WEBVTT 1 00:00:00.690 --> 00:00:02.810 Nick Humensky: Alright. Thank you, Jorge. 2 00:00:03.250 --> 00:00:08.899 Nick Humensky: Good morning, everyone. My name is Nick Uminski, and I'm here with my colleague, Dr. Cindy Williamson. 3 00:00:09.080 --> 00:00:24.560 Nick Humensky: Tell you a little bit more about ourselves in just a moment. But 1st I want to thank everyone for being here today. We're very excited to showcase our collaborative dashboard effort, and hope that you walk away with some some practical insight and knowledge about how you could apply similar lessons to a project that you may be working on 4 00:00:30.590 --> 00:00:49.410 Nick Humensky: first.st Cindy is going to start out by telling you about how this project came to be, and then she's going to discuss how her team had a desire to move towards a more dynamic approach to analyzing evaluation data. And then I'm going to take over and I'll discuss the technical implementation and some of the details that I carried out to make this dashboard come to life. 5 00:00:49.650 --> 00:00:58.649 Nick Humensky: or these dashboards come to life, and then Cindy will end us up with a discussion about how this project helped her team align with campus and system level goals. 6 00:01:03.040 --> 00:01:13.969 Nick Humensky: So I am a data developer on the institutional effectiveness team under the greater academic affairs, research and student success team at the University of Tennessee System Administration. 7 00:01:14.430 --> 00:01:16.960 Nick Humensky: I handle a lot of dashboard creation 8 00:01:17.380 --> 00:01:23.469 Nick Humensky: back end data modeling automations ad hoc data requests things like that. 9 00:01:23.740 --> 00:01:24.560 Nick Humensky: Cindy. 10 00:01:25.530 --> 00:01:54.850 Cindy Williamson: So Jorge already mentioned it. But I'm the director of accreditation and assessment at Utc, and also our institutional accreditation liaison. So the data that we are talking about today is important for both of those things, accreditation and assessment. Our office is responsible for lots of data collection. So lots of surveys, including course, learning, evaluations, faculty rating of administrators, and then also responsible for the institutional 11 00:01:54.850 --> 00:02:08.779 Cindy Williamson: outcomes. Assessment process. That is, part of our saccoc requirements, but also is driven by the need to support students and and make sure that we're doing what we should 12 00:02:08.830 --> 00:02:09.940 Cindy Williamson: for them. 13 00:02:17.150 --> 00:02:20.100 Cindy Williamson: Okay? So this all started. 14 00:02:21.010 --> 00:02:28.049 Cindy Williamson: innocently enough. We were in. I think it was a power bi workgroup Zoom Meeting. 15 00:02:28.200 --> 00:02:43.450 Cindy Williamson: and I asked if we could get some help with power via because we had talked about in my office, wanting to be able to present data in a way that our stakeholders could interpret it, they could access it fairly easily. 16 00:02:43.560 --> 00:02:47.494 Cindy Williamson: so started with a conversation between 17 00:02:48.310 --> 00:03:01.290 Cindy Williamson: you know Jimmy and Nick's office. Rachel was there, and a couple of others, and then our office at Utc. So we really just described what we were thinking about, and 18 00:03:02.160 --> 00:03:11.939 Cindy Williamson: then also talked as as groups back and forth about you know, questions that came up, or ideas that we had along the way. 19 00:03:12.130 --> 00:03:38.519 Cindy Williamson: and we all know how important communication is. And when we were planning this presentation, I I it just made me think about. Yes, that's how this all started was clear communication, answering questions. Making sure that everybody understood the need. And then, on our end, making sure that we understood what was possible and how how we could make these dashboards look you know, based on 20 00:03:39.110 --> 00:03:41.660 Cindy Williamson: power bi parameters. 21 00:03:41.930 --> 00:03:49.839 Cindy Williamson: So we're able to you know, communicate openly and talk about the project. But then 22 00:03:50.660 --> 00:04:12.580 Cindy Williamson: there's so much data, all the data. So it was interesting to to kind of step back and think about our data from a different perspective. We decided to start with our outcomes, assessment information course, learning evaluations and senior exit exams, and we'll we'll go into a little more detail on those here. In just a second 23 00:04:16.720 --> 00:04:22.010 Cindy Williamson: already mentioned communication, we had an excellent cycle of communication back and forth. 24 00:04:23.790 --> 00:04:26.360 Cindy Williamson: We answered questions, provided data. 25 00:04:27.240 --> 00:04:38.740 Cindy Williamson: And I've mentioned this already. But we thought about what we were doing as part of our data collection process and presenting that data from a place that we don't often 26 00:04:38.890 --> 00:04:45.323 Cindy Williamson: get to be. You know, a lot of times we're distracted by the process. How are we gonna make this happen. 27 00:04:45.920 --> 00:04:55.491 Cindy Williamson: and here's what we want. Let's get it. But really taking a step back and thinking about possibilities, and the best way to make this 28 00:04:56.210 --> 00:05:11.669 Cindy Williamson: it well, make it an efficient process, but also something that our stakeholders could use. When they needed to. So really, thinking about the the bigger picture in order to make data informed decisions across campus. 29 00:05:19.100 --> 00:05:25.989 Cindy Williamson: So the 3 dashboards that we started with were the Ets proficiency profile, which is our senior exit exam. 30 00:05:26.110 --> 00:05:30.689 Cindy Williamson: It's required that all graduating seniors take this exam. 31 00:05:31.240 --> 00:05:38.949 Cindy Williamson: We do 3 administrations of it. Well, 3 semesters outcomes assessment information. 32 00:05:39.562 --> 00:05:41.070 Cindy Williamson: That's a lot. 33 00:05:41.470 --> 00:05:49.400 Cindy Williamson: Our office. We have a team. Well, we have 5 people and 3 who work specifically on outcomes, assessment review and feedback. 