Introduction As data is collected daily through the heavy use of computers and the internet, we continue to be plagued by the problem of dirty data. Data that is error prone or inconsistent leads to bad outcomes, such as misguided decision making (e.g. inaccurate...
More and more applications (software) are residing in the cloud. In the past, these applications were often viewed as black boxes. This means the end user sees an interface but has little insight into the processing that goes on within them. But that approach has been...
So far, we’ve taken a look at the top mistakes made during IPEDS submission and some steps you can take to reduce bad data entry during that process. But once that process is done, that’s it. Right? You’ve crossed the finish line. You’ve slain the beast! The job is...
Question: How many times have you, when reviewing IPEDS data, found basic data entry errors? Better question: How many times have you missed such errors prior to submission? The issues around data entry errors in IPEDS surveys are very real and addressing them isn’t...
The IPEDS submission process is typically not a smooth one. From accessing to collecting to validating to formatting IPEDS data, there are numerous stages where mistakes can – and often do – occur. We’ve assembled a list of some of the most common mistakes made during...
“Duck Season! Rabbit Season! IPEDS Season!” While duck seasons and rabbit seasons come and go, it always seems to be IPEDS season. So, we’re here to continue our examination of the IPEDS survey process. In part 1 we looked at the various challenges encountered in each...
“To everything there is a season.” Whether you recognize that line from The Bible or The Byrds, it’s a commonly known phrase with wide-ranging applications. For Institutional Researchers, the everything is their IPEDS surveys and the season is nearly year-round. Every...
There are multiple ways of assessing where your institution is at regarding data validation and data governance. In this discussion, we’ll look at data validation as it relates to maturity models. The maturity model is an assessment of the data governance program...
Higher education institutions need to trust their data so that they can engage in effective decision-making. But building trust is not easy. So, we figured we would provide you our ten steps to build this trust. 1. Data requests should be a conversation Communication...