#RIUC13

For those of you who weren’t able to attend the 2013 Rapid Insight User Conference, we set a new record for most attendees and largest number of customer presentations. With two full days of dual track programming, the presenters covered a lot of ground. While we wait for some of the video recordings of customer presentations to be formatted, I thought it would be good to do a quick recap here. 

Mike Laracy, Data Geek (at right)

The conference opened with a keynote from our Founder and CEO, Mike Laracy, who talked a bit about the future of predictive analytics. With a mass public education on the value of analytics (from people like Nate Silver and Billy Bean, with a little help from Brad Pitt), as well as significant advances in data storage and processing power, a stronger need for predictive analytics is emerging. The market is shifting towards the view that more data access is better than restricted access, and that given the right tools along with access, smart people – data scientists – can turn raw data into actionable information. Given these changes, the data scientist – that’s you – will be in increasingly higher demand over the next decade and beyond, as will predictive analytics. 

The user presentations covered lots of different topics, and we’ve made all of their slide decks available here; I’d highly recommend checking them out. In addition to what’s there, I’d also recommend checking out some of the interviews we’ve done with customers on building campaign pyramids and using predictive modeling to drive fundraising efforts. The RI staff team also gave a few presentations,  including topics like Tips and Tricks in Veera, Techniques for Improving Your Predictive Models, and An Introduction to Reporting and Dashboarding with Veera.

Another thing worth mentioning is that we announced our partnership with Tableau to provide a complete solution for both predictive modeling and visualization. Now users can use Veera to clean up their data, Analytics to build their predictive models, and Tableau’s visualizations to turbocharge their presentations. For more information, check out our partner page.

I am now a scientist according to @RapidInsightInc. I feel super smart! #RIUC13
— Dustin Mayfield (@dlm0078) June 27, 2013

My favorite part of the User Conference has always been talking to customers about the cool data projects that they’ve been tackling, and this year was no different. Kudos to our users for being so creative and smart with the ways they use our software. We also owe a big thanks to the folks at Yale for hosting us, and to all who were able to attend. Here’s to the best User Conference so far and to making next year’s even better!

Campaign Pyramids: Brick by Brick

Recently, I got to chat with Chelsea Drake and James Dye, who are both Data Analysts at the College of William & Mary, about the work they've been doing on campaign pyramids. For a more in-depth look at the functions that their campaign pyramids serve, and their process for building them, be sure to check out their presentation at our user conference or stay tuned for a webinar rebroadcast in July. 

What is a campaign pyramid’s function in your office?

CD: Right now we’re using the pyramids as a donor-centric list of prospects. To give some background on the pyramids, we did a massive data mining project to determine where our donors’ interests were. The end result is a dynamic pyramid that updates as new gifts come in and as we get new information about where their philanthropic interest lie. We use them as accurate prospect lists.

JD: We had a bunch of people in our prospect pool and needed to know where their interests were. For example, if they’re into Athletics but graduated from the Business school, do we want to go after a split gift, or do we say that their primary interest is athletics, so they should be doing the ask? The pyramids help us decide which one we should try to raise money for. They also help to set goals for each department and each school. So we’ll set a goal and ask a question like ‘how many gifts do we need at different levels, and prospects do we need to make up that pool and reach our goal?’

How do you set the goals for each pyramid?

CD: We’re able create pyramids to test high, medium, and low goals to see which one is most feasible for each unit and each campaign overall.

JD: Each unit has three pyramids – they have a high goal, say 120M if 100M is the medium or mid-range goal, and a low goal, which might be something like 80M. The mid-range goal should be something they can accomplish without too much effort and the low goal is what we think they’d get if they only asked people we already knew. This allows us to see how much stretch we need to do and how many people we need to identify in order to hit certain monetary goals. The idea behind the project was to figure out where our prospect pool’s interests were and where we need to do work and identify new prospects to fill in gaps and holes.

What triggered your interest in campaign pyramids?

CD: We started last summer, our Assistant VP of Operations wanted to make sure we were being as donor-centric as possible. She knew we had some information on interests but that we didn’t have a reporting tool that identified which prospects should go with each interest. She knew I had an analytical background and that’s how she chose to bring the project to me.

