The Value of Running on a Longitudinal Data Structure

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Presentation transcript:

The Value of Running on a Longitudinal Data Structure Byrd, Gernhardt, Nolan-Abrahamian, White August 1, 2017

Agenda Motivation Implementation Outcome Application Considerations Aspirations Motivation (Jack) Implementation (Jack) Outcome (Aaron) Application (Rafi) Considerations (Steve) Aspirations (Aaron) Q/A (panel)

Motivation Disparate transactional systems, CSVs and/or Excel Lack of data direction Time cost in data compilation from multiple sources

Motivation Inconsistent data structure and quality Frequently recreating reports Unreliable key matching Lack of consistent field values

Motivation Instability in data sources and reporting over time Transactional system change destroys longitudinal reporting capability Report recreation in the transactional systems

Implementation 1998 – RFP for data warehouse 1999 – Implement Complete Data Warehouse (CDW) from eScholar 2000 – Data Warehouse goes live 2000+ – Expanded data and reporting capabilities 2000 – incorporates student data going back to 1993-1994

Outcome Common data structure Common reporting platform Facilitated data cleanup through standardized ETL (Extract, Transform, Load) plans Longitudinal data despite 4 different Student Information Systems

Application (School Reporting) Centralized location for demographic, attendance, discipline, and assessment data

Application (School Reporting) Allows for comparisons across years/transactional systems

Application (Evaluative Reporting) State/federal reporting determines data needs Departments maintain program records Accountability report serves as outcome measures Traditional FWCS Evaluation Program Evaluation with a Longitudinal Data System Department priorities dictate collection and evaluation plan Program participation and outcome measures stored in data warehouse Centralized impact report and/or department dashboard measuring program effectiveness

Application (Evaluative Reporting) Teacher Evaluation System Longitudinal data system serves as host for initial student-teacher linkage Historical assessment data leads to growth band creation for formative literacy assessments District created growth measures are used for teachers in non-tested subject areas

Application (Predictive Analysis) Allows administrators ready export of student-level data

Application (Predictive Analysis)

Considerations Potential for overabundance of reports and reporting platforms (transactional and data warehouse) Report identification, organization and management Customizable Data Security Process and implementation needs to be supported from top-down Data Extraction and Data Analysis expertise * Easy Accessibility to clean data (firehose effect) * Consistent usage of reporting guidelines based on user accessibility and familiarity * Old habits die hard

Aspirations Data modeling Business Intelligence (SSAS) Enhance data analysis Unified dashboard (report management) Power User access to clean, pre-joined data sets Row-level security

Questions