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1 Revising collections to reduce burden and increase data quality November 15, 2011 2:45 – 3:45 Kelly Barratt (CO) Kim Clement (IN) Kathy Gosa (KS)

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Presentation on theme: "1 Revising collections to reduce burden and increase data quality November 15, 2011 2:45 – 3:45 Kelly Barratt (CO) Kim Clement (IN) Kathy Gosa (KS)"— Presentation transcript:

1 1 Revising collections to reduce burden and increase data quality November 15, 2011 2:45 – 3:45 Kelly Barratt (CO) Kim Clement (IN) Kathy Gosa (KS)

2 2 Revising collections: CO Getting started on our revisions Catalyst: New Collection Framework 2009 SLDS Award – new collection system is a major component A perfect opportunity to revisit what we collect and why LEAs driving a move toward more transactional data collection paradigm Catalyst: Redundant Requests with Different Rules Data ElementTimesData TypesField Lengths Gender13Alpha Numeric Code 1 character 2 characters Grade Level9Alphanumeric Numeric Code 2 characters 3 characters Name16AlphanumericWide variation; sometimes collected as Full Name, others as individual elements

3 3 Revising collections: CO Goal: Improve Quality, Consistency, Support Longitudinal Analysis Objectives Streamline collections for the LEAs – ask for key data elements only once, or with consistent validations and business rules Shift resources from Data Administration to Data Analysis Be able to tie currently collected data to new longitudinal support systems (Teacher-Student Data Link, Standard Course Codes) Ultimately, make it possible for an LEA to send us a student-centric transaction record that supports our data needs Mechanisms Working with CELT to foster a culture of Data Governance, starting at the top Department program staff initiating discussions about combining related collections Organizational Change Management consultant as part of the 2009 SLDS grant, to help all stakeholders transition Education Data Advisory Committee (EDAC) – ongoing work to standardize data and minimize redundancy has proven fruitful already, even in our legacy environment

4 4 Revising collections: IN Reducing Data Redundancy & Improving Customer Service Catalyst: Funding cuts Complaints from schools and districts Goals: Remove burden of reporting unnecessary data from school districts Improve data quality Enhance data governance Critical Question: Where and when is each data field used? e.g., state reporting, federal reporting, accountability, legislative requirement, etc.

5 5 Revising collections: IN Results: Eliminated 45 redundant data fields Identified and eliminated unnecessary “offline” data collections Eliminated 10 collections (data no longer needed or not used; legislation sunset; data collected elsewhere) Critical Success Factors: From IT-driven to Senior Staff-led (whew!) Paradigm shift – Data isn’t an “IT problem,” rather needed for decision- and policy-making All staff buy-in

6 6 Revising collections: KS Background Data Collection revisions are part of our on-going process. Most collections have an annual cycle Project management (scope determines depth of PM) Master list of projects is maintained by IT, prioritized by program area and IT (issues escalated to executive leadership) Revision Process – continuous improvement! Iterative lifecycle process with requirements, design, development, testing, implementation LEA communication/training is part of every project Production issues addressed immediately; enhancements are documented in tracking system and considered at annual cycle Partner with LEA’s vendors (input, advanced notification, communication, certification/validation environment)

7 7 Revising collections: KS Reasons for Revising Collections User feedback [e.g., ease of use, clarify business rules, edits] Program area input [e.g., helpdesk calls, data quality issues, edits] Federal or state policy or data requirement changes [e.g., EDEN, state funding formula, educator evaluations] Master data management opportunity [student, educator, courses, organizations] Data Audit issue [students, assessments, educator] Technology advancements / issues [e.g., CEDS, development tool standardization (Foxpro .NET/SQL)] Strategic direction [e.g., early warning, teacher desktop productivity, grant initiative] Goals Meet federal and state reporting and data needs Enhance user experience and minimize data input Maximize data quality Support Master Data Management

8 Contact Info: Kelly Barratt, Barratt_K@cde.state.co.usBarratt_K@cde.state.co.us Kim Clement, 317.232.7536, kclement@doe.in.govkclement@doe.in.gov Kathy Gosa, 785-296-7931, kgosa@ksde.orgkgosa@ksde.org 8 Revising collections : Contact Info


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