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  1. 1. DAX TRAINING Randy Bowman
  2. 2. DAX Overview (Module 1) Improving Decision Making with Data
  3. 3. Module 1: Agenda • What is DAX? • Why use DAX? • How does DAX work? • What data is in DAX? • What reports are in DAX?
  4. 4. WHAT IS DAX?
  5. 5. Purpose The Data Access and Exchange (DAX) System is a system that facilitates the Alabama Community College System in the collection and distribution of accurate data in an organized and timely manner DAX is a data mining and data reporting tool.
  6. 6. History of DAX • Initial discussions - 2002 • Formation of DAX Steering Committee - 2004 • Data/file definitions - 2004-2006 • Initial development - 2007 • Beta testing - 2008 • Full implementation of collection - January 2009 (Fall 2008 data) • Migration from relational data structure to analytical data structure – 2013 to present
  7. 7. Initial Goals • Promote sharing of aggregated data • Promote exchange of information • Standardize reporting processes (ACHE, IPEDS, etc.) • Provide reliable, valid, real-time data to decision makers • Create a system-wide data warehouse
  8. 8. WHY USE DAX?
  9. 9. Why use DAX instead of internal data? • DAX data is “frozen” each term. Institution data may change. The frozen data allows better benchmarking. • Reports are vetted by functional leaders as “best practices.” • This is the data that is reported to ACHE, NCES, and OVAE. • Visualize state-wide trends and connect with colleagues of similar nature. • Understand your institution in context of the system.
  10. 10. Changing the culture of how to use data Culture of Assessment Culture of Performance Data collected end of term or year Data collected quarterly, monthly, weekly Data is a means unto itself Data is a means to an end Data used for accreditation or end-of-year reports Data used for decision- making and improvements Many measures Few key measures Aligned with professional interests Aligned with strategic priorities Reactive / Inactive Proactive VS
  11. 11. HOW DOES DAX WORK?
  12. 12. The Components of DAX • Data Collection System • The Validation Engine • Reporting Website (https://dax.accs.edu) • Data Management Website (https://ddm.accs.edu)
  13. 13. Data Collection • Colleges enter data into local administrative systems • Administrative systems generate files daily • Every night, DAX picks up data from colleges DPE has access to fresh data daily
  14. 14. Data Validation • DAX processes files against validation routines • DAX generates error reports • Once a week, DAX sends error e-mails for each file to persons as designated by each college (weekly on Sunday) • After mid-term, President of institution gets a weekly error email on Tuesday
  15. 15. Data Validation • Validated for sanity • Validated for conformance to Board Policy and Guidelines • Validated for conformance to other business rules Goal of accurate and valid data in a timely manner met.
  16. 16. WHAT DATA IS IN DAX?
  17. 17. DATA Warehouse A central repository of “loosely related” databases. Non-CreditCredit Adult Ed. GED Testing ATN Human Resources
  18. 18. Data Repository • Credit Student Data (including Schedule data) • Human Resources (Personnel) Data • Financial Data • Adult Ed • GED • Non-Credit Activity Available Databases Databases to be Added
  19. 19. Personnel Data – 3 Tables • PER – contains demographic, descriptive, and summary data on all personnel paid by the institution. • JOBS – contains details about each job and/or contract of an employee. • JOBACCTS – contains accounting information for each job and/or contract for personnel paid by the institution.
  20. 20. Student Data – 9 Tables • STU – contains demographic and academic information on all credit students enrolled in the reporting term. There is only one record per student. • SPECPOP - contains a separate record for each special population associated with a student enrolled for the reporting term.
  21. 21. • ASSESS – contains pre-placement scores for registered students • AWARD – contains awards conferred by institution to students • FINAID - contains a list of all financial aid awarded to registered students for the reporting term.
  22. 22. Schedule Files • SCHMST - contains detailed information for each credit course section in which students are enrolled at the institution for the reporting term. • SCHDET - contains a record of every meeting day and time combination for each credit course section in which enrollment exists for the reporting term.
  23. 23. • SCHINS - contains all instructors teaching any portion of a credit course section for the reporting term. • REG - contains all credit courses for which a student has enrolled for the reporting term. There will be one entry per student per enrolled course. The table will include the grade earned for each course.
