(real-time) data and analytics...learning disability memory problems hearing loss lower iq lead ......

#DataSmartSummit

THE VALUE OF DATA-DRIVEN DECISION-MAKING

RAYID GHANIDirector

Center for Data Science & Public

Policy

University of Chicago

@rayidghani

SUMMIT ON DATA-SMART GOVERNMENT #DataSmartSummitNovember 2017

Rayid Ghani @rayidghani

Data-Driven Decision Making for Governments

Rayid Ghani

Center for Data Science & Public Policy

Rayid Ghani University of Chicago @rayidghani

• Examples from the field

• Data Analytics Capabilities– What’s possible?

– What’s hard?

– What to watch out for?

• How to get started?– Identifying and Framing the problem

– Building a team

– Identifying data needs and maturity

– Building, Testing, and Deploying the solution

– Change Management

Agenda


Impaired Attention Lack of Motor Skills

Learning Disability

Memory Problems

Hearing Loss

Lower IQ

LEAD

Children in at least 4 million U.S. households are exposed to high levels of lead (CDC Report)

Preventing Lead Poisoning in Children (Chicago, IL)


50% fewer false positives20% more officers identified correctly

Reducing Adverse Police Incidents (Charlotte Mecklenburg, NC and Nashville, TN)


More accurately and 4 years earlier

Increasing Educational Outcomes in Schools (10+ school districts across the US)


11 million people move through 3,100 Jails

$22 Billion in costs

64 % suffer from mental illness, 68% have a substance abuse disorder44 % suffer from chronic health problems

In the top 200 predictions104 individuals went to jail in the next

year

19 years total jail time

Reducing # of people going to Jail (Johnson County, KS)


46% of the Mexican Population lives in poverty

~7.5 Million additional people can potentially be

better matched with services they need

Matching citizens with social services they need (Mexico)


Before:~400 Violations per 1,000

After:~750 Violations per 1,000

87% Improvement

Increasing Compliance with Environmental Policies (EPA, NYSDEC)


240,000 main breaks/yr in US

$13 billion in 2010 to repair

Expected $30 billion by 2040

180 breaks/yr in Syracuse, NY

64% of blocks in the top 1% of

predictions were correctly

predicted for last year

Preventing and Reducing Water Mains breaks (Syracuse, NY)


2.4 billion people don’t have sanitation facilities

946 million people defecate in the open.

2.5 million people die each year due to sanitation-related diseases.

Operate 2.5x more toilets with the same resources

Serve 45,000 more people

Increasing efficiency of waste pickup (Kenya)


27% of EMS incidents are under-dispatched

can get to the hospital faster

2,440 yearly

Improving the effectiveness of EMS Dispatches (Cincinnati, OH)


More details on projects at http://dssg.uchicago.edu/projects


Data Analytics Capabilities


Capabilities: What’s possible?

Types of Data

• Program Level

• Transactional

• Spatial

• Text

• Images/Audio/Video

Types of Business Problems

• Early Warning

• Prioritization/Resource Constraints

• Routing

• Scheduling

Types of Analysis

• Description

• Detection

• Prediction

• Optimization

• Behavior Change


• Building Inspections for Code Violations

• Getting 311 requests to the right department

• Targeting employment training opportunities

• Fraud Audits

• Placement of Mobile Clinics

Let’s work through some examples

Early Warning

Prioritization/Resource Constraints

Routing

Scheduling


• Anonymization (not De-Identification)

• Incorporating Social Media effectively in to analytical systems

• Dealing with Bias and Fairness

• “Explaining” the Analysis

• “Deep Learning”

• Artificial Intelligence

Capabilities: What’s hard? hype?


• Data Systems

– Can I export data out of it?

– Can I put more data in?

– Can I link data?

• Analytics Tools

– Can I take action based on it?

– How does it fit into my workflow?

– How does it compare to simple baselines?

– Can’t Watson do it as well?

Capabilities: What to watch out for?

Time for a Break

When we come back, we will focus

on how to get started:

● Framing the problem

● Building/Structuring a team

● Getting the data

● Testing/Deploying the Solution

● Change Management


• Define a concrete problem and a specific goal you are trying to influence

• Identify whether you have a real opportunity to influence that goal

– What actions can you take?

– What data do you have and will need?

– What analysis will need to be done and how will you validate the analysis?

• Ensure you have a champion with the authority to move resources and set expectations for participation and collaboration

Getting Started: Framing the Problem


Getting Started: Building your Team



Skills

Computer Science & Programming Statistics

Social Sciences

Experimental Design

Ethics & Legal Issues

Communication

Problem Formulation


Training Social Scientists in Computational Methods & Tools


The Eric & Wendy Schmidt

Data Science for Social GoodSummer Fellowship

Eric & Wendy Schmidt

Data Science for Social GoodSummer Fellowship

http://dssg.uchicago.edu

@datascifellows


Applied Data Analytics for Public Policywww.applieddataanalytics.org



Centralized

Distributed


Getting Started: Identifying the Data

Category Area Lagging Basic Advanced Leading

How is Data Stored?

Accessibility

Storage

Integration

What Data is Being Collected?

Relevancy & Sufficiency

Quality

Collection Frequency

Granularity

History

http://dsapp.uchicago.edu/resources/datamaturity/

http://dsapp.uchicago.edu/resources/datamaturity/


Getting Started: Building, Testing, and Deploying the Solution

Collaboration Tools

Privacy, Confidentiality, Security


• Go back to the metrics and goals defined at the beginning of the project

• Run a Pilot

• Deploy

• Set up Infrastructure and allocate resources to monitor “lift”




Security Privacy

Interpretability & Explanations

Fairness & Ethics

Critical Needs


• Ideally, the entire organization needs to be on board at all stages of the project

• Promote core practices and behaviors - Curiosity and Rigorousness

• Small Iterations – Not Big Transformations

• Metrics-Driven, but the right metrics

• Test, Test, Test

Getting Started: Change Management


From Open Data to

Open Policies


From Efficiency to

Personalized, Improved Services


From POLICIES to

policy policy policy policy policy


How are you going to use what you learned?

What’s Next?


Rayid GhaniCenter for Data Science & Public Policy

University of Chicago

[email protected]

Data Science for Social Good Summer Fellowship


Center for Data Science & Public Policy

http://dsapp.uchicago.ed

code at github.com/dssg

mailto:[email protected]


http://dsapp.uchicago.ed/


• Can I detect who’s going to get lead poisoning early?

• Can I determine which home inspections to prioritize?

• How do I improve the scheduling and assignment of my medics/ambulances/firetrucks?

• Can I route citizen requests more efficiently and effectively?

• Which policies do I modify to improve maternal mortality ?

• How much impact is my after-school program having?

• Can I get data that helps me match employers with employees ?

Problem TemplatesX

X

X

X

X

X

X

(real-time) data and analytics...learning disability memory problems hearing loss lower iq lead ......

Documents