34 00:05:49.966 --> 00:06:07.820 Cindy Williamson: We report, we report these data multiple times a year, and then, of course, also report to Saccoc as required. And I left out that our our senior exit exam is also reported to Tec. So not just something that we use at our institution, but as part of our quality assurance, funding. 35 00:06:08.682 --> 00:06:19.739 Cindy Williamson: course, learning evaluations. Yes, results are available to faculty department heads, deans, administrators, other stakeholders. But we wanted a way 36 00:06:20.269 --> 00:06:41.940 Cindy Williamson: for these stakeholders to easily access what they need. What we have not right now is fine. Our platform is just fine. But the reporting capability is limited, and so we thought that a dashboard would be a great way to present that information in a way that can be interpreted by those who need who need to use the data. 37 00:06:45.940 --> 00:06:49.984 Cindy Williamson: Okay, this is where my sob, story starts. 38 00:06:50.830 --> 00:06:58.460 Cindy Williamson: so senior exit exam. Results were previously presented in excel. We'll show you some examples in a minute. But 39 00:06:59.070 --> 00:07:01.983 Cindy Williamson: yeah, just don't cry. Okay. They're bad. 40 00:07:03.130 --> 00:07:09.050 Cindy Williamson: and the way it's presented, which you'll see, it's not easy to interpret results. And 41 00:07:09.290 --> 00:07:29.549 Cindy Williamson: we want this data to be used, not just you know, by OA for T Hec purposes. But this is a lot of good information that we can use programmatically, departmentally. You know, in order to make changes, make improvements, data informed decisions. 42 00:07:31.100 --> 00:07:34.506 Cindy Williamson: so yeah, we really wanted to make sure that those who needed 43 00:07:35.700 --> 00:07:41.950 Cindy Williamson: that maybe even didn't know they needed it. But those who we thought should be using the data would have access to it. 44 00:07:46.560 --> 00:07:49.736 Cindy Williamson: This is it. I'm sorry. It's graphic. 45 00:07:50.320 --> 00:08:00.990 Cindy Williamson: it's it's terrible. This is what has been on our our website for several years. We usually have at least 5 years of data up 46 00:08:01.200 --> 00:08:19.719 Cindy Williamson: at a time. So imagine as a department head or faculty member or dean, you know, trying to make sense of this information. It's there we have proficiency scores. We have, you know, mean test scores mean scores for each subject on the senior exit. Exam. But 47 00:08:20.502 --> 00:08:34.850 Cindy Williamson: it's it's it's not fun. And I know that there have been difficulties in people accessing the results and getting what they need. But just like with these other data sets, we were constantly 48 00:08:34.909 --> 00:08:50.399 Cindy Williamson: and probably more for course, learning evaluations than anything else, but providing data through, you know, from ad hoc requests. So we had our process. But then, on top of that, we we have multiple requests for the data in a different format. 49 00:08:55.792 --> 00:08:57.840 Cindy Williamson: Our outcomes assessment data. 50 00:08:58.060 --> 00:09:07.290 Cindy Williamson: This is a relatively new you know, data set that we had. We have worked for about 10 years. 51 00:09:07.804 --> 00:09:14.015 Cindy Williamson: To get to where we are now with outcomes assessment. So we've improved that process. 52 00:09:14.720 --> 00:09:38.510 Cindy Williamson: I mentioned that there are 3 of us who work with the outcomes, assessment information and really 2 fantastic outcomes, assessment management analysts who review every piece of outcomes assessment information for every program, department, college office, everybody across campus, academic student support, administrative. 53 00:09:39.061 --> 00:09:57.150 Cindy Williamson: So we're talking about around 230 separate areas, separate offices or departments, and we have about 220 outcomes assessment contacts that we send information to send data to send our assessment results to our feedback. 54 00:09:57.833 --> 00:10:04.556 Cindy Williamson: So we do provide specific, detailed feedback. We'll see an example of that here in just a second. But 55 00:10:05.920 --> 00:10:23.470 Cindy Williamson: we want to know where and how assessment occurs. Yes, we want these areas to highlight strengths and weaknesses in order to see where they can focus their efforts and where more resources may be needed. There may be a need for changes to the curriculum 56 00:10:23.890 --> 00:10:30.574 Cindy Williamson: changes to their particular assessment process. All kinds of good information that can be gained 57 00:10:31.130 --> 00:10:33.240 Cindy Williamson: from this outcomes assessment data. 58 00:10:36.850 --> 00:10:55.493 Cindy Williamson: So this isn't as bad as the senior exit data, but the the 1st screenshot you see at the top, the top on the right. That is our internal spreadsheet. So we had a spreadsheet, have still have a spreadsheet that lists all departments, all programs 59 00:10:56.070 --> 00:11:12.630 Cindy Williamson: and then contacts for those departments, and then across the top you'll see that the columns are. It's kind of hard to see on here, I'm sure, but the columns include deadlines, dates where certain pieces of our outcomes assessment information is 60 00:11:13.035 --> 00:11:22.769 Cindy Williamson: so when it's supposed to be reported, so we make notes on here, follow up with those who don't have what what they're supposed to have at the time. 61 00:11:23.261 --> 00:11:31.700 Cindy Williamson: And then that second screenshot is our outcomes assessment rubric. So that's 1 of the big improvements that we've made. 62 00:11:31.730 --> 00:11:54.509 Cindy Williamson: and that's been in the past 3 or 4 years. It's a rubric that has dimensions aligned with each requirement for the outcomes assessment process. So each outcome that's developed is supposed to have specific information. And so that's what we're looking at with that rubric. 