WEBVTT 1 00:00:00.000 --> 00:00:01.690 Christine Keller: Appreciate the inner 2 00:00:01.890 --> 00:00:18.949 Christine Keller: introduction, and to you and Brian both for the invitation to to join you all this morning. It sounds like a very strong, strong program. So I congratulate you all, and I'm really honored to to kick everything off. 3 00:00:19.720 --> 00:00:24.480 Christine Keller: So I am going to share my slides. 4 00:00:26.150 --> 00:00:28.830 Christine Keller: And get us started. 5 00:00:33.140 --> 00:00:33.950 Christine Keller: Alrighty. 6 00:00:37.600 --> 00:00:47.889 Christine Keller: this morning. I am going to be sharing some highlights from our National survey of Ir offices. 7 00:00:48.290 --> 00:01:11.309 Christine Keller: and I am going to be sharing quite a bit of data. So I want to answer the elephant in the room question before I even start, and that is, I will be sharing the slides after this presentation is over, just in case someone wants to go back and revisit some of the data. 8 00:01:12.097 --> 00:01:21.289 Christine Keller: The other. Thank you. I need to an acknowledgement. I need to say, up front is, Thank my colleague, Darlene Jones. 9 00:01:21.782 --> 00:01:44.449 Christine Keller: She is the analytic genius behind our survey. She's the one that really helped me shape this this presentation, and the one who is leading this survey, and has done since it began in 2015. So thank you to to Darlene for her assistance. 10 00:01:46.290 --> 00:02:05.110 Christine Keller: So you heard my formal bio. But 3 additional things that you need to know about me before I get started. One is, I'm often seen with the diet Coke in my hand, and this presentation will be no different. 11 00:02:05.568 --> 00:02:22.259 Christine Keller: I also enjoy running. I am a long time runner, and I'm really happy to be in Tallahassee. There are so many beautiful trails, and I just have a picture of one that I often go beside my house. 12 00:02:22.410 --> 00:02:48.869 Christine Keller: and finally, I do have a small dog named Addy. She is a wire hair dachshund, and so I was telling the folks earlier that they decided to do. My neighbor has decided to install a whole house generator next door. So if you hear Addie, she's just saying hello and making sure we're all safe and ready to go for for the morning. 13 00:02:51.570 --> 00:02:58.620 Christine Keller: So the national survey of Ir and ie. Offices 14 00:02:59.496 --> 00:03:07.849 Christine Keller: in earlier last fall we did our 4th iteration of the the National Survey. 15 00:03:08.614 --> 00:03:24.560 Christine Keller: We had about just over 600 responses out of about 1,700 attempts for a response rate of 38%, which is not too shabby in in these days. 16 00:03:25.345 --> 00:03:43.589 Christine Keller: So to put together the research data set that I'm going to be using this morning. We did exclude responses from sectors that were not well represented in the results. And you can see a list of those on the screen. 17 00:03:43.900 --> 00:03:45.170 Christine Keller: Also. 18 00:03:45.770 --> 00:04:04.210 Christine Keller: we did remove the respondents, who for some reason stopped out early and just answered a few questions. So the final research data set, we ended up with 552 Us. Degree granting institutions. 19 00:04:04.920 --> 00:04:24.120 Christine Keller: So I was going to try to just aggregate, pull out some of the results from Tennessee. But unfortunately we only had 9 institutions respond from Tennessee, so really not enough to to break out. 20 00:04:24.120 --> 00:04:39.510 Christine Keller: And then I decided I was going to do some polls, and I was defeated again. No, to everyone. You can't transfer polls from user to user outside of the same zoom license. So just in case everybody needs to know that. 21 00:04:39.560 --> 00:04:56.279 Christine Keller: But I may try, throughout the presentation to do a bit of a hand check. Let's use the raise hand function just to maybe see if we can get some sense of where all of you fall. And into this this data set 22 00:04:57.830 --> 00:05:23.000 Christine Keller: just one other note about the data set. You see the 2 pie charts on on the slide. First, st we always compare the results of the national Survey to the ipads universe, and this year, we found that we are slightly underrepresented for private, not-for-profit institutions in 2 year 23 00:05:23.120 --> 00:05:50.410 Christine Keller: public institutions. And we're slightly overrepresented for public 4 year, but not enough to be of concern. So it's not really too far from the ipads universe. And when we disaggregated by us census area, what our data set almost matches the ipads universe. So just another piece of information as as you look at the results 24 00:05:53.510 --> 00:06:12.879 Christine Keller: so threaded throughout my presentation are 2 concepts about the importance of working together in community. One is a quote attributed to John F. Kennedy, and that is a rising tide lifts all boats. 25 00:06:14.340 --> 00:06:22.090 Christine Keller: The other is a concept that's rooted in African philosophies, and that is Mbuntu 26 00:06:22.883 --> 00:06:31.900 Christine Keller: it was often invoked by 2 famous South African leaders, Nelson, Mandela, and Bishop Desmond Tutu. 27 00:06:32.110 --> 00:06:49.839 Christine Keller: and the I am because we are really takes that lifting all boats one step further by emphasizing our interconnectedness, the need to work in community for the good of all. 28 00:06:51.610 --> 00:07:01.260 Christine Keller: And I think that fits well with the theme of the Conference which is navigating uncertainty, collaborating for collective impact. 29 00:07:01.700 --> 00:07:13.590 Christine Keller: And I think our need for collaboration, our need to share each other's knowledge and expertise to elevate everyone has never been greater. 30 00:07:13.760 --> 00:07:36.359 Christine Keller: So you may see this little boat Graphic scattered out throughout the presentation. And those are areas that we at air think that are important areas where we might investigate a little further, how we could collaborate together for the good of our profession overall. 31 00:07:39.700 --> 00:07:52.179 Christine Keller: So also in this talk, I'm going to be comparing the metrics based on institutions with chief data officers and those without. 32 00:07:52.260 --> 00:08:18.030 Christine Keller: This is a new area of research for us. This is the 1st time we've asked that question on the National Survey, and I think you'll see there are some interesting results that we'll need to look at further about the impact of having a chief data officer. And who is that chief data officer at an institution? 33 00:08:19.570 --> 00:08:47.620 Christine Keller: I'm also going to be comparing outcomes by our Ir and ie. Office size. And we've grouped those into very small offices of one fte or less kind of the average office size in the middle from 2 to 4 staff fte, and then the larger offices of more than 6 staff fte 34 00:08:48.227 --> 00:08:54.850 Christine Keller: and I think you're going to see overall when you look at the results by office size, that 35 00:08:55.010 --> 00:09:11.729 Christine Keller: there are opportunities for growth and improvement across all offices for us to learn together. But those very small offices have some particular challenges ahead of them. 36 00:09:15.890 --> 00:09:22.279 Christine Keller: The other piece I wanted to do is, I want to ground this in Air's mission. 37 00:09:22.310 --> 00:09:47.520 Christine Keller: which is as a global association. We empower higher education professionals to use data information analytics in ways that are effective, ethical and impactful, and that we support data informed decision making that amplifies both student and institutional success. 38 00:09:47.650 --> 00:10:02.359 Christine Keller: So we focus both on the development of the individual, professional as well as working to improve an institution's systems, their processes, their structures and culture. 