Data Driven Advocacy TAKING OWNERSHIP IN CHANGE Old

  • Slides: 12
Download presentation
Data Driven Advocacy TAKING OWNERSHIP IN CHANGE

Data Driven Advocacy TAKING OWNERSHIP IN CHANGE

Old School Advocacy • Squeaky wheel gets the grease • Personal stories elicit sympathy

Old School Advocacy • Squeaky wheel gets the grease • Personal stories elicit sympathy • Relies on numbers • Based in subjective information • Identifies problems, not solutions • One to one • Years without change

Data Driven Advocacy • Based in objective facts • Personal stories still important but

Data Driven Advocacy • Based in objective facts • Personal stories still important but magnified • Requires numbers but easier to organize • Identifies solutions, not problems • One to many • Brings change faster

Example of Data Driven Advocacy

Example of Data Driven Advocacy

Accessible Parking Abuse with ‘old school’ advocacy • Based in anger and frustration •

Accessible Parking Abuse with ‘old school’ advocacy • Based in anger and frustration • Individual stories = sympathy • Short term solutions LE Patrols • One day Media events • • No long term change

Parking Mobility Data • Determined to show need for a solution Collect data •

Parking Mobility Data • Determined to show need for a solution Collect data • Build a solution from collected data • Demonstrate Results Build Solution Demonstrate Results

Spoke to Travis County Officials • Discussed the Problem • Discussed the National Data

Spoke to Travis County Officials • Discussed the Problem • Discussed the National Data • Offered a Solution • Officials Challenged Us 10 People • 10 Days • 843 Reports •

The Solution ENGAGE ENFORCE • Those Affected Get Involved • Trained Volunteers Issue Citations

The Solution ENGAGE ENFORCE • Those Affected Get Involved • Trained Volunteers Issue Citations EDUCATE • Offender Education • Community Awareness

Data Driven Solutions Gender Age Range of Offenders% 40 Not Supplied 1% Male 49%

Data Driven Solutions Gender Age Range of Offenders% 40 Not Supplied 1% Male 49% 35 30 Female 50% 25 20 15 10 5 0 Not Supplied Ages 16 -21 Ages 22 -30 Ages 31 -45 Ages 46 -65 Income Range of Offenders Pacific Islander Ethnicity of 0%Offenders 40 Not Supplied African American 2% Non-Hispanic Latino 9% 0% Other Asian 7% 5% 35 30 25 20 Hispanic 21% 15 10 5 0 Not Supplied <$10 k $10 -25 k $25 -50 k $50 -100 k >$100 k Caucasian 56% Ages 66+

Data Demonstrated Results ONE YEAR OF COUNTY-WIDE OPERATION • 175 Trained Volunteers • 5796

Data Demonstrated Results ONE YEAR OF COUNTY-WIDE OPERATION • 175 Trained Volunteers • 5796 Citations • 4192 Courses Completed • Volunteers’ Complaint

What Else is the Data Doing? DRIVING SYSTEMS CHANGE DRIVING PUBLIC OUTREACH • Educating

What Else is the Data Doing? DRIVING SYSTEMS CHANGE DRIVING PUBLIC OUTREACH • Educating Law Enforcement • Targeted Awareness • Targeted Enforcement • Educating Law Makers Addressing ‘knee jerk’ reaction • Driving Effective Change • • Educating Prosecutors & Judges Educational Alternative • Lower Dismissal • • Public Outreach Media Campaigns Physicians New Driver Education Social Media The Disability Community

What’s Next? Personal Emergency Management System

What’s Next? Personal Emergency Management System