63 00:11:55.084 --> 00:12:05.265 Cindy Williamson: Nick, you can go on to the next one. So we have that rubric feedback that we provide. But then also we provide 64 00:12:05.900 --> 00:12:29.149 Cindy Williamson: more qualitative feedback. So specifically like, here's your score. But here's specifically what we were looking for and what we didn't see. Or here's where you did well keep doing that. So we send all this feedback via email. We also post it in anthology planning, which is our platform that we use for outcomes assessment data collection. 65 00:12:30.630 --> 00:12:47.489 Cindy Williamson: and this information is, yes, helpful to individual departments, but we were thinking that having a dashboard would make it more accessible for the larger units. So the the colleges and units 66 00:12:48.150 --> 00:12:59.519 Cindy Williamson: they could access the information. And then, like I already said, you know, make data, informed decisions, improvements. And those responsible for outcomes assessment 67 00:12:59.760 --> 00:13:14.219 Cindy Williamson: typically only see their information, their feedback, that we provide. So this is a way to for multiple departments to have some kind of comparison as as far as what they're saying for themselves. 68 00:13:18.430 --> 00:13:24.048 Cindy Williamson: And everybody's favorite data collection process is course, learning evaluations. 69 00:13:24.960 --> 00:13:46.389 Cindy Williamson: it's always a hot topic, I'm pretty sure, on every campus across the country, probably across the world anyway. So currently, administrators, deans, department heads faculty can access results through the platform. I mentioned, though, that it is not user friendly and 70 00:13:46.784 --> 00:13:56.639 Cindy Williamson: the reports that can be generated through that platform are are lacking. So we have a ton of ad hoc requests. That come through 71 00:13:57.380 --> 00:14:01.904 Cindy Williamson: for this cle data in in different formats. 72 00:14:03.650 --> 00:14:24.239 Cindy Williamson: and there is comparative data in that platform. But that's 1 of the main things that I think we're asked about even though they can access it. It's not easy to access and having something right there already available, ready for people to use without making a separate request. I mean, that just just sounds like. 73 00:14:24.450 --> 00:14:32.670 Cindy Williamson: you know, the ultimate way to be with your your data. Processes. But so 74 00:14:33.260 --> 00:14:49.960 Cindy Williamson: lots of reasons that cle data is important. Jorge actually hit on that. And in his introduction, this is this is for faculty. They can use it in different ways, including, you know, during their promotion and tenure efforts. 75 00:14:52.890 --> 00:15:06.760 Cindy Williamson: These next couple of slides are examples again. Still, not as bad as senior exit exams. But it's not. It's not great. We you can see that there's so many different ways. We're asked to format the results 76 00:15:07.080 --> 00:15:11.190 Cindy Williamson: on the left hand side. You'll see that it's by 77 00:15:11.780 --> 00:15:15.090 Cindy Williamson: cle question, and then also about term 78 00:15:16.094 --> 00:15:24.159 Cindy Williamson: and then on the right hand side, we've also got it broken down by response type and question. 79 00:15:24.350 --> 00:15:34.840 Cindy Williamson: Then on the bottom, it's, you see, the comparison data there. So we have the program, the department, the college, and then the university. But that's also broken down by semester 80 00:15:36.360 --> 00:15:38.769 Cindy Williamson: and then this next one 81 00:15:40.330 --> 00:15:48.125 Cindy Williamson: is is broken down. Even further, we have as part of this request. Well, both of these requests. I think we 82 00:15:48.780 --> 00:16:02.690 Cindy Williamson: provide individual instructor data. And and that's for the department, right? That's not something that that's going out to everybody. But we have the app, the mean scores by course, modality also by term. 83 00:16:03.725 --> 00:16:08.440 Cindy Williamson: And then we've got the response 84 00:16:08.890 --> 00:16:23.899 Cindy Williamson: averages means by term and by question. So anyway, you get the picture like this is just a lot of data and having it displayed in a way that is functional, that can be interpreted and used is is what we needed. 85 00:16:27.900 --> 00:16:31.359 Cindy Williamson: And I told Nick that this is probably an 86 00:16:32.010 --> 00:17:00.978 Cindy Williamson: an underestimate of how much time this is actually saving us. When you think about updating spreadsheets with new data multiple times a year. Senior exit, we only do once a year. But then outcomes assessment. There's 3 or 4 updates a year that we do to that data. And it's aligned with those deadlines that that they have throughout the cycle. And then, Cle, data is is updated all the time as well. 87 00:17:02.020 --> 00:17:07.709 Cindy Williamson: at the least 3 times a year, once for each term. 88 00:17:13.190 --> 00:17:13.889 Nick Humensky: Alrighty. 89 00:17:14.770 --> 00:17:16.250 Nick Humensky: Thank you, Cindy, for that. 90 00:17:16.440 --> 00:17:23.990 Nick Humensky: Now I'm going to take over, and I'm going to walk you through the pieces of the technical implementation, and how we got this dashboard to be 91 00:17:27.420 --> 00:17:38.199 Nick Humensky: alright with any project like this or any sort of dashboard project. It's always good. It's a good idea to start out with a solid requirements gathering meeting. 92 00:17:38.350 --> 00:17:43.509 Nick Humensky: So we have 3 dashboards, we know. And for each I want detail on the following items. 93 00:17:44.560 --> 00:17:59.090 Nick Humensky: data source. So where is this coming from? Is it from a software? Is it a flat file or some sort of Api that we can connect to? Also does the structure of the data change over time. For example, do the number of columns or fields shrink or grow? 94 00:17:59.590 --> 00:18:06.030 Nick Humensky: What is the frequency in which you're going to be receiving the data. Is it monthly, yearly or by term? 95 00:18:06.150 --> 00:18:17.509 Nick Humensky: This helps me decide whether or not I want to have the data refresh pretty much instantly if it's very frequent, or have it set up on some sort of schedule to refresh the data more periodically, if it's less infrequent. 