JD: Previous pyramids had been done at the university level. For the college, we wanted to know who we had out there and how much money that would bring in with specific gift ratings. But we were also asking things like ‘How much can we get for athletics?’ and ‘Who are the people who are interested in athletics?’. That’s where it spawned into a donor-centric thing. We wanted to know what our donors’ interests were, what they’ve given to in the past, and on a program and unit based levels, who were the donors for each area.

Who builds the pyramids in your office? How did you decide that?

CD: James and I do, and that was decided based on our backgrounds. James has a programming and computer science background and I have a background in research and analytics.

JD: We’re the programming and analytic people in our office and were already working on data pools, but were brought onto this project based on our skillset. Within our department, we’re the ones who generally work with the data.

What’s your administration’s take on the pyramids?

JD: They like them a lot. It gives them an idea of monetary goals for each unit and school to stretch for and concrete lists of names. We can show them the people we’ve identified, and if we sum up all of things we have in a pyramid, we can see if the goal set for a department is realistic. It helps them to see who’s out there and who’s in our database. They also use it to present to a board of visitors in a slideshow on where we stand in a campaign and how our numbers are at any given point. They can tell how many people we’ve already identified and how many new people we need to identify to meet a goal.

What advice would you have for someone looking to undertake a project like this?

CD: One of the things that was really helpful for us as the project started was having a good relationship with IT to fine tune what the data files we get from them would look like. The key to doing this type of analysis effectively is to have the best data that you’re able to get from your system in the most consistent way possible. Also, you should absolutely plan out what your goals are for the project before you get started.

JD: You have to know which data points out there you can pull from and what would be relevant for your goal. Depending on the size of the school, you might want to focus on a single unit pyramid to narrow down the scope of what you want to do. You could start with a major gifts or annual fund pyramid, for example. It’s about first defining your question, then looking at the data to figure out which people to target and looking at the numbers to establish what your monetary goals should be. It helps to nail out a template of what you want the end result to look like before you start programming.  We knew what we wanted our end result to be, so then when we were programming forward, the question became ‘how do I fill out these blanks where these numbers should be?’. This way, when you start building, you’re able to visualize how to compile everything correctly according to your template. Also make sure that you have a good team working on the project, and that team members know what their role in the project is.

CD: Anytime you’re taking on a project like this, you want to have the ability to talk to the managers or executives of your department to make sure that your end result matches what they feel they need.

JD: Make sure it’s helpful for them. We’re numbers people. We can make a page full of numbers and look at it and understand it, but management might need something a little bit more nice looking. So the sheet we create for them outputs to a single page with colors so that when we turn it over to them, the information is logical and easy to read. It comes down to knowing your audience. 

Infographic: How One Small School Improved Student Retention

Here's how Paul Smith's College in upstate New York went about improving student retention:

Paul Smiths College and Student Retention | Create infographics

-Caitlin Garrett, Statistical Analyst at Rapid Insight

We'd love to hear about your strategies for tackling retention - what works, what doesn't, and what you'd like to try. Please feel free to share your perspective in the comments section :)

Rapid Insight at Ellucian Live

If you’re planning on attending Ellucian Live next week, we’d like to invite you to check out our session “Bringing Predictive Analytics In-House: A Case Study with Dickinson College”, co-presented by Michael Johnson, Director of Institutional Research at Dickinson College, and Michael Laracy, Founder and CEO of Rapid Insight. The session will take place on April 9^th at 10:50am in Room 204C.

As an Ellucian Community Partner, Rapid Insight provides Ellucian customers with predictive modeling solutions that are easy to implement. Join this session to see what is possible when you bring the right kind of predictive modeling in-house.

Attendees will discover how they can successfully use predictive modeling in all parts of the enrollment management process, including student retention, as well as to boost fundraising effectiveness.

We hope you’ll join our session and stop by Booth 102 to say hello!

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For those who aren’t attending Ellucian Live, be sure to check out our case study and webinar with Mike Johnson on his experiences with building predictive models at Dickinson College. 

Customer Webinar: Predictive Modeling for SEM

Our next customer webinar, "Strategic Enrollment Management: St. Michael's College and Predictive Analytics" will be given by Bill Anderson, CIO of Saint Michael's College today at 2pm EDT and will be re-broadcasted on Tuesday, March 26th, and Thursday, May 2nd. 

