven Decision Making By Larry Frevert, PE, PWLF Senior Consultant – TREKK Design Group, LLC, and Director – Public Works Institute, KCAPWA

Types of Data Driven Decision Making Projects Asset Management Traffic Studies Water and Waste Water Demand Studies Solid Waste and Utilities Rate Studies Fleet Usage and Replacement Studies Infrastructure Life Cycle Cost Analysis Property Codes Violations Tracking Other?

Roads and Bridges Magazine ~ February 2015

The Location of salt sheds, where road salt is stored, was the most limiting factor during RSIC operations. Essentially, trucks having to backtrack over areas they have already covered or worse traveling empty through an area that needs material.

Benefit:Cost Ratios Brush Creek Hydraulic Model MO River Levee Projects Turkey Creek Flood Control Project Corps of Engineers Criteria: Benefits must Exceed Costs B/C > 1

Rest Area Accident Study Traffic Study by Larry Frevert, Traffic Studies Engineer in 1973 Evaluated Interstate Highway Traffic Accidents, 20 miles preceding and 20 miles following a rest area Findings: Statistically fewer traffic accidents occurred in the 20 miles after rest areas than the 20 miles before rest areas Conclusion: Rest Areas promote highway safety - Retain Rest Areas or Build Additional Ones!

Lake City Arsenal Pavement Condition Study

KCMO Wheelchair Ramp Estimates

Sidewalk Data Collected, KCMO Block Containing City Hall

KCMO Wheelchair Ramp Estimates

Olathe CMP Assessment Project

Decision Making with Limited Data Estimating System Conditions from Small Samples Hypothetical Example (45,000 LF of Curb City- Wide): 3% Sample (1,350 LF of Curb Inspected) Identify “Issues” in 3% Sample (600 LF of Curb Deteriorated) Divide “Issues” by 3% (Estimate of 20,000 LF of Curb Deteriorated) Estimated System-Wide Value (Apply $ per LF of Curb Replacement from Recent Project Bid Tabulations)

Terminology of Interest Statistical Significance Order of Magnitude Estimates Planning or Budget-Level Estimates Project-Level Estimates

Managing Work Performance Standards Identify Work Crew Size Estimate Necessary Equipment Usage Estimate Necessary Materials Required Measure Work Accomplished Compare Work Accomplished per Unit of Input (Work Days) to Established Standards Adjust Standards for Variations

KCMO Street Maintenance Activity Guide

Reporting Work Accomplishment Inputs: # of Crew Members Quantity of Material Used Miles or Hours of Equipment Usage Outputs: Lane Miles of Street Resurfaced Linear Feet of Storm Drain Pipe Cleaned Cubic Feet of Sewage Treated Outcomes Pavement Condition Index Reduction in Drainage Complaints per 1,000 Citizens Percentage Increase in Sewage Utility Fees Collected

Ten Things You Always Wanted to Know About Data Driven Decision Making ! 1. If you’re not using data to make decisions, you’re flying blind! 2. This is all about process, not a specific technology 3. Get ready to feel threatened 4. You will be spending more money, not less 5. Data-driven decision making (D3M) does not save time

6. Your data’s cleanliness is next to Godliness 7. Don’t shoot first and ask questions later 8. A good D3M decision is one you can afford to change 9. Your first D3M decision is just the beginning, because 10. D3M is highly addictive Ten Things You Always Wanted to Know About Data Driven Decision Making! Pamela Wheaton Shorr – “Scholastic Administrator,” September 2003

Group Exercise As a team: 1. Agree upon a significant public works effort that could benefit from data driven decision making 2. Document what data will help you better decide your work effort and plan the work 3. Outline how the data will be collected 4. Describe how the data will be analyzed 5. Define how the data will be presented for decision making 6. Identify to whom you will present the data 7. Specify how you will document the effort to accomplish the public works effort selected