  24. 24. DOCUMENTATION AND VALIDATION DATA Hands On Demonstration
  26. 26. Questions and Comments Randy Bowman Acting Director of the Information Technology, Data, Planning and Research Division (334) 293-4542 randy.bowman@dpe.edu
  27. 27. DAX Operations (Module 2) Best Practices to Improving Data Accuracy and Timeliness
  28. 28. Module 2: Agenda • User Types and Roles • Error Emails • Interpreting Error Reports • Reporting Deadlines • Affidavit Signing
  29. 29. User Types and Roles • President • DAX Data Verifier • Data Maintenance • Data Access • Report Detail Access • Report Access • Error Email Access
  30. 30. Error Emails • Emails sent each Monday at 7:00 AM • Click on link in email • Enter the Pickup Code • Print the list of validation errors
  31. 31. Error Report • My Data Overview • Click a table that has errors to see table details • Click Errors button in table footer – Select a row – Read the error in the sub-table – Use definitions to determine best course of action • Alternatively, click Printable View of All
  32. 32. ERROR REPORTS AND INTERPRETATION Hands on Demonstration
  33. 33. Reporting Schedule • Data may be used throughout the term, but labeled “as of <date>” • Important to keep errors to a minimum at all times, not just end of term • Term data is collected for terms using the following dates: – Fall term data August 15 – January 15 – Spring term data January 1 – June 30 – Summer term data May 1 – September 15 • Term data is “frozen” (not picked up and processed) when the affidavit is signed or on the last date of that term’s collection • Class start and stop dates should fall between: – Fall term July 1 – December 31 – Spring term December 1 – June 1 – Summer term April 1 – August 30
  34. 34. DAX Affidavit Dates • DAX Affidavits may be generated and signed between the following dates: – Fall term data December 15 – January 15 – Spring term data May 15 – June 30 – Summer term data August 15 – September 15
  35. 35. Affidavit Signing Best Practices • 3 weeks before due date, all errors should be cleared. • 2 weeks before due, generate affidavit begin verification process. – Generate Affidavit – Print copy for each functional user – Highlight data that functional user should confirm – Send to functional user with due date of confirmation – Goal: All data confirmed 1 week prior to due date • 1 week before date, generate affidavit and route to President for approval.
  36. 36. Questions and Comments Randy Bowman Acting Director of the Information Technology, Data, Planning and Research Division (334) 293-4542 randy.bowman@dpe.edu
  37. 37. DAX Governance (Module 3) Improving System-Wide Data Accountability
  38. 38. Module 3: Agenda • What is the DAX Steering Committee? • How are reports added to DAX? • How are validations added? • How are differences between local reports and DAX reports resolved?
  39. 39. DAX Steering Committee • The DAX Steering Committee is in charge of all DAX functions including data standards, validations, reports, documentation and notification of changes to DAX contacts • DAX Steering Committee Members: – Mr. Randy Bowman (System Office) - Chair – Mr. Tim Carter (Gadsden) – Ms. Jamie Glass (Lawson) – Mr. Anthony Hardy (Jefferson Davis) – Ms. Linda Hodges (Enterprise) – Ms. Angie Stone (Northwest-Shoals) – Ms. Lisa Stephens (Bevill) – Ms. Linda McIntosh (Jefferson State)
  40. 40. DAX Steering Committee Goals • To ensure data provided for reports from the DAX database is timely and accurate • To ensure false errors are eliminated from validation procedures • To ensure proper communication between ACCS and Alabama Supercomputer Authority • To provide training on DAX policies/procedures and usage • To provide assistance with data definitions and review reports to be generated from DAX data
  41. 41. DAX Report Process • A need for a new report is identified • A mockup of the report is designed • Data elements needed are determined and defined for the programmers • The report specification is scrutinized by the committee and given to ASA programmers • Programmers create the report • Steering Committee vets the results of the report prior to releasing it
  42. 42. New Validation Rule • A need for a new rule is identified • The rule is written in plain English by the committee • The rule is pseudo-coded by committee • The rule specification is scrutinized by the committee and given to ASA programmers • Programmers create the rule • Steering Committee tests the rule
  43. 43. Problem Resolution Process • Each school has a DAX Primary Contact • Primary Contacts are single point of contact to the DAX Steering Committee and the System Office • E-mail daxhelp@accs.edu for – Questions/concerns – Add/remove/change requests to validation codes – Validation issues
  44. 44. Troubleshooting a report • Two reasons a DAX report might not match a locally produced report 1. The “logic” used might be different 2. The data used might be different
  45. 45. DAX Logic Right & DAX Data Right Local Report must be wrong DAX Logic Right & DAX Data Wrong Determine root cause of incorrect data & fix DAX Logic Wrong & DAX Data Right DAX report will be fixed DAX Logic Wrong & DAX Data Wrong DAX report fixed and root cause of incorrect data fixed
  46. 46. Questions and Comments Randy Bowman Acting Director of the Information Technology, Data, Planning and Research Division (334) 293-4542 randy.bowman@dpe.edu
  47. 47. DAX Reports (Module 4) Using the DAX Reports
  48. 48. Module 4: Agenda • What reports are available in DAX? • How do I understand what the report means? • How can I use these reports to make decisions?