39 00:10:04.900 --> 00:10:10.090 Christine Keller: So what does Airs mission look like in an action 40 00:10:13.030 --> 00:10:20.969 Christine Keller: for us? That means that institutions can produce information that's useful to all employees. 41 00:10:21.440 --> 00:10:36.540 Christine Keller: that those employees can easily access that information, and that the employees can interpret and use that information they have access to, and that exists within the institution. 42 00:10:38.770 --> 00:10:46.960 Christine Keller: So how do we support this? So how do we actually help institutions turn these concepts into action? 43 00:10:47.450 --> 00:10:59.149 Christine Keller: One is to increase data capacity. So how can we work together to help more data to be more prevalent at institutions? 44 00:10:59.430 --> 00:11:04.510 Christine Keller: How can we democratize data? How can we increase data access? 45 00:11:04.670 --> 00:11:16.500 Christine Keller: And how can we improve data literacy? So it's not just about data that exists and we have access. But how is data used responsibly 46 00:11:16.600 --> 00:11:21.480 Christine Keller: for the good of the institutions and the students we serve. 47 00:11:21.700 --> 00:11:28.609 Christine Keller: So let's look at the results of the annual survey within each of these 3 3 concepts. 48 00:11:29.940 --> 00:11:47.670 Christine Keller: So 1st is this idea of increasing debate data capacity which we've defined as an institution's ability to effectively collect, store, manage, analyze data in support of data, informed decision making. 49 00:11:50.390 --> 00:12:13.200 Christine Keller: So while ideally, the Ir and Ie. Office wouldn't be the only office that supports data and analysis for decision making. We found that still, for the most part the bulk of decision support is housed in one ir or ie. Office. 50 00:12:14.650 --> 00:12:15.770 Christine Keller: So 51 00:12:16.070 --> 00:12:31.449 Christine Keller: right now, I'm going to make an assertion. I don't have any direct data other than my observation, but I think it's pretty intuitive, and that is that if you have a larger ir, ie. Office with more staff 52 00:12:31.998 --> 00:13:01.360 Christine Keller: an institution has more data capacity. It it just makes sense. Of course, there's some assumptions embedded in that. You know, the staff have to be well trained. The data processes, you have to have strong robust data systems. But in general, more people in the Ir, ie. Office means more data capacity or the opportunity for more data use at the institution. 53 00:13:02.730 --> 00:13:09.859 Christine Keller: So let's look at the average staff size by sector for Ir and ie. Offices. 54 00:13:11.210 --> 00:13:23.409 Christine Keller: So across the 552 respondents. The average staff size is 3.8 or the average IRIE. Office. 55 00:13:23.730 --> 00:13:29.169 Christine Keller: and if we disaggregate by sector, you'll see that public 56 00:13:29.320 --> 00:13:34.559 Christine Keller: 4 year. Institutions have 5.5 staff Fte 57 00:13:35.030 --> 00:13:38.089 Christine Keller: public 2 year. Have 2.9 58 00:13:38.230 --> 00:13:44.629 Christine Keller: and private not-for-profit. Also have 2.9, 59 00:13:45.420 --> 00:13:51.900 Christine Keller: and there's really nothing magic about 4 year. Public institutions. 60 00:13:52.407 --> 00:14:15.809 Christine Keller: And why? They have more staff? Because we found there is a strong correlation between the number of students at the institution and the number of staff in the Ir. And ie. Office, and we found this correlation since we started this survey in 2015, and in fact, the correlation strong is 0 point 6. 61 00:14:16.130 --> 00:14:25.019 Christine Keller: So that's just a relationship that we found has held up across across time. 62 00:14:26.640 --> 00:14:27.740 Christine Keller: However. 63 00:14:28.000 --> 00:14:38.880 Christine Keller: as with everything, there's caveats to this. It's not true that all public four-year institutions have large ir and ie. Offices. 64 00:14:39.230 --> 00:14:49.790 Christine Keller: So we we took a look at those categories. To see kind of what? What the landscape, what that distribution looks like. 65 00:14:49.930 --> 00:15:06.589 Christine Keller: and for some public four-year institutions, 13%, they have less than one staff fte, and it goes all the way to the top, where 23% have more than 6. 66 00:15:07.000 --> 00:15:22.409 Christine Keller: And then you can also see that same breakdown at public two-year public 2 year have more offices. Almost a 3.rd Just have one or fewer staff, Fte. 67 00:15:22.670 --> 00:15:34.789 Christine Keller: and then the public 4 year, I mean, I'm sorry the private 4 Year institution again are are more likely to have smaller, smaller staff. 68 00:15:37.320 --> 00:15:49.690 Christine Keller: So this is where I was going to do a poll to to see. So let me see if this will work, if we can do this by by the raising of hands. 69 00:15:49.810 --> 00:15:50.635 Christine Keller: So 70 00:15:51.650 --> 00:16:04.390 Christine Keller: How many of you on on this, on this call have, and us what you would be a small office size for so less than one staff. Fte. 71 00:16:05.610 --> 00:16:07.459 Christine Keller: just raise your hand. 72 00:16:12.170 --> 00:16:22.400 Christine Keller: I don't see anybody with offices that quite that small. How about medium? How about from 2 to 4 staff Fte. 73 00:16:24.700 --> 00:16:26.200 Christine Keller: Add one. 74 00:16:31.350 --> 00:16:32.590 Christine Keller: Alright. 75 00:16:32.850 --> 00:16:40.099 Christine Keller: How many have staff that are larger than 6? So the large office size. 76 00:16:46.620 --> 00:16:47.290 Christine Keller: right? 77 00:16:48.230 --> 00:16:55.429 Christine Keller: All right. So apparently a lot of you fell between the 4 to 5. Is that another initiative? 78 00:16:56.140 --> 00:16:58.079 Christine Keller: And okay, okay. 79 00:16:58.260 --> 00:17:04.560 Christine Keller: so I think it is, is it? It seems like it's fair to say. And I'm gonna move 80 00:17:05.480 --> 00:17:07.650 Christine Keller: all of you over there, so I can see. 81 00:17:07.910 --> 00:17:25.809 Christine Keller: So so it looks like. It is fair to say that for most of the folks that are attending this session today you're kind of on the larger skew towards the larger end of of the ir, ie. 82 00:17:27.680 --> 00:17:34.870 Christine Keller: So here is so let's take sector out of it, and just look at the full distribution. 83 00:17:36.560 --> 00:17:48.039 Christine Keller: and this is what the results were for for the survey, so you can kind of see where your office fits into the overall national landscape. 84 00:17:48.721 --> 00:18:15.109 Christine Keller: I continue to have nearly a 3rd of our ir and ie. Offices have less than one staff fte, and I have to say this is hard for me to imagine, because my own experience at the University of Kansas, we had 15 people in our Ir. And ie. Office, and I thought that was normal. 85 00:18:15.210 --> 00:18:30.090 Christine Keller: Imagine my surprise when when reality hit. When I got to Aplu and found out just how many institutions were doing their data and analysis with much, much fewer people. 86 00:18:31.980 --> 00:18:33.090 Christine Keller: Still. 87 00:18:34.350 --> 00:18:44.429 Christine Keller: Then, I want to dig into this this idea of data capacity a little a little further. 88 00:18:44.540 --> 00:19:09.969 Christine Keller: So within the survey there are a series of likert scale questions and to help us kind of simplify the graph. Darlene, group them into these. These 3 buckets with the associated color. So disagree is red neutral is yellow and agree is in green, so you can more easily see 89 00:19:10.705 --> 00:19:13.330 Christine Keller: what the results look like. 90 00:19:15.470 --> 00:19:30.749 Christine Keller: So one of the questions we asked was, Can your office achieve its work, its goals without staff working overtime? And again we disaggregated by office size 91 00:19:31.410 --> 00:19:46.630 Christine Keller: and fully 56% of small offices disagreed. So 56%, in other words, said that they needed to work overtime to get the work done that they needed. 