I got the chance to ask him a couple of questions about his session, which will describe the ways in which Veera and Analytics are utilized on campus to produce predictions and other analyses for the scoring team. 

What types of models have you been building?
Almost entirely enrollment management - mostly apply to enroll. We've been building them on and off for about five years now. I have someone on campus that I collaborate with and when we first started, she was using SPSS for the statistical analysis, but we've since abandoned that. 

How has model building changed your Enrollment and/or Financial Aid practices?
There have been a number of ways that we've used the models - one as a sort of verification of what our consultant has been doing, two to be able to do some sensitivity and what-if analysis (and suggest different practices or emphases on where the aid awards should go), and three to help confirm in-semester and in-process prediction on where the class is going to end up. 

In some occasions, this has impacted size of waiting list or the way we thought about awarding wait list spots, including the total number of admits. This last year, our model suggested that we could be more selective than we had been in the past. 

What do you hope attendees will learn from your presentation?
One thing is that you can do it on your own - it's not that hard. You have to have a background that supports responsible interpretation of the results, but you can sit down and do it. That's one element: just do it. I think there's another element that says once you start thinking this way, it can become infectious. In our enrollment management meetings, we have the opportunity to appeal to the data or look at a Veera job that identifies the applicants we could avoid accepting. This changes the internal conversation - from a culture of anecdote, you can change the conversation with data. The use of the products has been fabulous in terms of making the data accessible to people. 

Rapid Insight's 5th Annual User Conference

Let the countdown to the 5th annual Rapid Insight User Conference begin! Here’s what you need to know about this fun and informative event:

We are making one big change this year: we’ve outgrown our space here in NH and are hosting the conference on the campus of Yale University in New Haven, Connecticut. It will kick off at 9am on Thursday, June 27^th and wrap up by 4pm on Friday, June 28^th. The cost of the conference is $150 per attendee.  In addition to the presentations and hands-on labs, we’ll be providing a continental breakfasts and an evening reception to all registrants.

For User Conference lodging, we recommend the Omni New Haven Hotel at Yale. We have arranged a special rate of $169/night + tax available through 5/26. You’ll find the dedicated Conference link to guarantee this rate, along with additional travel information, on the official User Conference webpage.

Be sure to check the Conference webpage frequently for updates on specific sessions and activities as the date draws near. We look forward to seeing you there!

Thoughts from a Registrar

Dan Wilson, Registrar at Muskingum University, recently talked with us  about some of the reports he's been working on, how he's using Veera, and his upcoming webinar. 
CG - What types of reports are usually on your plate?

DW - Some of the reports I'll be talking about in the webinar include:

• historical registrations by date,
• historical majors by semester,
• number of students still needing to take specific general education courses, 
• graduation persistence by major, 
• retention rates by various factors, and
• DWF (drop/withdraw/fail) rates by course.

I have one report for each of these and I make minor adjustments to it each time a new question is asked. 

CG - How has Veera helped with your reporting?

DW - It's helped me develop complex reports that would have taken me 4-6 hours each to get all the data, build, and run. Now I pull up a report and run it in about a minute. It's especially useful for complicated and repetitious reports. 

All of the reports I've mentioned have been automated in Veera. Everything that I can, I automate. I anticipate that people might be asking for DWF rate for the first year students, or by division, or by course, or by phase of the moon. Veera is good at pulling that data together and allowing me to tweak it and make adjustments as needed. I usually choose to work with Veera when I think I'll see a lot of revisions, need to do some digging around, or can see similar questions being framed differently. 

The year-over-year registrations by date report was one of the first I created using Veera. It's proven to be one of the most valuable to our administration in helping improve our retention rates. It literally takes two minutes to run - it actually takes longer to download the data file than it does to run the report in Veera.

CG - What do you hope attendees will take away from your webinar?

DW - I hope each person will find something that is a spark moment for them. Some people need exposure to Veera and to see what it can do. For users, I'm hoping to spark a brainstorm on how and why to use Veera - as the way to achieve what they need faster and easier. 

CG- Anything else you'd like to add?

