Using Institutional Data: Policies & Best Practices Institutional Research & Reporting

IRR Online Resources Contains reports suitable for public distribution for updates to the website and reporting tips. Request access to the IRR SharePoint site (share.ou.edu/sites/irr) for additional reports that are not for public distribution. Privacy considerations – FERPA: protecting our students – Open Records: think about potential issues before creating new reports – Storing and sharing person-level detail files

Official or Operational? Operational Data – Also known as the ODS or Cognos or oZONE – Is (usually) no more than 24 hours old – Is a copy of the transactional system – Should be used whenever current status is important, such as advising, course scheduling, etc.

Official or Operational? Official Data – Is a “snapshot” of data as of a point in time, according to a predetermined schedule – Is currently only accessible through IRR – May differ from operational system if corrections are warranted – Is set up for reporting needs, using established definitions – OU, state, federal – that take important nuances into account – Should be used whenever reporting to the outside world and when comparing apples to apples is most important: best example, trend analyses

Census Dates Each “snapshot” that IRR takes is more generally referred to as a census. What are the standard census dates IRR uses? Official Reporting Date (ORD)  For student data, this is after the add/drop period has ended; in fall/spring it is the first working day after three full weeks of classes, summer is more complicated so ORD comes later at more like the 8 th or 9 th week  For HR data, this is the first working day on or after November 1 st (after October payroll) End-of-Semester (EOS)  After all classes have ended and all grades have been submitted – significant because Law gets 30 days to submit their grades, so there can be quite a lag between classes ending and the EOS snapshot  Only applicable to student data

Same Original Source, Different Outputs Banner ODS Banner IRR frozen/ snapshot data IRR reporting tables

So How Do I Know What to Use? Will the data (potentially) be seen by deans/provost/other administrators? Will the data be shared with external agencies, such as program accreditors or media (especially the student newspaper)? Is it trend data? If the answer to any of these is yes, then you need to either be using official data from IRR or have checked your methodology with IRR.

So How Do I Know What to Use? Are you pulling the data for day-to-day operational use? Are you needing information on students that reflects status as of today, with all recent record changes included? Sounds like operational data might be a better fit. It’s easier to identify what does have to be official data than what doesn’t.

Step by Step 1.Write down the question you’re trying to answer. (This may seem obvious, but a lot of people skip this step.) 2.Think about what data you might use to answer that question. (This is where the official vs. operational question is usually addressed.) 3.Bounce your ideas off a peer. (Even the most experienced report writers have to do this. You might not have thought of additional fields you need, or you might need to clarify rules about a kind of calculation. Or a report that does that might already exist in the common folders. It pays to check.)

Step by Step 4.Sketch out a sample output table. (This helps remind you of additional details you want to include.) 5.Think about how you might be able to check your results. If you’re not sure, give that friendly peer another call. (If you don’t have any idea of what the “right” answer looks like, you can’t evaluate the results you get.) 6.Write the report in Cognos. (Finally!)

List or Summary? Lists are good when: – Your reporting need calls for person-level detail – You want to see more clearly what your query is returning, while you’re writing/refining your summarized query Summaries should be used every other time. Why pull and summarize person- level detail when the query can roll it up for you?

Notes are Necessary Of equal importance to data quality: clear, consistent, and complete notes and labeling  When were the data pulled?  Where did the data come from?  Who/what is included/excluded, and why?  What kinds of calculations/transformations have been done with the data, if any?

Susannah Livingood