92 00:19:47.670 --> 00:19:57.469 Christine Keller: It's a little better for staff from 2 to 4. Only 44% said some. Some sort of office overtime was required. 93 00:19:58.860 --> 00:20:08.190 Christine Keller: and it's a little again, a little better for larger office size, but I guess I was surprised that even the larger office 94 00:20:08.300 --> 00:20:12.683 Christine Keller: size over almost a 3rd said that 95 00:20:13.950 --> 00:20:20.580 Christine Keller: There was some overtime required to get the work done that was needed. 96 00:20:22.250 --> 00:20:44.429 Christine Keller: So then we asked it, could the offices better meet institutional expectation with additional staff, and no one's going to be surprised by the results that you see here. Between 80 and 90% said yes, regardless of their current staff size. 97 00:20:45.160 --> 00:21:01.520 Christine Keller: So we wanted to dig into this a little deeper. So what is that ideal staff size for ir and ie. Offices? So if you recall, the average is is 3.8, 98 00:21:01.810 --> 00:21:02.920 Christine Keller: and 99 00:21:03.050 --> 00:21:15.589 Christine Keller: the ideal across all sectors was almost an additional 2 staff Fte. They felt that would be the ideal size of staff. 100 00:21:16.040 --> 00:21:24.150 Christine Keller: and we also asked to imagine 3 years from now. What was your ideal staff size look like? 101 00:21:24.400 --> 00:21:32.870 Christine Keller: And somewhat surprisingly, it's still about just a little over 2.2 point 2. 102 00:21:35.510 --> 00:21:46.400 Christine Keller: So are there differences in the AI dependent, this ideal staff size for a lot, small average and and large offices. 103 00:21:47.487 --> 00:21:55.060 Christine Keller: So for small offices, again, 2.2 fte. Additional 104 00:21:56.040 --> 00:22:18.550 Christine Keller: for medium to additional or large, just over 2 additional. So I'm not quite sure what is magical about this 2 additional staff member. But that's what we found consistently across. The small average and and large 105 00:22:19.523 --> 00:22:21.149 Christine Keller: ir office. 106 00:22:23.300 --> 00:22:28.919 Christine Keller: So let's go back in time and look at trends in office size over time. 107 00:22:30.050 --> 00:22:32.810 Christine Keller: So there were 108 00:22:33.370 --> 00:22:54.869 Christine Keller: 253 institutions that answered the survey, both in 2021, and 2024, and effectively for those institutions. We really found no difference in the average office size over the the past 3 years. It stayed pretty consistent. 109 00:22:55.930 --> 00:23:13.569 Christine Keller: So let's go back to 2024, there are 147 participants who answered the survey all 3 years, and the as compared to 2018, the average office size has gone down a bit. 110 00:23:14.264 --> 00:23:33.800 Christine Keller: We hypothesized. This may be a result of the covid pandemic that maybe there was some downsizing around 2020, 21, and then it just never recovered. That became the new steady state. 111 00:23:38.540 --> 00:23:39.300 Christine Keller: So 112 00:23:41.340 --> 00:23:57.380 Christine Keller: beyond staff size. We also asked a series of data environment questions, that kind of get into the data maturity, that of the institution and the office 113 00:23:57.620 --> 00:24:00.440 Christine Keller: and those 114 00:24:00.820 --> 00:24:19.330 Christine Keller: ratings were on a 4 point scale. And again, you know, just to make the graphs easier, to interpret, not occurring or reactive. And I would define reactive. Is this activities only happening in a crisis more of a crisis situation 115 00:24:19.530 --> 00:24:39.900 Christine Keller: and then proactive and optimized, we group together. So one, you're kind of acting in anticipation, taking action to meet future needs and the other is optimized, so the situation is as perfect as it would get under the current situation. 116 00:24:42.120 --> 00:25:01.490 Christine Keller: So let's look at ir office maturity, and how the respondents related the maturity of their office on that 4 point scale on these various kind of common tasks that we see Ir and Iie offices 117 00:25:01.690 --> 00:25:02.770 Christine Keller: performing. 118 00:25:03.230 --> 00:25:19.879 Christine Keller: and you can see on this graph, on the far left, basic analytics all the way to advanced analytics. So they're kind of ordered in in the tasks are ordered in by degree of complexity across that that X-axis. 119 00:25:21.120 --> 00:25:31.660 Christine Keller: So you can see for basic analytics. Most offices felt. Their maturity level was either proactive or optimized 120 00:25:32.140 --> 00:25:37.280 Christine Keller: managing data still very high at at 80%. 121 00:25:37.520 --> 00:25:41.150 Christine Keller: But as I go and click through 122 00:25:43.930 --> 00:26:02.069 Christine Keller: and the tasks become more complex, you can see that people's confidence in rating of the data maturity of their office tends to go down, and which is, is, I don't think, too too surprising. 123 00:26:06.670 --> 00:26:28.190 Christine Keller: So again, I was going to do a poll here about how you would rate your own office for for data maturity. So this would if I do it this time. It's not going to be anonymous. So how do you all feel about that? 124 00:26:28.420 --> 00:26:35.750 Christine Keller: Do you want me to try this? Or do you want you? You? Wanna you wanna give this a shot? Okay, okay? 125 00:26:36.150 --> 00:26:37.190 Christine Keller: All right. 126 00:26:37.370 --> 00:27:00.450 Christine Keller: So what I'm going to ask 1st is, let's go with the kind of just the the basic analytics would. How many of you? What? Let's go with the positive? How many of you would rate your office data maturity for basic analytics as proactive or optimized. Raise your 127 00:27:01.030 --> 00:27:02.450 Christine Keller: virtual hand. 128 00:27:07.780 --> 00:27:14.209 Christine Keller: I'm watching the number go up over here. So so we're looking good. Okay, 129 00:27:16.620 --> 00:27:33.509 Christine Keller: So the other one, let's go to the other end of the scale. How many of you would rate your office's data maturity for advanced analytics as proactive or optimized. 130 00:27:33.690 --> 00:27:35.360 Christine Keller: Erase your virtual. 131 00:27:41.140 --> 00:27:45.952 Christine Keller: I think y'all are better than that. 132 00:27:48.160 --> 00:27:52.339 Christine Keller: Spend a few, a few less, a few less. Okay. 133 00:27:52.470 --> 00:27:56.990 Christine Keller: Okay. So perhaps in an area we could all all work on schedule 134 00:27:57.200 --> 00:28:03.320 Christine Keller: to to figure out how to to better utilize more advanced analytics. 135 00:28:03.640 --> 00:28:27.179 Christine Keller: The other one I was just curious about. And you'll see I'm going to show a breakdown on on this metric later in the slides is for data visualization in particular, which was kind of in the middle of that scale. How many of you would rate your offices as proactive or optimized when it comes to data visualization. 136 00:28:32.060 --> 00:28:37.400 Christine Keller: A few but looks like 1213, 137 00:28:38.010 --> 00:28:50.680 Christine Keller: 14 people would would do that. Okay, alright. Well, thank you all for for indulging me here as as we we do this in in real time. So 138 00:28:51.824 --> 00:28:53.366 Christine Keller: so let me 139 00:28:55.190 --> 00:28:58.558 Christine Keller: So let me kind of take this from 140 00:29:00.227 --> 00:29:14.550 Christine Keller: kind of the next step. So I think that we would all agree that if we could increase the maturity of our ir and ie. Offices. Across these various tasks 141 00:29:14.660 --> 00:29:35.859 Christine Keller: we could increase its capacity to work more effectively, more efficiently, and maybe cut down on some of that overtime that's necessary to to get the work done. So what are some ways? What are some insights we found in the survey to to do that. 142 00:29:37.890 --> 00:29:38.640 Christine Keller: So 143 00:29:39.830 --> 00:29:53.359 Christine Keller: First, st we wanted to take again to segregate by office size and see if there were any trends that that we could look look at, based on office size. 144 00:29:53.700 --> 00:29:54.880 Christine Keller: And 145 00:29:57.320 --> 00:30:17.090 Christine Keller: so, perhaps not surprisingly, that creating data visualizations those larger offices had perhaps more opportunity, perhaps more expertise, perhaps more time to to really lean in to create those data data visualizations 146 00:30:18.150 --> 00:30:27.209 Christine Keller: for managing data. There's a little bit of difference across office sides, but pretty robust overall. 