96 00:18:19.200 --> 00:18:29.319 Nick Humensky: what is the volume of the data. So each time that you receive a batch in the cadence of the frequency, how many rows are coming in with it? Is it hundreds, thousands, millions. 97 00:18:29.960 --> 00:18:33.569 Nick Humensky: Spoiler, alert, we went with storing the data on 98 00:18:34.280 --> 00:18:40.040 Nick Humensky: sharepoint and excel files. And what this volume of data piece helped me decide was. 99 00:18:40.500 --> 00:18:55.359 Nick Humensky: if I wanted to keep the data all in one spreadsheet and just append rows to it. Or if there's just too much too many rows and a lot of transformations that needed to happen to where I just wanted the stakeholder to go in and be able to drop a file and have power bi refresh it. 100 00:18:55.700 --> 00:19:20.809 Nick Humensky: And to that matter, the transformations I like to get with the stakeholder and understand exactly the process that they have where they take the data from the raw format to the presentable format. I like to mimic their actions as closely as possible in whatever automation tool I'm using in this case, it's power query. It's similar to what Justin said yesterday. If you were on for the panel discussion, our job is to help the stakeholder 101 00:19:21.050 --> 00:19:30.600 Nick Humensky: not worry about the data or any of the manual pieces. Let us take care of all of the automation so that you can focus on what your area of expertise is. 102 00:19:31.040 --> 00:19:33.480 Nick Humensky: And then finally, security. 103 00:19:34.640 --> 00:19:38.839 Nick Humensky: Are there any sensitive pieces of information that we want to hide or protect? 104 00:19:38.980 --> 00:19:42.959 Nick Humensky: Or is there any sort of row level security that we want to implement, such as 105 00:19:43.300 --> 00:19:46.389 Nick Humensky: keeping departments from seeing each other's data. 106 00:19:46.850 --> 00:19:49.080 Nick Humensky: And so with that. 107 00:19:50.240 --> 00:20:03.119 Nick Humensky: this is sort of a high level piece or screenshot of what I have for all 3 of the dashboards the course learning, evaluation, and the senior exit exam. Dashboards both come from an external software. 108 00:20:03.220 --> 00:20:15.630 Nick Humensky: Unfortunately, there was no way to directly connect to them and get automatic access to the data. So we had to go with the next thing. The next best thing, which is a export to a tabular data source such as a spreadsheet. 109 00:20:16.010 --> 00:20:20.859 Nick Humensky: The outcomes assessment dashboard is just a manually created spreadsheet maintained by Cindy's team 110 00:20:21.300 --> 00:20:26.969 Nick Humensky: for the frequency of the data. Both the Senior exit Exam and outcomes assessment dashboards 111 00:20:27.810 --> 00:20:37.520 Nick Humensky: are updated about once a year. The outcomes assessment might be Updated 2 or more. But the course. Learning evaluation is a different story that is updated once per term. 112 00:20:38.040 --> 00:21:05.059 Nick Humensky: Again, for the volume outcomes assessment and senior exit exam are similar. It's just hundreds high, hundreds of rows. And then course, learning evaluation is a massive data set. It's got tens of thousands of rows in each file that's added to the sharepoint folder, as far as transformations go, outcomes. Assessment and senior exit exam are pretty simplistic. You're just adding new rows to the existing spreadsheet. But the course, learning evaluation requires some complex transformations, and we'll talk about that in a moment 113 00:21:05.150 --> 00:21:15.390 Nick Humensky: and for security. But the course learning, evaluation and the senior exit exam dashboards have some names in them. The the free text questions might have 114 00:21:15.670 --> 00:21:27.669 Nick Humensky: the professor names in the course, learning evaluation, and the senior exit exam records the students, 1st and last names in in separate fields whenever they take the exam, and we take measures to protect that which I'll showcase in a second 115 00:21:28.750 --> 00:21:39.210 Nick Humensky: with all of that in place. I determined that the easiest and best way to move forward with this would be to store the data in excel files in a structured manner in a sharepoint folder. 116 00:21:39.420 --> 00:21:40.270 Nick Humensky: So 117 00:21:40.860 --> 00:22:03.439 Nick Humensky: you have the General Document library that comes with standard sharepoint sites underneath that I created a folder called assessment data, dashboard data sources. And I created this to house everything under there and separate it from any other projects that they might have within their team's sharepoint site, but I wanted them to be able to have access to the site so that they could go in and drop the data files in there 118 00:22:03.950 --> 00:22:10.460 Nick Humensky: underneath that I have a folder for each of the 3 dashboards, and then you'll notice 119 00:22:11.970 --> 00:22:17.599 Nick Humensky: there are right underneath the level, right underneath the dashboard folder are data files 120 00:22:18.690 --> 00:22:24.099 Nick Humensky: for each of the dashboards, and then I also created a separate dimensional folder 121 00:22:24.250 --> 00:22:30.460 Nick Humensky: that will allow you to drop in any sort of if you're familiar with standard data modeling or reporting 122 00:22:31.341 --> 00:22:35.899 Nick Humensky: acumen dimensional tables just provide additional context 123 00:22:36.380 --> 00:22:39.109 Nick Humensky: and labels for the factual data. 124 00:22:43.790 --> 00:22:54.460 Nick Humensky: So now that I have the data stored, it's time to start pulling it into power bi. And whenever you create a connection to data and power bi, you want to make sure that you're setting up authentication and privacy levels appropriately. 125 00:22:55.500 --> 00:23:04.989 Nick Humensky: Here's a reiteration of the security. You have the names in both the course, learning, evaluation and the senior Exit Exam. Nothing really an outcomes assessment. It's just department names and scores 126 00:23:05.486 --> 00:23:17.640 Nick Humensky: for the authentication on all 3. I wanted to do Microsoft credentials. It makes the most sense because everyone who's going to be using this as part of the University of Tennessee system, so they will be on the Microsoft tenant 127 00:23:18.120 --> 00:23:19.550 Nick Humensky: for privacy levels. 