Surfing the Data Standards: Colorado’s Path

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

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.

The IT Reality• Many source systems• Escalating technology

demands• Constrained

resources• Dependent on

vendor cooperation

Education data management isdecentralized and complex

3

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

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

Approve data•Run state-level

validations•Generate error report•Close the collection

Final validations

Changes

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

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

Surfing the data standards

Increasing Levels of Operational Specificity

NCES

“Data Handbook”

CEDS

“Data Definitions”

Ed-Fi

“Data Model”

Implementing Entity

“Implementation Guide”

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

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

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

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.

DistrictSource 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

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

We believe Ed-Fi is a positive and transformational force in using education data….and our actions demonstrate our beliefs

CDE Chartering 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.

Deloitte Major enhancements to Deloitte WDCS Solution to support Ed-Fi compliant data exchanges.

DLP & MSDF Commitment 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.

For Colorado - Acceleration

Data Warehouse(K-12)

External Data Files

ETL

Colorado Department of Education Information Management Systems

SLDS Strategic Objectives

Schools

District / LEA

Pre-K

Post-Secondary

Labor / Workforce

OperationalData Store(CEDS/Ed-Fi)

LINK

Real-Time(Transactional)

After-Time(Analytical)

CAPTURE

PERFORM

PROVIDE

Eden& EDFacts

Reports Automation

For Colorado and Beyonda portfolio of low cost and rapidly deployed Ed-Fi APPs that improve student outcomes in real time.

Early WarningSystems

High School Feedback Reporting

Program & Financial

Effectiveness

Educator Effectiveness

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.