147 00:30:28.010 --> 00:30:35.310 Christine Keller: And then for the basic analytics again, a little bit it. There is some differences. 148 00:30:35.845 --> 00:31:03.179 Christine Keller: And there. These differences are statistically significant, but perhaps not as as different as one might might expect. I'm actually pretty darn impressed with offices with less than one person, and that 78% said that they were proactively able to to get the the basic analytics that were were needed. 149 00:31:05.040 --> 00:31:09.900 Christine Keller: So of course, we know it's more than just additional staff. 150 00:31:10.050 --> 00:31:19.099 Christine Keller: So we asked also asked respondents if their offices were adequately funded to meet those operational needs. 151 00:31:19.260 --> 00:31:23.209 Christine Keller: And again, we're looking at this by by office size. 152 00:31:23.870 --> 00:31:31.219 Christine Keller: So fully 51% of the very small offices felt like they weren't adequately funded. 153 00:31:32.180 --> 00:31:37.439 Christine Keller: That number re goes down a bit for the average office size 154 00:31:39.700 --> 00:31:47.099 Christine Keller: and goes down a little bit more for the larger offices. 155 00:31:47.450 --> 00:32:01.400 Christine Keller: So while things are better for those those larger offices, I I think things still could be improved. That's why you see the boat that my boat icon up here? 156 00:32:01.410 --> 00:32:18.259 Christine Keller: Where are there places that rather collectively we could advocate? Maybe air could help collectively, we could find ways to make our case, to make sure that our Ir and Ir 157 00:32:18.360 --> 00:32:26.470 Christine Keller: offices have the funds they need to fulfill their their operational needs for the institution. 158 00:32:28.740 --> 00:32:52.720 Christine Keller: So now I'm going to move from data capacity over to data access. So it doesn't matter if you have lots and lots of data produced and managed and collected. If the people who need the data can access that data. So let's take a look at what data access looks like from a national perspective. 159 00:32:53.380 --> 00:33:13.969 Christine Keller: And we looked at this in 2 different ways. One is, does the Ir and ie. Office have the access to the data they need to do their job. And then later, and we're going to dig into this metric a little bit more. Do stakeholders have access to the data they need 160 00:33:14.654 --> 00:33:22.650 Christine Keller: to to make the decisions they need and do the analysis, they need to do their jobs. 161 00:33:22.830 --> 00:33:25.800 Christine Keller: But 1st the Ir and ie. Office 162 00:33:26.736 --> 00:33:32.150 Christine Keller: smaller offices about a 3rd have have challenges. 163 00:33:33.070 --> 00:33:53.299 Christine Keller: but so do the the larger offices. 20% of the medium and larger offices still feel like there are some challenges, getting the data. They need to to do their to do their work. 164 00:33:56.620 --> 00:34:22.579 Christine Keller: So I'm I'm curious again. So I'm going to again do do the positive one. How many of you all feel as though you have your office has access the date to the data you need to do your work and your office responsibilities. Do the raise the hand. 165 00:34:35.110 --> 00:34:45.849 Christine Keller: I'm showing about about 8. I see a cup. I see a a real hand raised there, so I'll say 9, 166 00:34:45.969 --> 00:34:53.900 Christine Keller: So maybe there are some, some challenges, again, that collectively could be worked on. 167 00:34:54.040 --> 00:35:06.179 Christine Keller: What are some strategies to to better access the data you need as a data professional to to serve your institution and stakeholders. 168 00:35:08.300 --> 00:35:24.443 Christine Keller: So now, let's turn to to do. Stakeholders have access to the information they need, and we looked at this across 2 different metrics. One is, Do institutions 169 00:35:25.140 --> 00:35:42.439 Christine Keller: meet stakeholders needs for the information so kind of the information itself. And does the institution produce the reports that are easily accessible and used by stakeholders across all all levels. 170 00:35:43.050 --> 00:35:44.130 Christine Keller: And 171 00:35:44.620 --> 00:36:08.340 Christine Keller: this is one of those times when there were no statistical differences between by office size. About a 3rd of Ir and Ir and ie. Leaders report that their stakeholders had some challenges accessing information or reports 172 00:36:09.113 --> 00:36:18.290 Christine Keller: or look at the positive. 2 thirds felt like that they were in their stakeholders did have access to 173 00:36:18.550 --> 00:36:20.470 Christine Keller: to information. 174 00:36:22.680 --> 00:36:42.250 Christine Keller: So let's do another show of hands. How many of you think that your stakeholders do have access to the information and reports that they need to improve student success or outcomes. 175 00:36:42.450 --> 00:36:45.260 Christine Keller: Raise them virtual hand. 176 00:37:03.410 --> 00:37:04.045 Christine Keller: That's 177 00:37:05.960 --> 00:37:28.419 Christine Keller: that's it. That's that's interesting. At least by by the show of hands and the count that was showing on the screen, it appeared that for for your group. You all thought that your stakeholders had greater access to to the data than you do yourself, or there are fewer challenges which is. 178 00:37:28.420 --> 00:37:41.329 Christine Keller: is, is interesting to to think about what may be the implications for that, or it could be an error in the polling. So well, that's something interesting to to think about. 179 00:37:44.470 --> 00:37:58.659 Christine Keller: so I think we can all agree that if we could increase access to data for both our offices, and especially for our stakeholders. We could improve student outcomes. 180 00:37:58.750 --> 00:38:19.580 Christine Keller: It just seems. I think that's just something we hold as as a belief within a profession. If we could get those, you know retention reports. If we could combine it with financial aid data, if we could create those those just in time dashboards for our stakeholders 181 00:38:19.730 --> 00:38:30.340 Christine Keller: that those stakeholders would be able to use that information, be able to make better decisions, resources that could be allocated more effectively. 182 00:38:30.940 --> 00:38:43.370 Christine Keller: So what are ways that we could potentially do that. And what are some insights we learned from from the National Survey. 183 00:38:43.820 --> 00:39:04.040 Christine Keller: and this is the one of the places where we did try to to get a handle on. Were there differences for those institutions with chief data officers on these 2 access metrics versus institutions that did not have a chief data officer. 184 00:39:04.420 --> 00:39:08.160 Christine Keller: And so we asked this question 185 00:39:08.680 --> 00:39:18.190 Christine Keller: because this particular position is, it's not really widespread yet. We asked it in 3 different ways. 186 00:39:18.936 --> 00:39:32.090 Christine Keller: We asked. It does your institution have a chief data officer? And is it you as the the Iri office leader? 187 00:39:32.380 --> 00:39:41.350 Christine Keller: Do you have a chief data officer? But it's outside the Irie office. And does your institution not have 188 00:39:41.620 --> 00:39:44.130 Christine Keller: a chief data officers? 