128 00:23:20.210 --> 00:23:21.949 Nick Humensky: I went with 129 00:23:22.290 --> 00:23:28.769 Nick Humensky: private, organizational and private. There's 4 different privacy levels. Whenever you're connecting data in power. Bi, there's none. 130 00:23:29.000 --> 00:23:31.449 Nick Humensky: public, organizational and private. 131 00:23:31.700 --> 00:23:41.459 Nick Humensky: The reason I went with private for course, learning, evaluation and senior exit exam is because of the names. And what private means is that it just isolates that data. Query, that specific data query 132 00:23:41.740 --> 00:23:49.360 Nick Humensky: from being merged or appended to any other data sets and creating some sort of unwanted inference about maybe the student 133 00:23:49.950 --> 00:23:56.839 Nick Humensky: so the private isolates it from being connected or merged with an even other private data sets 134 00:23:57.600 --> 00:24:09.240 Nick Humensky: for outcomes. Assessment. I went with organizational. That's a step up from private. It's just for more trusted internal resources. I didn't. Blanket set everything to private, because I wanted to be intentional about my decisions of 135 00:24:09.370 --> 00:24:12.159 Nick Humensky: the level of privacy that I set for the dashboards. 136 00:24:16.680 --> 00:24:21.460 Nick Humensky: So with the dashboard or with the senior exit exam and outcomes assessment. 137 00:24:21.890 --> 00:24:32.270 Nick Humensky: it's pretty simple. You just connect to a single spreadsheet, and then there's minimal transformations that need to be done to it. But the course learning evaluation is a monster, and I'm going to show you 138 00:24:32.730 --> 00:24:44.639 Nick Humensky: some of the steps that I took to be able to create make this digestible format for power. Bi. Now, here we have a it's a kind of a cumbersome looking spreadsheet, but I'll I'll describe it the best I can. 139 00:24:44.840 --> 00:25:02.030 Nick Humensky: This is just one terms, file that comes from Cindy and her team. For one term. Of course, learning evaluation survey data. Within each file there are different sections or templates of data responses. And that's just for the different colleges that 140 00:25:02.130 --> 00:25:10.339 Nick Humensky: they might have different templates, which just means they have some additional questions that they ask for the students within that particular particular program. 141 00:25:10.900 --> 00:25:18.990 Nick Humensky: They all start out like this where there's it skips a few rows and 142 00:25:20.160 --> 00:25:29.590 Nick Humensky: it has a little header here. It says raw results for template brsc. That's just the honors college at Utc. It'll skip a row, and then it has the 143 00:25:30.520 --> 00:25:35.950 Nick Humensky: headers down here like this, and then so all the responses 144 00:25:36.150 --> 00:25:43.090 Nick Humensky: and brsc usually has a couple of 100 rows. I did this little row skip, to try and show 145 00:25:43.220 --> 00:25:48.219 Nick Humensky: how like a different section in the data without having it scroll out and being extremely scrunched. 146 00:25:48.670 --> 00:25:51.539 Nick Humensky: So this right? Here is the 147 00:25:51.940 --> 00:25:54.760 Nick Humensky: last row of the brsc data. 148 00:25:55.470 --> 00:26:01.589 Nick Humensky: and then the next section starts raw. Result, raw results template for course, evaluation skips row 149 00:26:02.240 --> 00:26:08.580 Nick Humensky: more more data, and there's a varying number of sections within each spreadsheet. There is a varying number of rows. 150 00:26:08.720 --> 00:26:15.490 Nick Humensky: and there's a varying number of columns like I mentioned, because some might have additional columns or additional questions. 151 00:26:15.800 --> 00:26:17.199 Nick Humensky: So if you notice 152 00:26:19.090 --> 00:26:25.880 Nick Humensky: starting at this fall, 2020 right here and all the way to the right are all question columns, and it could go to column 153 00:26:26.580 --> 00:26:29.289 Nick Humensky: Z. It could go to column A, B, 154 00:26:29.450 --> 00:26:37.350 Nick Humensky: and as such, when you pull this into power. Bi, you can't simply filter out the blanks and just stack them on top of each other. We have to do a little transformation. 155 00:26:37.520 --> 00:26:45.389 Nick Humensky: Do some unpivoting and get an equal number of fields so that you can create one long, narrow table that's more easily digestible by power. Bi 156 00:26:46.130 --> 00:26:48.260 Nick Humensky: and I included this little graphic 157 00:26:49.570 --> 00:26:58.820 Nick Humensky: to help you visualize what unpivoting is. If you don't know what it means. I'm sure everyone knows what it means, but I wanted to include it just in case someone might not be familiar with unpivoting. 158 00:26:59.030 --> 00:27:01.470 Nick Humensky: Let's say you have a 159 00:27:02.670 --> 00:27:09.520 Nick Humensky: survey that's sent out. And these 3 people respond to the 3 questions. Here you have these descriptive fields, the dimensional fields. 160 00:27:09.900 --> 00:27:19.799 Nick Humensky: such as their name and department? And then you have all the questions that they answered the question. Headers are the attributes, and then the respective values that they answer for each question below 161 00:27:19.940 --> 00:27:26.019 Nick Humensky: unpivoting. What that does is. It transposes the column headers down to a single column like this. 162 00:27:26.370 --> 00:27:28.220 Nick Humensky: And then the matching values. 