ECU Data Retreat 2022

Published May 23, 2022 by
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  • Focus topic: Health Sciences data
    • Sub-topics: Data management and storage; Health Sciences registration processes; Data standardization and training needs; Educational, clinical, and research data intersections.
  • Planning committee: Erin Beaman, Tarrick Cox, David Eldridge, Rich Franklin, Greg Harris, Beverly King, Kelly Lancaster, Jhojana Infante Linares, Zach Loch, Jo Anne Murray, Russ Price, Otto Rehfeld, Kerry Sewell, Gaye Sexton Tennison, Jed Smooth, James Ward, Carla Williams
  • Dates: February 21 to 25, 2022
  • Location: Virtual (Three-hour opening session on Monday followed by 1.5-hour sessions on subsequent days.)
  • Keynote speaker: Zach Loch
  • Sample guiding questions:
    • From what systems, besides Banner, do you have to pull data for reporting? Do these systems have to be updated independently?
    • In your perspective, what percent of your data are in Banner vs. other systems?
    • At a high level, how does data flow in your unit?
    • What are your pain points related to registration and other student-related processes (e.g., withdrawal, recording LOAs, re-entry into curriculum, reporting – specifically for accreditation)?
    • What is the status of communication between your unit (or other units) and students about registration processes? What are the ways it could be improved?
    • Have you developed data dictionaries in your unit?
    • What are the standards for documenting business processes in your unit, and who provides oversight for that? How are those shared? How often are these revised?
    • Where do you think educational, clinical, and research data intersect?
    • Who (or what office) do you reach out to for gathering data for each area (educational, clinical, and research)? Who “owns” these data and is responsible for entering?
    • What are the major issues encountered when collecting data? When cleaning data and generating reports?
    • In regards specifically to clinical trial data: How do you define a clinical trial?   What are the major barriers to clinical trials data access that you encounter?
    • How are data management plans developed and are there barriers to operationalizing them? How can data processes be streamlined?
  • Expected outcomes:
    • Identify factors that make HS processes different from those on main campus.
    • Become familiar with issues related to reporting institutional data to UNC-SO, system definitions, and the funding formula.
    • Understand what data governance is and the data governance structure at ECU.
    • List ways to manage change and resistance to change at the institution, especially concerning data processes and quality.
    • Understand, at a high level, how educational data flows on the HS campus.
    • Determine if there is a different or better way to have data make its way into SIS/Banner.
    • Identify possibilities of how ECU’s data warehouse (ODS) can be used to store HS data.
    • Identify opportunities for more effective and efficient data management and storage that are good candidates for follow-up actions.
    • Identify software/apps that must be maintained due to lack of integration with Banner/ODS.
    • Identify pain points in registration and other student-related processes (e.g., withdrawal).
    • Describe the mismatches between the HS and main campus calendars and the impact on data/processes.
    • Identify the current workarounds due to mismatches in academic calendars and how to become more efficient in this regard.
    • Determine the efficacy of current communication within/between/external to units responsible for student registration processes.
    • Identify lessons learned across ECU that may improve performance in other units.
    • Identify programs, systems, and tools used at HS. 
    • Evaluate data/software-related training needs at HS.
    • Determine how to utilize ECU student identifiers (e.g., Banner IDs, Pirate IDs) across platforms.
    • Identify how units document their business processes for using data platforms and standard operating procedures they have in place to train new users on these processes.
    • Identify what data points are currently recorded and reported regarding research and clinical data.
    • Identify opportunities to centralize reporting in these areas. (For example, clinical data are recorded in three different systems. Are there opportunities to centralize?)
    • Determine how student involvement in research is recorded and tracked.
    • Identify where the intersections occur among education, research, and clinical data.
  • General areas identified for further action:
    • Duplicate IDs generated for students applying through WebAdmit
    • Data integration using APIs
    • Clinical data hub
    • Process & resource documentation
    • Listing/mapping of data systems
    • Resolving BSOM registration and academic calendar conflicts
    • Better tracking of student leaves of absence
    • Creation of data dictionaries
    • Data standards & efficiencies
    • Governance of health sciences data