189 00:39:44.977 --> 00:39:52.910 Christine Keller: So for this 1st one, I'm just going to look at whether the institution has a chief data officer or not. 190 00:39:53.070 --> 00:40:00.459 Christine Keller: And so again, looking at those data stakeholder data access questions, this is what we found. 191 00:40:00.770 --> 00:40:08.740 Christine Keller: And these differences are statistically significant. We found that 192 00:40:09.090 --> 00:40:26.259 Christine Keller: for those who had a chief data officer or someone dedicated to helping optimize data across the institution not surprisingly, stakeholders in general had more access to information. 193 00:40:26.570 --> 00:40:33.180 Christine Keller: and the same when it comes to producing high quality reports. 194 00:40:34.370 --> 00:40:41.280 Christine Keller: And, as I said, both of those are statistically significant and 195 00:40:41.967 --> 00:40:52.370 Christine Keller: the the outcome gets even better when the Iri office leader is also the the chief data officer. 196 00:40:52.760 --> 00:41:13.740 Christine Keller: So when you look at those 2 questions, you see, it bumps up to 81% rated access to data as either optimized or proactive. If the chief data officer was also the office leader which 197 00:41:13.910 --> 00:41:24.280 Christine Keller: think there's some some wisdom there, some lessons we can learn from that, and the same when it comes to 198 00:41:25.160 --> 00:41:30.689 Christine Keller: the reports and the availability for institutions. 199 00:41:35.050 --> 00:41:53.740 Christine Keller: So my next question is, for how many of your institutions I'm going to do this in 2 parts. First, st I'm going to ask how many of your institutions have a chief data officer role if you would raise your virtual hand. 200 00:42:05.420 --> 00:42:13.690 Christine Keller: Okay, we've got account of about 8 on on a little metric here. 201 00:42:15.550 --> 00:42:30.489 Christine Keller: All right. So now, the next question I'm going to ask is, how many for those of you who have chief data officers, how or how many of you. Is that also the Iri office leader? 202 00:42:41.270 --> 00:42:49.619 Christine Keller: So I. My 1st number was 8. My second number was 4, so just in general, maybe about half 203 00:42:50.107 --> 00:42:54.639 Christine Keller: of those who have chief data officers. It's also the office leader. 204 00:43:00.170 --> 00:43:01.820 Christine Keller: And then, finally. 205 00:43:02.496 --> 00:43:25.269 Christine Keller: I want to talk about data literacy, which is a topic near and dear to my heart, because it doesn't matter how much data you have or who has access to it. If the people who have the access don't have the knowledge and skills to use the data appropriately, responsibly. 206 00:43:25.831 --> 00:43:32.300 Christine Keller: The impact is is not going to be as strong as it. It could be 207 00:43:34.610 --> 00:43:57.690 Christine Keller: so. When we measured this on the National Survey, we used 2 different metrics. We use the rating on employees understand institutional information, and also that employees know when that information should be used for decision making. 208 00:43:58.740 --> 00:43:59.780 Christine Keller: and 209 00:44:00.030 --> 00:44:12.439 Christine Keller: turns out that there's no statistical difference between office size. This is consistent across all IRIE. Office sizes. 210 00:44:14.330 --> 00:44:19.870 Christine Keller: Although I do find this this, this result a little depressing. 211 00:44:19.980 --> 00:44:28.889 Christine Keller: Yeah, over half rated institutional data literacy as not occurring or reactive. 212 00:44:29.080 --> 00:44:41.729 Christine Keller: So I think we all perhaps have some work to do. Where are ways that we can actually improve data literacy at our our institutions? 213 00:44:43.760 --> 00:45:04.330 Christine Keller: so what are ways that that we can do that. How can we impact data literacy on our campus? So the data that we spend so much time collecting and managing and providing access to is actually used for the benefit of both the institution and for our students. 214 00:45:05.440 --> 00:45:10.860 Christine Keller: So again, we look for insights for the chief data officer question 215 00:45:11.556 --> 00:45:27.700 Christine Keller: and perhaps not surprisingly. We found that the rating of data literacy on those 2 metrics improved. If the chief data officer was also the the office leader. 216 00:45:29.020 --> 00:45:33.630 Christine Keller: And you can see fully 85%. 217 00:45:34.821 --> 00:45:52.830 Christine Keller: Said that employees know when information could be used for decision making and 66 understand institutional information. And these are over 20 point differences between these 2 ratings. 218 00:45:52.880 --> 00:46:04.429 Christine Keller: So again, that idea of a dedicated position, of a chief data officer does make a difference. 219 00:46:04.520 --> 00:46:30.269 Christine Keller: and I dug around just a little bit to see what other evidence there was about the value of a chief data officer, and I found some work by educause, also by the Gates Foundation, and from those sources some of the findings they found were chief data officers. 220 00:46:30.856 --> 00:46:46.170 Christine Keller: Often help bridge those divides between Ir, it and the academic units. They can help bring those people together, build trust and understanding of what data was available. 221 00:46:46.988 --> 00:47:10.320 Christine Keller: There were also some case studies I found from Arizona, State and the University of Wisconsin Systems, and those those schools, those systems had found that Cdo position and in particular help kind of demystify data for non-technical staff. 222 00:47:10.510 --> 00:47:26.150 Christine Keller: So even though this is a newer measure for us, it seems like there is evolving evidence from other associations and other studies that that Cdo position is, is and can be important. 223 00:47:27.740 --> 00:47:53.439 Christine Keller: But it's not just about one person in one position. Many of you may recall that air published the statement of aspirational practice for institutional research back in 2016, and one of the things we posited, and one of the calls for action we had within that report 224 00:47:53.470 --> 00:48:23.169 Christine Keller: was that institution had a role. Institutional research had a role to play when it comes to coaching. So not just providing data, but helping stakeholders use the data for a wide array of data consumers and our goal for data literacy that that we had. Was it for it to be as ambiguous as expectations for writing and speaking and computer skills. 225 00:48:25.150 --> 00:48:40.090 Christine Keller: So within the National Survey we used in back in 2018, we asked the question, so how many ir and ie. Offices were providing educational opportunities for stakeholders. 226 00:48:40.410 --> 00:48:42.176 Christine Keller: and in 227 00:48:43.730 --> 00:48:51.260 Christine Keller: 2018, we found that number was about 28%. So just a little over a quarter. 228 00:48:52.140 --> 00:49:08.999 Christine Keller: But when we asked that question again in 2021, that number had almost doubled. So about half by our and ie. Office, were providing some sort of educational opportunity for for their stakeholders. 229 00:49:09.190 --> 00:49:15.120 Christine Keller: and again, in 2024, that number went up a little bit more. 230 00:49:15.730 --> 00:49:24.499 Christine Keller: So now it's almost 60% for providing some sort of educational opportunity. 231 00:49:26.170 --> 00:49:29.100 Christine Keller: So or 232 00:49:32.070 --> 00:49:52.960 Christine Keller: just went backwards. There we go. So for the 2024 survey. Darlene also took those numbers and just aggregated it by office size, just to see if there were any any differences, and 44% of those very small offices 233 00:49:53.130 --> 00:50:04.080 Christine Keller: are still finding ways to to help with data, coaching and data literacy, which I think is is quite impressive 234 00:50:05.630 --> 00:50:19.539 Christine Keller: And as the office size gets larger, not surprisingly, there are probably more expertise, more opportunities to help with improving data literacy on our our campuses. 