163 00:27:28.740 --> 00:27:39.979 Nick Humensky: This does create create some duplication and redundancy in the descriptor fields. But it's necessary for this particular data set. And it's a much easier format to pull into power bi to work with. 164 00:27:41.790 --> 00:27:51.000 Nick Humensky: So this is what I this is what I had in. This is just one of the sections that I would work on in the Cle. Dashboard. So I keep all the 165 00:27:51.560 --> 00:28:04.009 Nick Humensky: columns between term and professor name, and then anything to the right of that is going to be an attribute column, and I have to use some complex power query to be able to dynamically figure out where the end is, and then unpivot it. 166 00:28:06.080 --> 00:28:13.669 Nick Humensky: And so here's what all the power query steps for that look like. I know this is a lot, and I don't expect you to read, and really 167 00:28:14.420 --> 00:28:23.730 Nick Humensky: I don't expect you to read all of it. But I have this in here for just a little heads up for best practices in terms of naming your steps when you're working in power. Query. 168 00:28:24.010 --> 00:28:44.079 Nick Humensky: You want to be as descriptive and specific as possible to help yourself whenever you come back in several months at a time to remember exactly what's happening in the power query. And also if someone else inherits the dashboard, they'll be able to go in and troubleshoot a step, because they'll be able to see what's happening in the steps. 169 00:28:45.310 --> 00:28:46.570 Nick Humensky: I left 170 00:28:47.750 --> 00:29:10.790 Nick Humensky: these 1st few steps as their general, the generic names that are generated by power, bi or power query. For a couple of reasons. One. These are automatically generated whenever you connect a sharepoint file to power Bi. They don't really do anything for the purposes of troubleshooting. It's just creating some additional helper files to pull the data in. And 171 00:29:10.860 --> 00:29:21.577 Nick Humensky: also I wanted to showcase how these generic step generic name steps don't really mean anything so filtered hidden files. One? What does that mean? Well, 172 00:29:22.280 --> 00:29:30.569 Nick Humensky: yeah, you want to be specific, because there could be multiple filtered rows, filtered rows, one filter rows, 2 things like that. And so everything below that 173 00:29:32.070 --> 00:29:44.229 Nick Humensky: and over to the right are all the steps that I created for this particular data set in order to unpivot all the columns, all the sections, and combine all the files together. 174 00:29:47.380 --> 00:29:53.400 Nick Humensky: And so, after all is said and done, you have one huge table that has. 175 00:29:53.880 --> 00:29:58.569 Nick Humensky: I think, upwards of 3.9 million of rows or 3.9 million rows. 176 00:29:58.980 --> 00:30:02.829 Nick Humensky: That's quite a lot of data to work with. And because the 177 00:30:03.730 --> 00:30:09.289 Nick Humensky: the value column is going to have a combination of both. The free text answers as well as the numeric answers. 178 00:30:09.530 --> 00:30:18.100 Nick Humensky: it's quite difficult to perform mathematical Dax calculations on fields like that, unless you want the measures to be extremely cumbersome 179 00:30:18.470 --> 00:30:40.990 Nick Humensky: as such. I separated the big table that's aggregated into 3 fact tables, and I did this to make the Dax easier, but group them logically together. So it's a little easier and more clear to work with. So I have a table that has groups together all of the likert questions. And that's all of those questions where you rank between strongly disagree or strongly agree. 180 00:30:41.160 --> 00:30:46.880 Nick Humensky: We have fact text, and that's just all the free text responses and questions. And then we have. 181 00:30:47.250 --> 00:30:54.020 Nick Humensky: I just call this counts. It's any other remaining question. They're pretty much multiple choice questions, such as. 182 00:30:54.140 --> 00:30:58.950 Nick Humensky: what modalities did you use to be successful in this course? Choose all that apply items like that. 183 00:30:59.120 --> 00:31:09.909 Nick Humensky: And then this 4th one over here is a separate data sort set that comes from the same tool as the surveys. But it's just a different spreadsheet. It's for the response rates, and I have that separately. But I 184 00:31:10.130 --> 00:31:17.979 Nick Humensky: am able to connect them all to the dimensional tables based on term and college and department connections like that. 185 00:31:22.190 --> 00:31:27.130 Nick Humensky: So once I have the data in power Bi, and you start creating measures. And you have all that good stuff 186 00:31:27.420 --> 00:31:43.839 Nick Humensky: you want to start worrying about the visual styling. And so I got some branding assets from Cindy, including the Utc approved colors and their hex codes. And then I also asked for some vector images, Logos of her office, so that I can include that on the dashboard. 187 00:31:43.840 --> 00:32:01.389 Cindy Williamson: Hey, Nick? I just wanna add in really quick this is kind of funny. So when Nick asked for the graphics or Logos, I asked him which ones he wanted, and he told me, but I had no idea what he was saying. Like the words did not, so I was like, well, which ones are those, and he's like, just send them all. Just send them all to me. 188 00:32:01.580 --> 00:32:02.170 Cindy Williamson: So. 189 00:32:03.040 --> 00:32:07.699 Nick Humensky: Yeah, they these were helpful to add that sort of personal touch to the dashboards. 190 00:32:09.730 --> 00:32:31.890 Nick Humensky: So here is an initial iteration of the outcomes assessment dashboard. It's called. I initially had it called the Assessment Review. But it's now the assessment or the outcomes assessment dashboard where I had across the top the all the different filters for the dashboard. So that way I could create as much real estate in the body area, keep it wide, and have all the visuals large, so that people can see them. 191 00:32:32.510 --> 00:32:37.339 Nick Humensky: But I did the same thing for the senior exit exam. Where I had the filters across the top 192 00:32:37.640 --> 00:32:46.609 Nick Humensky: for the course, learning evaluation. There were too many filters to have it horizontally across the top, so I had to make them vertical, and I didn't want to have 2 dashboards where 193 00:32:46.840 --> 00:32:51.629 Nick Humensky: they were the filters were horizontal, and then one was vertical. I wanted everything to be consistent. 