DW - Anyone who knows what a small college registrar does will understand that I wear many, many hats. Though I'm not an institutional researcher, some of the work that I do is borderline IR. Typically I'm asked a question and need to get someone the answer quickly. That's what I use Veera for the most. 


For more information or to register for Dan's upcoming webinar, Digging Deep into Data: How a Small University's Registrar Develops Complex and Repeatable Mission-Critical reports, click here. 

Subroutines (Customer Post by Tony Parandi)

Today's blog entry comes from Tony Parandi, Assistant Director of Institutional Research at Indiana Wesleyan University: 

One feature of Veera that I’ve found very helpful is the Subroutine node. This node allows you to feed the output of another job directly into the job you’re currently working on. In essence, it allows you to put a job within a job. This is especially helpful if you have a certain data stream that you commonly use, and do not want to rebuild it each time you need it.

An example that I commonly use the Subroutine node for is the recoding of student ethnicity. In 2010, when the Department of Education mandated the new ethnicity categories, we added two additional ethnicity/race fields to our data system (Datatel). Thus, students could have a wide myriad of ethnic category combinations, which means we have to reform these combinations to match the IPEDS definitions. In order to accomplish this I bring in the ethnic data from our warehouse, and use a series of transform nodes to convert the three ethnicity fields into one final ethnic category:

Rather than recreate this stream for every job, I have it saved as a separate job, called “Ethnic Conversion”. As you can see in the picture above, the Output Proxy node is necessary when creating a job for Subroutine purposes as it connects the output data to the Subroutine to the other job you’re building.

Now when I create a new job that needs ethnicity reformatting, I simply bring in a Subroutine node and connect to my data via Student ID in the Merge node.

The output gives me a single ethnic category that matches IPEDS for every student, based on data brought in via the Subroutine.

Although this is a small and simple example of a Subroutine job, the node is a powerful way to connect jobs without having to do copy/pasting or rebuilding. I have found the Subroutine node to be a great time saver, and I encourage everyone to use it whenever possible.

PS: Be sure to check out the rest of our customer tips series here!

Customer Tips From... Brian Johnson (DonorBureau)

Today's installment in our customer tips series comes from Brian Johnson, the VP of Product and Operations at DonorBureau. Here are his tips: 

1. You can have multiple select statements in the Query node as only the last statement does not have a temp table statement. This has allowed me to automate any complicated SQL queries I have for reports to run every week. 

2. If you need to create a new version of a scheduled report, use the old version and it will inherit the schedule of the original report in terms of run times. 

3. You can save output to Dropbox or Google drive to distribute data to team members without having to fill up their inbox. 

Customer Tips From... Dr. Nelle Moffett (California State University - Channel Islands)

The next installment in our customer tips series comes from Dr. Nelle Moffett, Director of Institutional Research at Cal State - Channel Islands. Nelle is an avid Veera user and loves building analytic processes. Here are her tips:

• Use a Rename node before an output to select only the fields that you want and re-sort them in the desired sequence.
• Use the Cleanse node liberally before any Transform node to remove or replace missing data. This will eliminate unexpected results when the Transform node encounters missing data. 
• To create your own variable labels, create a look-up table in Excel with the original values and the new value labels. Then merge this file with the original file using the field labeled "original" and keep the new field with the desired value labels. 
• Example of value file:

• To calculate the percent of a certain characteristic in the dataset, first use a Transform node to set a flag for that characteristic where 1= has the characteristic and 0= does not have the characteristic. Then use an Aggregate node and select the mean for a flag.
   
• To update a job to the current form of a dataset, first make the connection to the updated dataset. Then double-click on the data node in the job. At the bottom of the window where it says "connection" click on the name of the data file and select the new version. make sure all of the data fields are checked (that should be) and save the changes. If you give the data node a generic (rather than dated) name, then it will still be appropriate as the data continues to be updated to the current date. 

...Have tips of your own? Email them to caitlin.garrett@rapidinsightinc.com!

Customer Tips From... Scott Alessandro (MIT - Sloan School of Management)

The third edition of our customer tips series is brought to you by Scott Alessandro, Associate Director of Sloan Educational Services at MIT's Sloan School of Management. Scott is a long-time and avid Veera user who has been very creative with his applications of the software. Here are his tips: 

1. You are able to remap your data files by clicking on the connection icon either under the connections menu or in the job on the actual file. When I first started using Veera, I was afraid to move files around as it would break connections. Now I know better.  

