The Value of Running on a Longitudinal Data Structure


August 30th, 2017

Did you miss our presentation with Fort Wayne Community Schools (FWCS) at this year’s NCES Summer Data Conference 2017 in Washington, D.C.? Well, you’re in luck! Below you will find a detailed agenda from the presentation and the PowerPoint slides. If you have any questions, feel free to reach out to us so that we can connect with you!



Download our case study on how FWCS Informs Its Strategic and Daily Decision Making with Data.





The Value of Running on a Longitudinal Data Structure

 Byrd, Gernhardt, Nolan-Abrahamian, White

August 1, 2017



  • Motivation
  • Implementation
  • Outcome
  • Application
  • Considerations
  • Aspirations


  • Disparate transactional systems, CSVs and/or Excel

– Lack of data direction

– Time cost in data compilation from multiple sources

  • Inconsistent data structure and quality

– Frequently recreating reports

– Unreliable key matching

– Lack of consistent field values

  • Instability in data sources and reporting over time

– Transactional system change destroys longitudinal reporting capability

– Report recreation in the transactional systems


  • 1998 – RFP for data warehouse
  • 1999 – Implement Complete Data Warehouse (CDW) from eScholar
  • 2000 – Data Warehouse goes live
  • 2000+ – Expanded data and reporting capabilities


  • 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
  • Allows for comparisons across years/transactional systems


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


  • 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


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


Click here to download the slide deck. 

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