194 00:32:51.930 --> 00:32:56.620 Nick Humensky: so I took some time to think it through. And this is the format that I ended up coming up with 195 00:32:57.460 --> 00:32:58.810 Nick Humensky: is the following. 196 00:32:59.160 --> 00:33:17.589 Nick Humensky: I have a narrower navigation bar on the left where I'll have some buttons, such as a filter button to show and hide a filter pane so that you can apply filters. But you don't have to have a consistent, persistent filter pane on the left that takes up space where you could have more visuals. 197 00:33:17.710 --> 00:33:24.970 Nick Humensky: and then other page navigation icons, and then a button for a tutorial that'll show you how to walk through the dashboard and use it 198 00:33:25.200 --> 00:33:35.429 Nick Humensky: up on the top. We have a header section. That's where I have the dashboard title, a label on when the data was last updated, and then I also will have a text box that contains 199 00:33:35.670 --> 00:33:46.369 Nick Humensky: which filters have been applied, so that after you hide the filter pane you can understand and remember what the context is that you're looking at. So you don't have to keep toggling the pane back on, back and forth. 200 00:33:46.770 --> 00:33:48.880 Nick Humensky: And then just a main data section. 201 00:33:49.770 --> 00:34:18.579 Nick Humensky: Also fun fact. I found out that you can create backgrounds and pretty looking visuals in Powerpoint. This background was actually created in Powerpoint. You can do a little more complex, and fancy visuals, such as the gradient borders and transparency effects within Powerpoint, and then save the slide as an Svg. And set that to your canvas background and do an Svg. Just so it doesn't lose the quality whenever it scales and expands to a bigger size. 202 00:34:18.870 --> 00:34:41.380 Cindy Williamson: Hey, Nick, one other quick thing. So it was interesting, as Nick was doing all this stuff. When he would find something new or create something. He he would, you know, message me and be like, can you get on real quick? I got something to show you. I just found this out or so it was like, I don't know. It's just kind of refreshing to to know that like this was exciting right on both 203 00:34:41.389 --> 00:34:46.920 Cindy Williamson: things. This wasn't just like me being excited about what what the final result was. Gonna be so. 204 00:34:47.510 --> 00:34:48.679 Nick Humensky: Definitely. 205 00:34:49.370 --> 00:34:57.470 Nick Humensky: And so just to reiterate, here's that initial iteration of the outcomes assessment dashboard the before. 206 00:34:57.570 --> 00:35:01.274 Nick Humensky: And then here's the after. It's a little more clean looking with the 207 00:35:02.430 --> 00:35:06.390 Nick Humensky: gradients from the approved Utc colors, and 208 00:35:08.070 --> 00:35:13.970 Nick Humensky: Yeah, yeah, right? It's it's super fancy. So over here. This is the 209 00:35:14.430 --> 00:35:39.830 Nick Humensky: label that shows when it was last updated. Here is the text box that shows the filters that are applied, and this is the filter button that shows and hides the filter pane. Another little thing that, I added, is this number here to just add another visual cue to remind you how many dimensions have been filtered. The outcomes assessment doesn't have any other pages. It's just one. So there's nothing here, but there is the how to use button down here. I'll show you what that looks like in a moment. 210 00:35:42.120 --> 00:35:46.819 Nick Humensky: Here's the senior exit exams data, same format, similar format. 211 00:35:47.440 --> 00:35:58.030 Nick Humensky: this one actually has multiple pages. So right now, you're viewing the scores. But there's also a little radar gauge, but button to go to the proficiency profile. 212 00:36:00.490 --> 00:36:06.290 Nick Humensky: and finally the the big one, the course learning evaluation. 213 00:36:06.580 --> 00:36:09.760 Nick Humensky: This has the most pages, and 214 00:36:10.400 --> 00:36:17.090 Nick Humensky: on the front home page. I wanted to include the the the minimum required 215 00:36:17.380 --> 00:36:25.610 Nick Humensky: measures that Cindy asked for. Just so it's right front and center for people to be able to go in and look at, and slice and dice. However, they see fit with the filters 216 00:36:26.600 --> 00:36:33.130 Nick Humensky: whenever you click on the filter button. This is what it looks like pops up, and you can adjust the slicers. 217 00:36:33.450 --> 00:36:38.810 Nick Humensky: You can see that they are applied up in the text box behind a little transparent background, just to 218 00:36:39.160 --> 00:36:43.530 Nick Humensky: show that something is currently in the front foreground of the visuals. 219 00:36:43.890 --> 00:36:45.300 Nick Humensky: and then you can hide it. 220 00:36:45.850 --> 00:36:50.799 Nick Humensky: And then this is what the tutorial looks like when you click on this, how to use button down here. 221 00:36:51.000 --> 00:37:13.350 Nick Humensky: It brings up this page that says, you know, welcome to the dashboard. I want a tour, or you can just click no and go right back into working. But whenever you click this, the following happens, it starts a little sequential coach mark one of those sort of self guided tutorials on how to use the dashboard. I didn't include a screenshot for every one of them, but I have it walked through 222 00:37:14.680 --> 00:37:16.890 Nick Humensky: a few pieces for the navigation bar. 223 00:37:17.390 --> 00:37:24.150 Nick Humensky: and then it'll walk through the filters. It'll walk you through how to use the filter page. And then the 224 00:37:24.860 --> 00:37:28.679 Nick Humensky: this little section is for the data, and it just walks you through looking at the data. 225 00:37:30.800 --> 00:37:33.530 Nick Humensky: So we're really excited to get these out and rolling. 226 00:37:34.810 --> 00:37:39.810 Nick Humensky: and I'm going to pass it back to Cindy to discuss the final strategic alignment steps. 227 00:37:40.850 --> 00:37:43.155 Cindy Williamson: Wanted to mention to Nick that 228 00:37:43.800 --> 00:37:51.312 Cindy Williamson: That help feature in the dashboards. Dashboards, I think, is something that's going to be really helpful. 