32 ideas posted
What is a good first project for analytics? What are the top five reports to start with?
Submitted by Catherine 10 months ago
Submitted by James Brewer 10 months ago
Submitted by Kyle Johnson 9 months ago
Submitted by Rebecca 9 months ago
Submitted by Tom Dugas 9 months ago
I think admissions is always a good place to start. They tend to already have a culture in place of using data to make decisions, and if you can help them target their resources and increase their yield, they're all for it. Financial aid is another good one, although getting some financial aid staff to think about targeting resources rather than spreading it around evenly (which seems more "fair") can be challenging. Next for me would be academic success measures (i.e. early warning systems). For tuition driven institutions, these systems can make the difference between red and black ink, but getting all the offices and academics on-board for the more intensive data collection takes time.
As someone new to analytics, can you provide more details on how you might start helping admissions?
I have been interested in this for some time, and would agree on the comments below that this is not a "project" but an ongoing resource/service to a particular area. However, are there not some other analyses that are more project based (one-and-done)?
Love your reply Kyle! I was thinking that "projects" was the wrong word. You provided areas in the institution which have large amounts of data and would benefit from pulling that data together, providing it back in a form which can be easily analyzed. I think this points out the major benefit of "analytics" - the creation of a data-store which can be polled for data as needed.
You also hint at possible (standard) questions the departments may have.
The great thing about Metrics in this day-and-age is the large amounts of data generated (and much without human-involvement) which makes Analytics a timely topic!
Thanks Martin. Not only was I uncomfortable with the word "projects" but also with the word "reports." Both to me imply something static that will be done at some point. Analytics encompasses more than just inputs and outputs, and I think it's important to think about analytics from a process perspective for it to be helpful.
David, admissions is an area that it might be good to bring in an outside firm that specializes in the tools and processes for analyzing admissions data. One of the first things that can be done to help admissions is to focus on the right group of students. At one school we worked with a firm and discovered we were spending too much time with students who either were almost sure to come or almost sure not to come and not enough time on the ones in the middle. By focusing recruiting activities we were able to both build a better sized class and one that had incoming characteristics we wanted.
Actually a reply to David - you said, "However, are there not some other analyses that are more project based (one-and-done)?" and yes you are absolutely correct. As Kyle's example proves - once they knew the right (and wrong) groups to focus on, they wouldn't need to continue collecting and analyzing that same data-set. They may want to look at it occassionally (every 5 years?) to ensure they are still working with the proper group. They may also want to collect information to show how successful their new focus is...but that doesn't require "analytics."
In truth, your question about specifics makes me think you are more interested in developing metrics for the Admissions department than you are in identifying standard Analytics for the area. And this points out the complications with Analytics, Metrics, and the lack of a common language.
Well, I am actually struggling with trying to distinguish the difference between analytics and metrics myself. I am interested in this (don't have a degree in mathematics for nothing) and just trying to figure out where it is useful in higher ed.
(copying from a reply to a different thread)
We come together by agreeing on the common language to use. We come together by working together to create "standards" or "definitions" of terms and use them accordingly.
I use a taxonomy of:
1. Data - the smallest unit of measure. A number or value.
2. Measures - adding context to the data, but still low level
3. Information - adding enough context to make the measures useful. Answer a question, or tell a story
4. Metrics - combining information from different sources, adding prose to explain and interpret, wrapping it all together to tell a complete story - in response to a clearly stated root question.
And happily add Analytics:
5. Analytics - the analysis of data and measures to produce information.
This information will hopefully be used to:
1. Inform Decision making
2. Inform Direction setting
3. Provide information for the fulfillment of one or more Metrics
But that's just the beginning. We need standards for the measures we use for performance. We need to have a common language for "availability", "reliability", "customer satisfaction", "usage", and so on.
The answer is who, not what. You are better off identifying a group of users that want analytics and are willing to devote some time to develop them with you. Don't start with the areas that need analytics most or have the most robust data. Start with the people that can help you get something done.
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