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How to find/match student records in a post-SSN age?

How to find/match student records in a post-SSN age?. PCAT. SAT. GMAT. ESL. GRE. ACT. College Transcripts. TSI. High School Transcripts. Visiting Scholars. Law School Apps. Prospective Students. Undergraduate Apps. Graduate Apps. PCAT. SAT. GMAT. ESL. GRE. ACT.

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How to find/match student records in a post-SSN age?

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  1. How to find/match student records in a post-SSN age?

  2. PCAT SAT GMAT ESL GRE ACT College Transcripts TSI High School Transcripts Visiting Scholars Law School Apps Prospective Students Undergraduate Apps Graduate Apps

  3. PCAT SAT GMAT ESL GRE ACT College Transcripts TSI High School Transcripts Visiting Scholars Law School Apps Prospective Students Undergraduate Apps Graduate Apps

  4. PCAT SAT GMAT ESL GRE ACT College Transcripts TSI High School Transcripts Visiting Scholars Law School Apps Prospective Students Undergraduate Apps Graduate Apps

  5. PCAT SAT GMAT ESL GRE ACT College Transcripts High School Transcripts TSI Visiting Scholars Law School Apps Prospective Students Undergraduate Apps Graduate Apps

  6. PCAT SAT GMAT ESL GRE ACT College Transcripts TSI High School Transcripts Visiting Scholars Law School Apps Prospective Students Undergraduate Apps Graduate Apps

  7. PCAT SAT GMAT ESL GRE ACT College Transcripts TSI High School Transcripts Visiting Scholars Law School Apps Prospective Students Undergraduate Apps Graduate Apps

  8. What has led to this situation? • Receiving less information • SSN no longer used in many cases • DOB no longer used or only partially provided • Privacy Concerns : both individual and FERPA • Foreign student population increasing • Mobile society = more fluid data • Frequent address changes • Multiple email addresses • Multiple phone numbers

  9. Impact of this environment • Confusion when we have duplicate records • Time spent matching records when it becomes a manual process • Time spent merging records • Risk of overlay or mashing of student records • Violation of FERPA • Angry/upset student – legal consequences • Time spent “unmashing” student records

  10. Steps to ensure best possible matching success • Capture as much information as possible as soon as possible

  11. What information should we capture • Name – first, middle(not initial), last, suffix • Address – differentiate between permanent, local, foreign, mailing, separate components • Phone • Email address • Date of Birth – complete if possible (privacy concerns) • SSN – if available (privacy concerns) • Gender (privacy concerns) • Citizenship • Birth city/Birth Country • High School • College

  12. Steps to ensure best possible matching success • Capture as much information as possible as soon as possible • Normalize or Standardize your data as much as possible(preferably on input)

  13. Normalizing or Standardizing your data Name Address City St ZIP Phone ------ ----------------- ------------ -- ---------- --------------1 SMITH 2 E 13TH ST CHICAGO IL 60601-2407 (312) 458 99922 2 13 ST EAST CHICAGO IL 60601 SMITH3 SMTH 2 EAST 13TH CHICAGO LAWN IL 312-458-99924 SMITH 2 E THIRTEENTH ST CHICAGO IL 60601 458-99925 SMITH TWO EAST 13TH ST CHICAGO IL 60601 312-458-9991 Name Address City St ZIP Phone ----- ----------- ------- -- ---------- ------------1 SMITH 2 E 13TH ST CHICAGO IL 60601-2407 312-458-99922 SMITH 2 E 13TH ST CHICAGO IL 60601-24073 SMTH 2 E 13TH ST CHICAGO IL 60601-2407 312-458-99924 SMITH 2 E 13TH ST CHICAGO IL 60601-2407 XXX-458-99925 SMITH 2 E 13TH ST CHICAGO IL 60601-2407 312-458-9991

  14. Steps to ensure best possible matching success • Capture as much information as possible as soon as possible • Normalize or Standardize your data as much as possible(preferably on input) • Perform your search in an ever tightening approach but at a high level

  15. Steps to ensure best possible matching success • Capture as much information as possible as soon as possible • Normalize or Standardize your data as much as possible(preferably on input) • Perform your search in an ever tightening approach but at a high level • Compare results on as many attributes as possible and rank based on experience

  16. Attribute Comparison

  17. Record Comparison

  18. Steps to ensure best possible matching success • Capture as much information as possible as soon as possible • Normalize or Standardize your data as much as possible(preferably on input) • Perform your search in an ever tightening approach but at a high level • Compare results on as many attributes as possible and rank based on experience • When match is found then update where appropriate

  19. Steps to ensure best possible matching success • Capture as much information as possible as soon as possible • Normalize or Standardize your data as much as possible(preferably on input) • Perform your search in an ever tightening approach but at a high level • Compare results on as many attributes as possible and rank based on experience • When match is found then update where appropriate • Normalize and de-duplicate your data periodically

  20. The Future • New System Integrations • Cloud computing • Smartphone payment systems • National/State Identification • Health • Voter • Financial Aid • Visa/Immigration • Sharing login credentials (Social Networks)

  21. Questions? Barry McClendon Senior Software Developer/Analyst Office of Admissions – Information Technology The University of Texas at Austin bmcclendon@austin.utexas.edu

  22. Questions? Barry McClendon Senior Software Developer/Analyst Office of Admissions – Information Technology The University of Texas at Austin bmcclendon@austin.utexas.edu https://joind.in/event/sacrao2012 M2.08 - How to Find/Match Student Records in the Post-SSN Age

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