235 00:50:22.880 --> 00:50:33.369 Christine Keller: So if you're doing the training, are there their differences in outcomes when it comes to those ratings of data, literacy, maturity. 236 00:50:33.940 --> 00:50:35.750 Christine Keller: And yes. 237 00:50:36.490 --> 00:51:01.339 Christine Keller: we found that data literacy training does does matter as measured by the the ratings on the data literacy, maturity for those 2 metrics. So you can see that for those Irie offices that are providing training their data, literacy ratings. They they rated their institution as higher 238 00:51:02.642 --> 00:51:11.169 Christine Keller: on both of the the different metrics. So the short answer to my question is, yes. 239 00:51:11.390 --> 00:51:40.209 Christine Keller: but I think those red parts of that bar are still pretty high. So again, I think we still have some work to do as a profession? How can we collaborate together? How can we exchange ideas? How can we collectively help our stakeholders better understand so they can use the data that we are providing to to them. 240 00:51:41.590 --> 00:51:50.520 Christine Keller: and the training does help 30% difference. But I think we all still have some some work to do 241 00:51:51.671 --> 00:52:15.589 Christine Keller: so one more, Poll, I'm curious how many of you in in your role as an ir or ie. Officer, how many of you all provide some sort of data literacy, coaching, training, informal or informal to your stakeholders. You would raise your hand. 242 00:52:31.740 --> 00:52:40.179 Christine Keller: Quite a quite a few of you. That is, that is, that is great. And now you have evidence that it makes a difference. 243 00:52:40.774 --> 00:52:57.869 Christine Keller: So I I know there is all sorts of creative ways to do this. I've heard of open houses where there's wine and data involved, wine, data and cheese to to actually going into 244 00:52:57.870 --> 00:53:11.589 Christine Keller: an academic unit and really walking through dashboards and report so I think there's a range of stories I've heard about how I are in our offices are fulfilling 245 00:53:11.840 --> 00:53:12.930 Christine Keller: that need. 246 00:53:14.830 --> 00:53:16.060 Christine Keller: So 247 00:53:18.270 --> 00:53:37.230 Christine Keller: I know I've shared lots of data, a lot of information. It's it's been nearly an hour since since I started talking, and I think it's, I think. It's pretty clear. There's not one size fits all path path forward. 248 00:53:38.014 --> 00:53:49.100 Christine Keller: And some of these challenges are not. They're not easy. There's not just an obvious solution to move forward. 249 00:53:49.893 --> 00:54:09.140 Christine Keller: But I'm convinced that we, as a profession can influence change, and one of the ways we can do that is, if we're clear about our needs, we start to build those important coalitions, to to support our work. 250 00:54:10.045 --> 00:54:23.750 Christine Keller: I think one of the keys is to figure out kind of what's within our control. And where can we make some progress? And then, step by step, we can build from there. 251 00:54:24.020 --> 00:54:49.599 Christine Keller: So what I wanted to do is conclude by kind of walking through what some of those steps might look like for improvement. I'm going to borrow one of the strategies that we share within our air leadership course. Air leads leadership with evidence, data and analytics. 252 00:54:49.600 --> 00:55:16.420 Christine Keller: because in that course what we try to do is provide tools and strategies that don't require a huge investment or some new fancy data system. But what can we do where we are now? So I'm just going to walk through an example of what this might look like, with the hope that there are some ideas, and perhaps some some further conversation. As you continue your time together. 253 00:55:18.400 --> 00:55:35.379 Christine Keller: So I think the 1st question for for all of us is getting clear on what is so? What is the reality of the situation. What are you actually doing right now? What does your team spend time on? 254 00:55:35.560 --> 00:56:02.559 Christine Keller: Who else is involved in those conversations? I know this sounds really basic. But sometimes getting those things on paper can really be eye-opening, and you can do that through workflows or logs, or one of the tools we use a lot is that racy tool? So who's responsible? Who's accountable? Who's consulted and who's informed? 255 00:56:02.680 --> 00:56:06.880 Christine Keller: I think those are important questions to get some clarity on. 256 00:56:07.200 --> 00:56:13.689 Christine Keller: and then equally important to ask the question of, Why are you doing the things you're doing? 257 00:56:13.830 --> 00:56:28.830 Christine Keller: Why are we still doing that report? Who's using that dashboard? And if the answer is unclear. Perhaps there's some redundancy, and there are some things that that we can let go. 258 00:56:30.340 --> 00:56:35.440 Christine Keller: So the next step is, where are there opportunities for efficiencies. 259 00:56:35.560 --> 00:56:47.970 Christine Keller: Maybe it's automating part of your work. Maybe it's saying no to low value requests or having honest conversations with your stakeholders about those trade-offs. 260 00:56:48.450 --> 00:56:58.740 Christine Keller: I think that by nature many of us in Ir and ie. Are helpers. We really want to serve the needs of our stakeholders. 261 00:56:59.130 --> 00:57:02.990 Christine Keller: but I think part of being helpful is also being strategic. 262 00:57:03.290 --> 00:57:13.859 Christine Keller: So where can we focus that limited capacity that we saw in the survey to focus on those things that matter most 263 00:57:14.080 --> 00:57:18.820 Christine Keller: for our institutions and for our our students. 264 00:57:20.350 --> 00:57:28.050 Christine Keller: So once you've clarified what's being done where efficiencies are possible, then, of course, we have to gather some evidence. 265 00:57:28.540 --> 00:57:56.299 Christine Keller: And one of the things we found that, and reported back from our institutions that the idea of Roi, even though it's a business concept return on investment, that sometimes people kind of bristle at in higher Ed, that sort of analysis can really be powerful to to leadership. 266 00:57:57.030 --> 00:58:01.240 Christine Keller: And just as important is finding allies. 267 00:58:01.570 --> 00:58:11.299 Christine Keller: So when others advocate beside you, it kind of shifts from my office needs to our institution needs. 268 00:58:11.420 --> 00:58:13.470 Christine Keller: And I think that's just another 269 00:58:13.670 --> 00:58:29.870 Christine Keller: important point on why gatherings like like this are so very important to share knowledge and kind of build that that common purpose, whether that's across the ut system, or or even wider. 270 00:58:31.920 --> 00:58:58.989 Christine Keller: And then, of course, even with clear evidence, we still need to prioritize, and that is step 4. And, as I often tell our own staff, sometimes this means we have to choose between really good things. We wish we could do it all. But sometimes we need to make choices about what we need to pursue now what we can defer and what may not happen. 271 00:58:59.855 --> 00:59:02.080 Christine Keller: And these things are not 272 00:59:03.130 --> 00:59:19.710 Christine Keller: static. Right can't put together a plan and execute, because leadership changes our institution. Situation evolves the policy environment that's surrounding us is obviously changing. It seems like on a daily basis. 273 00:59:20.280 --> 00:59:35.509 Christine Keller: So unless you have some priorities to start, though I think it's true that even stronger teams risk burnout or their efforts just become shattered, scatter, shot, and don't have an impact. 274 00:59:35.590 --> 00:59:51.049 Christine Keller: And those overtime numbers worry me about the health of all of us as a profession. So where can we kind of prioritize? Where can we refocus our our efforts. 