Preparing for Collaborative Data Driven Projects December 9, 2016 - - PowerPoint PPT Presentation

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Preparing for Collaborative Data Driven Projects December 9, 2016 - - PowerPoint PPT Presentation

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Preparing for Collaborative Data Driven Projects December 9, 2016 Lauren Hareem Erika Haynes Naveed Salomon Center for Data Science and Public Policy dsapp.uchicago.edu @datascifellows 1 University of Chicago Agenda 5 Minutes

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Preparing for Collaborative Data Driven Projects

December 9, 2016

1 Center for Data Science and Public Policy University of Chicago dsapp.uchicago.edu @datascifellows Lauren Haynes Erika Salomon Hareem Naveed

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Agenda

  • 5 Minutes Introductions
  • 20 Minutes Intro to Project Scoping (DSaPP)
  • 20 Minutes Intro to Data Maturity (DSaPP)
  • 20 minutes Intro to Data Governance (McClean) - How

to get legal agreements, stakeholder buy in

  • 20 Minutes Q&A
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38 projects

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“We are used to using data to justify funding decisions. Now we can use data to improve what we do.”

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This May Sound Daunting ...

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But Most Common Machine Learning Tasks...

Regression Using trends to predict outcomes Clustering Finding existing groups or categories Classification Labeling and sorting into groups

Dimensionality Reduction

Dimension Reduction Finding important predictors

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… You Actually Learned In Kindergarten

Regression Using trends to predict outcomes Clustering

Finding existing groups or categories

Dimension Reduction Finding important predictors Classification

Labeling and sorting into groups

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Most Projects Fall in a Few Categories

  • Early warning & intervention
  • Efficient resource allocation & targeted action
  • Effective advocacy & fundraising
  • Data-driven policy recommendation & evaluation

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Data are People

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“Predictive analytics is emerging as a game-changer. Instead of looking backward to analyze “what happened?” predictive analytics help executives answer “What’s next?” and “What should we do about it?”

Forbes Magazine Why Predictive Analytics Is A Game-Changer 12 Center for Data Science and Public Policy University of Chicago dsapp.uchicago.edu @datascifellows

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Project Scoping Workshop

dsapp.uchicago.edu 13 Center for Data Science and Public Policy University of Chicago dsapp.uchicago.edu @datascifellows

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“We are used to using data to justify funding decisions. Now we can use data to improve what we do.”

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Scoping a Good Project is Easier Said than Done

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What Makes a Good Project

  • A solvable problem.
  • A challenging problem.
  • An important problem with social impact.
  • A motivated, capable, and committed partner.

a. Domain/business resources b. Data understanding resources c. Commitment to implementation

  • Appropriate, relevant, available data.

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Relevance and Sufficiency

GOAL

Relevant but Insufficient Relevant and Sufficient Irrelevant and Insufficient

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Data Analytics: Problem Formulation

  • Identify Goals
  • Identify Actions you can take to achieve those goals

– Break down actions into fine-grained questions/subactions

  • Identify Data Sources Resources you need and have
  • Identify Analysis/Modeling that needs to be done

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Workshop

  • Scope an analytics-driven project for a problem your organization is facing

– Identify goal(s) – Actions (persuasion for example) – Data sources (data you have, data you need to collect, relationships to get that data) – Models

  • Who? (to target for each action)
  • What? (to say to them)
  • How? (to use different communication channels)

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Workshop: Identify Actions that can be taken to achieve the goal

  • What programs do I have access to? What would they do differently if they

had more information/knew where their interventions were most likely to be effective

  • Be sure to name out which individuals are taking the action

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Workshop: Data Sources

  • Data sources (data you have, data you need to collect, relationships to get that

data)

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Workshop : Changing Actions

  • Who? (to target for each action)
  • What? (to say to them)
  • How? (to use different communication channels)

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Partner Data Maturity Framework

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Data Maturity Framework

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How to Arrive at Data Nirvana

  • Commit to data-driven decisionmaking.

○ Data as a first class citizen ○ Ready for tough love from data ○ Willing to take data risks

  • Understand what data supports the mission and how.

○ Has the data it needs or can obtain it ○ Technical and organizational capacity ○ Outcome linked to action

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Data Readiness

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Relevance and Sufficiency

GOAL

Relevant but Insufficient Relevant and Sufficient Irrelevant and Insufficient

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Data Capture

population with data population of interest

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Data Storage / Format

.csv .json .xml

API

High cost of use Low cost of use

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Data Quality

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Integration

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Accessibility

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Documentation

DATA

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Organizational Readiness

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Leadership Buy In

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Individual Buy In

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Stakeholder Buy In

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Thank You! http://dssg.uchicago.edu/ http://dsapp.uchicago.edu/ DSSG 2017 Project Partner Applications Due 1/31/17!

Contact Us lnhaynes@uchicago.edu

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Case Studies

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Impaired Attention Lack of Motor Skills Learning Disability Memory Problems Hearing Loss Lower IQ

LEAD

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Constituents Elected Representatives

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Personal features and location

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