1. Surfing the Data Standards: Colorado’s Path 2012 MIS Conference – San Diego Daniel Domagala, Colorado Department of Education David Butter, Deloitte Consulting LLP Zeynep Young, Double Line Partners

2. The SEA Data Challenge Accountability Increased and changing accountability demands drive up the cost and complexity of data collections. LEA differences LEAs have different source data systems. Even if on the same platform, different policies and practices complicate data collections. Data standards Education data standards efforts have not solved the problem. Difficult to understand and navigate the various standards efforts.

3. The IT Reality Many source systems Escalating technology demands Constrained resources Dependent on vendor cooperation Education data management is decentralized and complex 3

4. Colorado Situation CDE collects a wide variety of information from 178 LEAs to support different requirements: Monitor compliance with federal and State law, regulations and standards. Preparation of federal reporting requirements. Respond to State legislative and board of education data requests. Produce annual statewide summary publications. Determine if classes are receiving instruction from Highly Qualified Teachers. 22 different data collections at different points in the school year ASCII flat-file, fixed field formats are used for each collection

5. Complex submission and resubmission process for each collection Extract from source systems Verify the data Generate flat file formats Extract and format data Upload into temporary file Run preliminary edit checks Generate error report Upload data Load into staging database Run validations Generate error report Submit data Generate summary report Review summary report Approve data Run state-level validations Generate error report Close the collection Final validations Changes

6. Web Data Collection System Objectives Reduce the number of re‐submissions by an LEA for a given collection by 50% Reduce the overall number of collections by 20% Reduce data redundancy so that the total number of data elements collected is reduced by 20% Provide an extensible system to support future data collection technology Provide a technology that supports rapid data exchange and accommodates new data elements

7. Surfing the data standards Increasing Levels of Operational Specificity Provides users with a list of data elements to help ensure consistency NCES “Data Handbook” CEDS “Data Definitions” Ed-Fi “Data Model” Implementing Entity “Implementation Guide” Provides users a list of data elements with definitions and code sets, with a focus on the meaning of data stored in a SIS Provides users a model for exchanging education data Provides users documentation on how to use, adapt, and extend the model

8. Surfing the data standards Increasing Levels of Operational Specificity NCES “Data Handbook” CEDS “Data Definitions” Ed-Fi “Data Model” Implementing Entity “Implementation Guide”

9. Surfing the data standards Increasing Levels of Operational Specificity NCES “Data Handbook” CEDS “Data Definitions” Ed-Fi “Data Model” Implementing Entity “Implementation Guide” “Data Handbook” PLUS

10. Surfing the data standards Increasing Levels of Operational Specificity NCES “Data Handbook” CEDS “Data Definitions” Ed-Fi “Data Model” Implementing Entity “Implementation Guide” “Data Handbook” PLUS “Data Definitions” PLUS

11. Surfing the data standards Increasing Levels of Operational Specificity NCES “Data Handbook” CEDS “Data Definitions” Ed-Fi “Data Model” Implementing Entity “Implementation Guide” “Data Handbook” PLUS “Data Definitions” PLUS “Data Model” PLUS

12. Ed-Fi streamlines the data collections Funding, Budgets, and Actuals Student Information System (SIS) Operational Data System (ODS) Other District Source Data By continuously collecting fine-grained education data throughout the year, satisfying the various reporting requirements are isolated for maximum efficiency. District Source Systems & Raw Data Web Data Collection System Operational Data System Reporting This process is completed for each LEA Single, adaptable infrastructure Most new reporting changes can be handled without impact to the LEAs Ed-Fi XML Interchanges Ed-Fi Database Schema Federal reporting State reporting Legislative requests Board requests Annual summaries

13. Web Data Collection System Configurable File Formats for Collection and Extraction Business Rules and Statistical Validations Flexible Data Collection Methods Meta Data Management Customized Workflow Integration of COTS Reporting and BI Tools Security, Audit and Logon Management

14. We believe Ed-Fi is a positive and transformational force in using education data…. and our actions demonstrate our beliefs CDEChartering of bold project to implement Ed-Fi compliant data exchanges in an aggressive timeline. Commitment of significant CDE resources for business process redesign and capacity building. DeloitteMajor enhancements to Deloitte WDCS Solution to support Ed-Fi compliant data exchanges. DLP & MSDFCommitment of Ed-Fi subject matter experts are enabling rapid design of an Ed-Fi compliant data collection strategy for Colorado, in time for implementation as part of the WDCS Data Exchange Initiative.

15. For Colorado - Acceleration

16. Eden & EDFacts Reports Automation For Colorado and Beyond a portfolio of low cost and rapidly deployed Ed-Fi APPs that improve student outcomes in real time. Early Warning Systems High School Feedback Reporting Program & Financial Effectiveness Educator Effectiveness

17. Conclusions Ed-Fi brings the years of education standards activities to a point where CDE can implement and tangibly benefit from it. Fine-grained SEA data collection from LEAs should reduce overall data collection costs. Supporting data collection infrastructure needs to flexible for future extensions.