In January 2017, I moved from a medium IR office to a one-person office; specifically, from Office of Institutional Research and Planning at The Ohio State University, to the one and only IR role for the College of Arts and Sciences at Ohio State. The College of Arts and Sciences (ASC) at Ohio State has nearly 20,000 students, which makes it larger than most four-year colleges in the United States. As the sole IR person in the College, I served as the data specialist in meeting data reporting and analytics needs of the entire college. It has now been more than five years, and I would like to share my experience. Instead of mentioning the leadership structure and pattern of reporting, I want to provide an overview of the changes over time. As shown in Table 1 below, the demand for data support was high since day one and growing fast. With increasing demand for service, I had to embrace the challenges and transform them. I think that I landed well after about one year and a half, and here is a summary of my experience.

Table 1

Redefining Effective and Non-traditional

Once I started my job at ASC, I was immediately overwhelmed and also encouraged by the thirst for data from our senior leaders, department chairs, and faculty. To get the high volume of production to meet the needs, I started to prioritize tasks and think creatively to meet the demands. Of course, something had to give. I had to cut out some unnecessary steps and become lean and agile not just in my project selection, but also in my personal approach to completing projects. Here is where the non-traditional aspect comes into place. These strategies will not work for everyone, and I did struggle with some, and even fail.

• Get started somewhere—particularly where your gut tells you to. Dashboards are great. They deliver data to users in an accessible and dynamic format. However, if most users don’t have a good understanding of the data or know how to best use it, an organized and meticulous process simply is not working well. Meeting with end users to get broad information on what they might need was often not effective and created more distractions from my limited amount of available time. Instead, I felt that I needed to: Jump right into it. Your analyst intuition (gut feeling) will provide you with the understanding on what might be needed for the first draft of a dashboard.

   

• Get the dashboard about 80% of the way based on your standards. I spent way too much time trying to perfect a dashboard. With this semi-finished product, I found the first meeting with end users proved to be more productive as I had a tangible protype to discuss specific changes. I would ask them what they would like to see more or less of. Yes, you could probably miss some key points; but we are all pulling more variables than we need for our analyses; at this point you will be prepared to discuss what else is possible, or not possible, and the amount of time it took to get you to this point. Not sure about you, but I’m guilty of spending a lot of time with the last 15-20% of dashboard creation process.

   

• Use standardized reports and templates to save time. I love templates. As analysts and reports creators, we probably all have our preferred ways of doing our work. Try to create templates—for dashboards, email communications, PowerPoint presentations, analyses…for everything in your work. But then, review those templates about once per year with the mindset that they need to improve. My newest, favorite template is related to presenting dashboards; I copied the following four points from a Harvard Business Review article:
  • What if you could…
  • So that…
  • For example…
  • And that’s not all.

Building Trust, Collaborations, and Relationships

Working in a small IR office, you are often the only person colleagues will count on to gain access to data resources and to analyze data. Sometimes, even when you work 10-12-hour days, you may still not be able to get all the requests done on time. Here, building up trust, collaborations, and good relationships really helps.

• Be relentlessly transparent with your internal audience. Create your lists of priorities based on key players. Having this list available to all your data users should help them understand their role in relation to others for the demands on your time and work. Here is an example of my list:
  • Executive Deans
  • Divisional Deans
  • All Other Deans
  • Directors of Administrative Core Units
  • Chairs
  • Center Directors
  • Directors of Graduate / Undergraduate Studies
  • Curricular contacts
  • HR Consultants
  • Finance Managers
  • Advisors
  • Faculty and Staff
Many times, I don’t ask end users (internally within the college) the reason for requesting specific data. I know many of you would raise your brows on this. But I do have ways to check their employment role and whether they completed the required institutional training for the specific data domain. Depending on my availability, I ask some users whether giving them the data in a simple format is sufficient, especially if they can do their analysis. I do ask them to share back with me if they find something worthwhile in the data. But, if the requestor is toward the top of my list, then I do the full analysis. For them, I prepare short and long versions of the communications.


• Elevate your audience and end users. When presenting data or writing reports, the traditional way is to start from the beginning. Yet, how many of us run out of time in presentations? Treat your audience as an equal, level analyst and start from somewhere in the middle. This will give you space to adjust if needed, or leave time for questions, debates, critiques, and questions. This last point is a key ingredient that could help you in updating and improving the work you are presenting.
  
  
• Collaborate with others. We’re doing institutional research, not making astrological discoveries. Copy from others, and let others copy your work—even if you don’t receive credit for it. Don’t spend time seeking credit. Instead, use that time to improve your ongoing work. Still, on my end, I try to give credit to others as much as I can. What do we have to lose if we all share the credit and the sense of accomplishment?

I liked to mix things up. While I worked hard and fast on creating the core dashboards, I had to maintain some relationships with my audience by answering small data requests and engaging in smaller analyses. I turned some of those requesters into reviewers of my work, since I was a one-person office. I think being familiar with project management, mapping, and organization is crucial for analysts of small IR offices. The non-traditional work emerges at the time when the traditional tools are mixed. In my work, I found it useful to mix parts and methodologies from 5S Lean Tools (Sort, Straighten, Shine/Simplify, Standardize, Sustain), Agile, Scrum, SWOT, etc. The best analyses come from mixing data domains and presenting them in scorecards, ratios, or profiles. For example, we had some success in mapping donors’ financial giving data with student enrollment data. This is exciting and fulfilling for IR staff because we can materialize the impact of our work.

Conclusion

This shouldn’t come as a surprise to you: We are experts in moving fast in our analyses and approaches to working with data. We use many processes, methodologies, and promising tools. Yet, we all know that our effectiveness in addressing the needs of our stakeholders comes from tweaking the system (the tradition) to make a small office work.

I’m looking forward to hearing your stories, perhaps in future eAIR newsletters.

Edited by Henry Zheng, Vice Provost for Institutional Effectiveness and Planning at Carnegie Mellon University.