EOTSS: Data Sharing and Services July 18 , 2019
Agenda ■ Data Sharing Framework ■ Overview of EOTSS's Data Services ■ Key Products: ○ Data Prep / Secure Storage ○ Data Analytics ○ Data Visualization
Data Sharing
Data Sharing Problem Statement Challenge 1: Challenge 2: Data is not shared across state agencies in Confusion over rules and regulations limits a cost-effective, replicable manner. data sharing. Not sharing is the default. ■ Lack of clarity around what can be shared 287+ with whom ■ Unique data-sharing agreements No common process or support system for data-sharing 133 Days to create a data-sharing agreement (on average)
MOU New Legal Framework MOU: A statewide agreement broadly governing the sharing of protected data between Secretariats Data Use Licensing Agreement (DULA): An agreement between a data owner and a data recipient(s) specifying the details of how data will be shared for a specified purpose/project DULA DULA DULA
What’s in the MOU and DULA? Parts of a DULA: The MOU covers the following areas: Value Extraction Tech and Justification for ■ Governance: and Learning: Security: Data Sharing Framework for active Translation of Data Infrastructure Data Access/ ■ management of data into Actionable to combine and sharing Confidentiality Information protect data Data Transfer/Storage ■ Security Requirements/ ■ Purpose/ Question Data Security Legal Compliance Breaches Requirements under a D is ■ Term and semination Data Transfer DULA Termination
Data-Sharing Support: The Data Steward Council The Data Steward Council is a peer forum to support data-sharing. Assist with the Manage Mediate Provide general timely execution the MOU, disagreements support for data- of the DULA including the around data- sharing projects process, before addition of new sharing and after signatories signatures Reach out to your Secretariat’s representative at any point in the data-sharing process.
DULAs: The Process Recipient reworks question/problem or project ends NO Identify the question/problem to be addressed Owner or recipient initiates DULA online Identify the data and YES Can data owning agency be shared? Both parties complete/ review DULA online Reach out to the owning agency’s contact Both parties sign DULA online
Data Use License Agreements Data Sharing Resource Site What they are and how to execute them ■ Resources for Data Sharing Coordinators: The Data Steward Council ○ DocuSign resources for initiating/using DULAs Data Sharing FAQs Membership and ○ Quick access to the Data Sharing MOU mission ○ Listing of Data Sharing Coordinators Data Sharing Homepage ■ Resources for other data users: ○ Introduction to the Data Steward Council ○ Application to join the MOU ○ Instructional resources for signing DULAs Current Parties to the Data Sharing MOU ○ Data Sharing FAQs MOU Full text MOU and joinder List of parties and their Data application Sharing Coordinators
EOTSS's Data Services
EOTSS’s Data Services Data Sharing Data Analytics Open Data ■ ■ ■ Support the work of the Data Matching Data Sites Data Steward Council ■ ■ Integrated Data Systems Mass.gov open data ■ Manage the statewide platform (FY20) ■ Data Science/Analytics MOU ■ Machine Learning ■ Facilitate the electronic DULA system ■ Develop resources for Data Sharing Coordinators
Data Prep / Storage
Data Prep and Storage: Integrated Data System (IDS) Data Processing Flow Secure and Locked Down TSS Environment Sensitive Database Reporting Database Aggregations Agency Data #1 Matching Analytics Dashboard Agency Data #2 And Anonymized Anonymizing Data Agency Data #3 Staging Database Ingest Process Transfer Stage Report Pull data from agencies into Merge data and suppress Verify data is anonymized Store anonymized data and Deliver data results to secure environment identifiers prep for reporting stakeholders
Data Analytics
Analytic Processes : SNAP "Churn" Describe Predict Prescribe Which individuals are high-risk for What approaches are effective at How much unintended churn takes place in SNAP? unintended churn? reducing unintended churn? Expiring SNAP Months after Expiration Customers True Positives Feature #2 A E Thresholds Churn determined by O Predictor cost models Expiration Month Feature #1 False Positives Minimal Aggressive Engagement Engagement Stochastic Models Cohort Feature-Driven Prediction ROC Curves Analysis Some Engagement
Isolating Churn Hypothesis: Different Types of Behaviors Drive Return to SNAP 1 Late Recertifiers ■ Clients who knew to renew but engaged with DTA 1 too close to the application deadline ■ Clients who did not know they expired until they 2 prompted by no access to benefit Count ■ Clients who let their benefit expire but returned due 3 2 True Churner to life changes Hypothesis: Three different behaviors drive return cycles to SNAP 3 Pausers Time
Data Visualization
Data Story: TNC Rideshare ■ EOTSS partnered with Department of Public Utilities (DPU) to develop a data site for TNC rideshare (Lyft/Uber). ■ The site helps the state and the public better understand ride flows between municipalities and over time.
End
Data Steward Council Members Secretariat Member Administration and Finance Patrick Lynch Education Ann Reale Energy and Environmental Affairs Faye Boardman Housing and Economic Development TBD Health and Human Services Sarah Ricardi Labor and Workforce Development Michael Doheny Public Safety and Security Cliff Goodband Technology Services and Security Holly St. Clair (Chair) Transportation Rachel Bain Governor’s Office Michael Kaneb
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