275 00:59:53.320 --> 00:59:57.860 Christine Keller: And I think when you go through that kind of 4 step analysis 276 00:59:58.040 --> 01:00:18.139 Christine Keller: that local opportunities, even within tight budgets often emerge, and sometimes these are small wins. Maybe it's a process you can streamline. Maybe it's 1 of those reports you can retire. Maybe it's a dashboard that can be simplified. 277 01:00:18.210 --> 01:00:30.729 Christine Keller: but sometimes it's a bigger insight. Maybe it's identifying a partner office that's ready to collaborate in a new way for the benefit of of both offices. 278 01:00:30.970 --> 01:00:42.170 Christine Keller: And I think the important point is to stay open to what is possible even when budgets are and resources feel constrained or tight. 279 01:00:44.300 --> 01:00:59.190 Christine Keller: I also think this sort of analysis puts you well positioned doing, you're ready to advocate for more whether it be budget, tools or people, because and no surprise, your case is grounded in evidence. 280 01:00:59.660 --> 01:01:02.852 Christine Keller: and the framing matters. 281 01:01:03.820 --> 01:01:13.809 Christine Keller: it can tie it to our institutions, goals, compliance risks, operational efficiencies. What does your leadership care about the most? 282 01:01:14.150 --> 01:01:24.710 Christine Keller: So when your case for more resources connects the dot between the data and the impact for the institution. It just resonates 283 01:01:24.940 --> 01:01:26.080 Christine Keller: more deeply. 284 01:01:30.540 --> 01:01:43.530 Christine Keller: And finally, I just want to say that none of us are in this alone. Air. We're here to help where we can. 285 01:01:44.130 --> 01:02:09.700 Christine Keller: whether that be through our courses we have a new consulting arm that could be of value. Also our peer networks I can almost guarantee there is going to be people who face similar challenges. You only have to look at the survey results and those and we together, we can figure out a way through. 286 01:02:11.330 --> 01:02:31.379 Christine Keller: So if you're not sure where to start, we often say, start with the conversation. Maybe it's a peer within your institution. Maybe it's with another air member. Maybe it's with here in this gathering with a colleague from another ut institution. 287 01:02:31.884 --> 01:02:48.469 Christine Keller: We do offer opportunities connect and network at air. We have our online community platform air hub. It's free and open to all all you need to do is is create an account. You don't have to be an air member. 288 01:02:49.071 --> 01:03:00.379 Christine Keller: We also offer monthly coffee chats through just informal opportunities, and those this is a member benefit for members to get together and discuss. 289 01:03:01.410 --> 01:03:26.709 Christine Keller: But again, I just want to commend all of you for for this 3rd gathering of the summit, where you can learn together as peers. These are opportunities to surface ideas you may have not even thought of before, and I think even more importantly, right now, where you can get the encouragement and supports. You need to take that next step 290 01:03:26.740 --> 01:03:44.130 Christine Keller: because my vote icon has reappeared. I don't ever want to estimate the power of our community to to get through and to thrive, no matter what the circumstances. 291 01:03:48.100 --> 01:03:50.600 Christine Keller: So that's 292 01:03:51.460 --> 01:04:16.939 Christine Keller: the end of my slides. So I got through them a little more quickly. I think the lack of polls. I got through pretty quickly. But I'm glad to to start a conversation. What resonates what questions you have for me for each other? I can. I will. In fact. 293 01:04:19.100 --> 01:04:27.409 Christine Keller: ends slideshow, so we don't have the the slideshow in our way. 294 01:04:27.560 --> 01:04:42.220 Christine Keller: and so can have a conversation or questions. Whatever is the the will of the group again? Thank you for the opportunity to share some of what we've learned with all of you. 295 01:04:42.950 --> 01:05:04.139 Rachel Borashko: Thank you so much, Christine. I appreciate the presentation that was really helpful, I know, at least for me and some really great nuggets in there. I know 1 point. Part of being helpful is being strategic, and I was like, I gotta cling in on the that's such a good point. You know, we get. Sometimes we'll get really focused on the next thing or the next ask. And it is, it is still being helpful to be strategic and thinking long term. So 296 01:05:04.200 --> 01:05:17.009 Rachel Borashko: I really appreciated that I will invite anybody. If you want to unmute and ask questions, please feel free to do so we do have some questions in the chat that I can pull from. Also, if nobody wants to speak up. 297 01:05:26.490 --> 01:05:53.609 Rachel Borashko: Oh, we're also we're too shy. We're too. Nobody wants to be the 1st one. Okay? So I. There were some questions that came through in the chat, so I can just read those for you, Christine, if you are comfortable with answering them. Okay. So when you were discussing participants, sense that they didn't have an adequate access to information and reports. You noted there wasn't a correlation with office size. What other factors do you think could be driving? What's apparently a common phenomenon. 298 01:05:58.331 --> 01:06:12.909 Christine Keller: Think it it it. So I'm I'm hypothesizing. I we did, you know. I I would need to do more research to to figure that that out. But I I think that 299 01:06:13.060 --> 01:06:37.976 Christine Keller: towards the end of the presentation I I think probably all of you notice when we started talking about data, literacy and reporting and information. It, it seemed like office size mattered, mattered less. So it seems like we as a profession are are 300 01:06:38.940 --> 01:07:01.240 Christine Keller: still looking for ways to. I, I always say, get the right information in the hands of the right people at the right time, which it just rolls off the tongue. But I think that is is still our challenge. How do we get timely, actionable information in the hands of users. 301 01:07:01.300 --> 01:07:29.239 Christine Keller: and especially when the world's changing so much. Our institutions are changing so much, at least for me. That's kind of what we just got done. Many of you, some of you were there. I saw you at our annual conference, and that this idea of timely actionable just in time information came through from so many conversations that that we had. 302 01:07:29.240 --> 01:07:52.632 Christine Keller: So if that's something we could start to unpack as a community and get together and get some strategies, I think we could. This is one of those opportunities where we could have really help all office sizes. So that's more of a comment and not an answer to your question. But that's kind of where I see some of the the 303 01:07:53.060 --> 01:07:56.549 Christine Keller: barriers that that are happening right now. 304 01:07:58.120 --> 01:07:59.090 Rachel Borashko: Thank you. 305 01:07:59.990 --> 01:08:09.489 Rachel Borashko: What are some traits or practices that contribute most to improved data literacy when a Cdo is in place, and can those be replicated by non-cdo leaders. 306 01:08:11.850 --> 01:08:18.747 Christine Keller: So that's a part that we we tried to dig in to 307 01:08:20.473 --> 01:08:41.680 Christine Keller: a little bit bit deeper, and we found in, and we did a separate survey of Ceos, and what we ended up doing. We found there were people on campuses, particularly in Ir, and ie. Offices that were kind of acting as de facto 308 01:08:41.680 --> 01:09:01.560 Christine Keller: cdos, which we found interesting. So they didn't have the role. They didn't have the title. The institution didn't have the role, but when they described what they were doing to engage with the community, it became clear that they were doing that that sort of role. 309 01:09:01.810 --> 01:09:04.059 Christine Keller: I I think I would. 