  49. 49. USING PREBUILT REPORTS AND EXCEL Hands On Demonstration
  50. 50. Questions and Comments Randy Bowman Acting Director of the Information Technology, Data, Planning and Research Division (334) 293-4542 randy.bowman@dpe.edu
  51. 51. DAX and Outside Agency Reporting (Module 6) Using the DAX System to complete IPEDS Surveys, Perkins Reports, and ACHE
  52. 52. Module 6: Agenda • ACHE Submissions • IPEDS Surveys • Using Excel to compare local data to DAX data
  53. 53. ACHE State Student Database (SSD) • ACT 96-509 (Alabama Code 16-5-7) requires reporting of unit record data to ACHE SSD • Every term data is pulled from DAX and submitted to ACHE SSD • System office “locks” the data • Institutions are required to confirm the data
  54. 54. ACHE Graduation Database • Annual submission of awards granted by student. • Summer Year 1 – Spring Year 2 • Pulled from DAX Award file for Summer Year 2. • Should match the IPEDS Completions Survey. • Best Practice Alert: Run the Award Summary by Program CIP Code (DAXAWARD-003L) and IPEDS Completion report and compare totals during the Summer Term.
  55. 55. IPEDS Surveys – Race & Ethnicity • Race/Ethnicity Calculation – Count all Non-Resident/Alien, regardless of race/ethnicity – Count all Hispanic ethnicity, regardless of race – Count for each race • DAX treats the criteria as 3 different fields
  56. 56. IPEDS Survey/Perkins Report - Gender • Neither report usually has a place for “Unknown” • Must try to capture gender on every student and faculty member, even if they refuse to self-identify
  57. 57. IPEDS Surveys - Exclusions • Exclusions are lists of students/personnel that were excluded from the DAX Report because DAX did not have enough data to properly categorize the person. • DAX is “smart” enough to realize that the person needs to be counted, but the missing data prevents DAX from knowing which part/column the person is to be reported. • These people should be reviewed and appropriately placed in the IPEDS Survey.
  58. 58. Getting Details • IPEDS Reports – Use “Show Query” and look for the “Backing Query” • Perkins Reports – Click the “Get Details” link above each column
  59. 59. Using Excel to compare detail lists • Import details from DAX • Import details from local administrative system • Use the MATCH(), ISNA(), and NOT() functions • Filter list as appropriate https://www.youtube.com/watch?v=58RrUXr_SGI
  60. 60. Best Practices • Run reports DURING the terms which are going to be included in the report – This gives you time to see EXCLUSIONS and fix them before DAX Freeze dates • Know which data fields are used as “decision” points and pay careful attention to those • Start preparing IPEDS and Perkins reports early • Have a deep understanding of IPEDS Definitions • Watch the tutorials provided by AIR every year prior to starting the surveys
  61. 61. Questions and Comments Randy Bowman Acting Director of the Information Technology, Data, Planning and Research Division (334) 293-4542 randy.bowman@dpe.edu
  62. 62. Custom Queries in DAX (Module 5) Using the DAX Query Page
  63. 63. Module 5: Agenda • Querying Data (SELECT statement) • Joining Tables • Using Functions • Using subqueries
  64. 64. USING THE QUERY PAGE BASIC SQL Hands On Demonstration
  65. 65. SQL Resources and Tutorials • http://www.myassignmenthelp.net/basic-structure-of-an-sql- query.php • http://www.firstsql.com/tutor2.htm • http://www.w3schools.com/sql/ • http://sqlzoo.net/wiki/Main_Page • https://www.khanacademy.org/computing/computer- programming/sql
  66. 66. Questions and Comments Randy Bowman Acting Director of the Information Technology, Data, Planning and Research Division (334) 293-4542 randy.bowman@dpe.edu