2. Use the Find DeDup or Remove Dup node when you are working with a data file for the first time. You will be surprised how often duplicate records exist in data files (well, really we should not be surprised, but sometimes are).

3. With the output node, you can check or uncheck the columns you want to include. This is especially useful when you are using transform or merge nodes. I like to keep all of the columns throughout my job and then only select out the relevant ones in the output node. Helps you to keep track of what you are doing within a job (especially also if you create a ‘test’ output you move around the job).

4. Use ‘Set Run Order’ when you have multiple outputs in a job or a job that relies on one output to run another output. Akin to that, in the Merge node, you can also change the order that files are merged together by right clicking on the file number. Since I like to merge a lot of different files together, it is useful to be able to change the merge order especially if you add files later. 

5. Right click on a job to make a copy and then paste it onto the workspace.

6. In Cleanse, can multi-select columns and run the same type of cleanse, rather than selecting each column individually. 

7. Use the Rename node to re-order columns.

...Have tips of your own? Email them to caitlin.garrett@rapidinsightinc.com!

Customer Tips From... Dan Wilson (Muskingum University)

Our next set of customer tips comes from Dan Wilson, Registrar at Muskingum University. Dan typically uses Veera for repetitive and/or complex reports, including multi-year enrollment history by date, historical majors and minors (by year and department), and IPEDS reporting. Here are his tips:

1. It is important to remember the merge characteristics (all from a, all from b, all from both, only matching, etc.) so the last thing I do in developing any report is to verify each of these. 

2. While Veera's CrossTab feature is quite useful, I find it easier and more familiar to output my results to a target excel file, and then have another excel spreadsheet with my pivot table that has all of the formatting and other features set up. That way I can update the data file without overwriting my formatted "results" file. The same can be done with separate sheets in a file, but some of my reporting files pull data from different queries and Veera reports. For those reports I can run data from several sources, then open up my main file and hit "refresh". 

3. For those instances when a transform looks like a computer program, I'll break it into smaller bits and spread it out over several nodes. This allows me to test smaller chunks of the function at a time and locate any errant code prior to needing valium. (Editor's note: using the de-bugger in the Transform node can also help to find errors quickly!)

...Have tips of your own? Email them to caitlin.garrett@rapidinsightinc.com!

Customer Tips From... Dr. Loralyn Taylor (Paul Smith's College)

Our new customer tips series will feature tips from customers on using either of our software applications. Each entry will focus on one customer’s ideas to make your lives easier.

We’re kicking things off with Dr. Loralyn Taylor, from Paul Smith’s College. Dr. Taylor is a one-woman IR office and Registrar and is constantly looking for ways to save time when creating reports and executing jobs. Here are her five tips:

1.  Take the time to rename your nodes so that you can easily follow your line of thought as you move through the job.
2. Remember that there are multiple ways of doing things. The shortest is not always the best – to me it is often more important to be able to easily follow my thought on how I am working through the problem than to do it elegantly in the fewest number of nodes.
3. Common problems to check for: data format incompatibility (just use a convert node), and sometimes a null is not actually a null (just because something looks blank doesn’t mean that it is).
4. Remember that creating a job is like solving a puzzle; you have to think about it and play with it.
5. I often have to run jobs many times to get them right. Helpful tip: Set up a test data output that you can move around to different parts of the job to see how your data is coming through at different points when you are troubleshooting. 

...Have tips of your own? Email them to caitlin.garrett@rapidinsightinc.com!

UB Readers' Choice Awards

As you may know, University Business magazine is launching its first annual Readers’ Choice Top 100 Products award this year. This award will be given to 100 products used in higher education based on nominations from its readers. Winners will be selected based on both the quantity and quality of reader nominations, and will be featured in a special issue along with testimonials describing the impact each product has had.

Here at Rapid Insight, we're working hard to make data analysis as easy as possible for our higher ed customers. Whether that means data clean-up, reporting, or modeling, we hope we're making a difference in the way you work with your data. You've told us time and again how our products have saved your analytic life!  This is a great opportunity for you to tell others.  If we’ve made a difference in your analytic world we’d greatly appreciate your nomination.