310 01:09:05.540 --> 01:09:23.189 Christine Keller: I go back to some of what I found when I I did kind of a search about what research is out there about the effectiveness of cbo or research is maybe too strong award evidence or experiences or practices. 311 01:09:23.240 --> 01:09:52.359 Christine Keller: and I really think it is having a person with an institution wide perspective that can look at the various groups of stakeholders, have conversations with them, and figure out what the barriers are for them to to actually use data effectively, and to use that data to influence their decisions and take actions. 312 01:09:52.450 --> 01:10:12.619 Christine Keller: So rather, having one person with kind of a overall institutional view of those things is important. The other thing I think, that came through is having a person that can start to build connections and trust. 313 01:10:12.760 --> 01:10:36.360 Christine Keller: I know for many of us. We're like, of course, you use data. It's it's, you know, it's awesome. It's great. Give me more data. I couldn't give that presentation with all of the charts and graphs to to anybody of the senior leaders their eyes would glaze over. But we're like, Okay, give me more. What's behind that information. 314 01:10:36.430 --> 01:10:45.999 Christine Keller: And I think having someone who can focus on not just the data, but those relationships to build trust and understanding 315 01:10:46.540 --> 01:11:12.779 Christine Keller: to give people permission to ask those questions about data. I think, having both the relationship piece and the institution wide perspective. I think that's kind of what we're seeing with that Cdo role is someone dedicated to to those 2 pieces that I, at least from what I've seen in the literature and the practices and the case, studies those things really matter. 316 01:11:14.220 --> 01:11:24.310 Rachel Borashko: Makes a lot of sense to me, right? Like, if you if you've got a chief data officer with those connections, right? Maybe people know, then, where they can get the data. Who to ask for it. You might not even have that otherwise. 317 01:11:25.000 --> 01:11:25.860 Christine Keller: Yeah, and. 318 01:11:25.860 --> 01:11:26.570 Nick Humensky: Hey! Christine! 319 01:11:26.570 --> 01:11:28.709 Christine Keller: There's yes! Whoa! 320 01:11:29.140 --> 01:11:30.230 Nick Humensky: Hey? That was! I can hear you. 321 01:11:30.230 --> 01:11:32.280 Rachel Borashko: Good spotlight. So maybe we can, you know. 322 01:11:32.870 --> 01:11:33.370 Christine Keller: There we go! 323 01:11:33.715 --> 01:11:34.060 Rachel Borashko: Okay. 324 01:11:34.060 --> 01:11:43.320 Nick Humensky: I was the one who asked that question. I was the one who asked that question. I was just curious, if, like for the connections and the relationships part portion. Do you think that the title itself. 325 01:11:43.840 --> 01:11:49.440 Nick Humensky: a chief chief data officer, helps build that trust. People will look at that and think like. 326 01:11:49.650 --> 01:11:53.700 Nick Humensky: yes, let's definitely trust this person. Or is there a possibility for someone 327 01:11:54.250 --> 01:11:56.549 Nick Humensky: else without that title to help. 328 01:11:56.990 --> 01:12:01.360 Nick Humensky: you know, help themselves, gain that trust, and build those relationships. 329 01:12:07.010 --> 01:12:08.840 Christine Keller: Had a couple of anecdotes 330 01:12:09.660 --> 01:12:36.209 Christine Keller: that I will share, that the title in some situations does matter because it gives you credibility. It opens doors that may have not been open before. I know a couple of colleagues that in the past 6 months have moved into, and and they've been doing the work before. 331 01:12:36.260 --> 01:12:46.279 Christine Keller: but then they were given the title of Cdo, and it did make a difference. It it did it the that gave some 332 01:12:46.500 --> 01:12:54.520 Christine Keller: cache to to the work they were doing, and some more institution, wide recognition. 333 01:12:56.320 --> 01:13:17.039 Christine Keller: At the same time. When I think about my former boss at the University of Kansas. She operated as a Cdo. Even back 20 years ago, when I was there, and her title was Director of Institutional Research. 334 01:13:17.540 --> 01:13:47.009 Christine Keller: But she was one of those people who could build relationships, and she would build a relationship with everyone. She knew someone in every academic unit across the university, and she went to lunch with them. She was, she understood, the importance of building trust and relationship. So I think it's possible without it. And there are people who are doing that very, very successfully. 335 01:13:47.080 --> 01:13:58.720 Christine Keller: but I think it kind of gives you a jump start if you have the title and just makes it a bit easier. That that's that's just observation from from my experience. 336 01:13:59.590 --> 01:14:00.400 Nick Humensky: Thank you. 337 01:14:04.770 --> 01:14:10.619 Rachel Borashko: Got a couple more questions I can return to, but I'll open it up again in case anybody thought of anything that they want to come off of mute and ask. 338 01:14:18.200 --> 01:14:24.730 Rachel Borashko: so does air provide data literacy training to members? And if not, do you have any resources to use for self guided learning? 339 01:14:26.602 --> 01:14:33.000 Christine Keller: Yes, we do. We have something called the the Data Literacy Institute. 340 01:14:33.170 --> 01:14:50.480 Christine Keller: It's one of our newer offerings that is targeted at institutions rather than individuals. So what it is, it's a 12 week 341 01:14:50.640 --> 01:14:51.600 Christine Keller: program. 342 01:14:51.710 --> 01:15:04.879 Christine Keller: And you build a cross institutional cross, functional and across institutional for one institution. It's a cross functional cohort of individuals 343 01:15:04.880 --> 01:15:26.179 Christine Keller: that work together and walk through a course over the 12 weeks as well as work on several institution or related projects. So you're learning and working on a project that's directly related for your institution. As the group moves forward. 344 01:15:26.190 --> 01:15:34.260 Christine Keller: So I think we've, I think we're up to almost a dozen cohorts that have moved through the program. 345 01:15:34.876 --> 01:15:53.199 Christine Keller: It's it's a commitment. It's a 12 week program for so obviously senior leadership is important here to get the commitment to give people the release time. But we really true, try to build those cohorts from faculty Ir directors enrollment management, I mean. 346 01:15:53.544 --> 01:16:05.240 Christine Keller: one of our biggest fans was a professor of physics in one of the earlier cohorts, and he just raves about the experience and the connections he was able to build on his own. 347 01:16:06.090 --> 01:16:24.829 Christine Keller: So if if anyone's, you know, I'd be glad to to. If anyone's interested or wants to learn more. Glad to put you in touch with Jason Lewis is our deputy director, is the person who leads those efforts. Glad to connect you with Jason. If if you're interested. 348 01:16:25.720 --> 01:16:28.699 Rachel Borashko: Question about that just popped up in the chat. Is it online? 349 01:16:30.438 --> 01:16:37.770 Christine Keller: It is, it is it is a completely virtual some of the cohorts. 350 01:16:38.219 --> 01:17:02.019 Christine Keller: The cohort on the institution, so they'll often meet together and do do some of the work together. So there is kind of an in person on campus. But the the course itself is is online. It's live online. So people are in real time with an instructor that so you can 351 01:17:02.020 --> 01:17:09.390 Christine Keller: have interaction and share. There's some tutorials that you do outside of the online meeting? 352 01:17:09.991 --> 01:17:19.809 Christine Keller: So it's it's it's it's a combination of different modalities. But it's not an in-person experience. 353 01:17:21.450 --> 01:17:24.569 Rachel Borashko: I meant to stop recording before Q. And A, so I'm gonna stop now that I.