229 00:37:51.900 --> 00:38:01.519 Cindy Williamson: of course we're there. Nick's there. Whoever is there to answer questions. But I think, having kind of a tutorial like Walkthrough situation is. 230 00:38:01.820 --> 00:38:07.045 Cindy Williamson: it's gonna be really nice. So yeah, strategic alignment. 231 00:38:08.380 --> 00:38:11.529 Cindy Williamson: When we 1st started this, I obviously did not. 232 00:38:12.690 --> 00:38:15.489 Cindy Williamson: Excuse me, I obviously did not think about it on 233 00:38:15.720 --> 00:38:28.096 Cindy Williamson: on this level, right about this collaboration between campus and system. But I am glad to have had the opportunity to think about it in that way, because this is something that's really important. 234 00:38:28.830 --> 00:38:41.510 Cindy Williamson: of course, ideally, we want goal alignment, right between the system utc our office, any office on our campus but that cross campus collaboration, I think, is super important. 235 00:38:42.020 --> 00:39:08.139 Cindy Williamson: So open data sharing across departments and units and then also system wide. Yes, we report data to system. Yes, we report to T. Hec, but I think that reciprocal data transfer. And while in this case, like what Nick was doing wasn't technically, you know, data transferred. It was a back and forth process. That 236 00:39:08.560 --> 00:39:17.265 Cindy Williamson: required efficient communication in order for us to you know, both get what we need for our offices for our 237 00:39:17.890 --> 00:39:21.237 Cindy Williamson: you know, employers to get what we need. So 238 00:39:21.900 --> 00:39:43.040 Cindy Williamson: I think when we think about that clear communication and sharing data. It's not just the data that we're sharing. There's you know, contextual information that we can provide. And then the next level is like talking about the process involved, for all of this. 239 00:39:43.540 --> 00:40:12.980 Cindy Williamson: But the sharing of data is is helpful for us. Yes, but also our stakeholders, and that communication really facilitates solid partnerships, I think, across the system and between institutions. So we can work together and collaborate. We can share ideas about processes, projects on some level. We all do many of the same things, regardless of which institution we're at 240 00:40:13.521 --> 00:40:33.760 Cindy Williamson: and then having the conversations that are are beneficial to everyone involved. And then, of course, being able to meet requirements for our creditors. Programmatic, institutional also for T, Hec, and then for the individual campus campuses to be able to provide what system needs to. 241 00:40:34.690 --> 00:40:47.316 Cindy Williamson: I mean, you can go to the next slide, Nick. But I wanted to mention to elizabeth Pemberton is on here. I'm gonna call you out, Elizabeth. But she reached out to us when she saw 242 00:40:47.810 --> 00:41:06.909 Cindy Williamson: that this was going to be one of the sessions for the ie. Summit, and that just like spoke to me. I was like, yes, we will talk about all day about this. So Nick and I met with her, and so she had a little sneak, sneak, peek of what we were going to be talking about. But that's exactly what we want from this right? 243 00:41:07.600 --> 00:41:08.730 Cindy Williamson: so 244 00:41:10.900 --> 00:41:25.129 Cindy Williamson: aligned goals talking about continuous improvement. But that data gives us a way to make informed decisions, to guide our planning, not just seeing what our planning needs to be or where we think we need to go. But 245 00:41:26.410 --> 00:41:47.949 Cindy Williamson: yeah, there's just so much to it and the data that we have in our new dashboards. They're not live yet. But stay tuned but this they absolutely facilitate continuous improvement and individual faculty staff. Again. Department heads. Deans. 246 00:41:48.060 --> 00:42:04.249 Cindy Williamson: vice chancellors, you know, can really leverage the data for enhancement of their programs and services and make sure that they're allocating resources in the appropriate places. And then I'll mention Sacco. See? One more time. But our creditor 247 00:42:04.370 --> 00:42:12.529 Cindy Williamson: wants to know that we're following our processes and that we're we're continuously improving. And I think something like this is exactly 248 00:42:13.620 --> 00:42:19.408 Cindy Williamson: something, you know. One thing that we can use to provide evidence of that continuous improvement. 249 00:42:20.520 --> 00:42:28.813 Cindy Williamson: so yeah, we can provide artifacts. We can provide assessment results. But I think, having them in a way to 250 00:42:29.400 --> 00:42:39.280 Cindy Williamson: a way that someone from outside our system, our campus can look at it and answer their own questions is is super helpful. 251 00:42:43.430 --> 00:43:07.899 Cindy Williamson: And Dick and I also talked about strategic alignment across levels. So yes, we have ut system. We have an overarching strategic vision. We have priorities that have to be well communicated, and then campuses can align with those priorities. So then, down to the campus level, we have our strategic plan. 252 00:43:08.508 --> 00:43:17.791 Cindy Williamson: That is, in alignment with system priorities and then executing our plan and making changes at the campus level. 253 00:43:19.110 --> 00:43:31.267 Cindy Williamson: one more way to show evidence of continuous improvement, but also it gives a place for individual departments, units areas, whether it's colleges or divisions. It gives them a place 254 00:43:31.830 --> 00:43:50.349 Cindy Williamson: to align what they're doing. We we want to see that all the way through our organizational structure. So if we're talking about assessment and accreditation, accreditation, yes, start there. But think about what that means, as you you know, look higher in the organization, or. 255 00:43:50.650 --> 00:43:52.409 Cindy Williamson: you know, up to the system. 256 00:43:53.928 --> 00:44:02.260 Cindy Williamson: Okay, so what's next? We are excited. Oh, well, I am. I didn't ask Nick. If he was excited or not 257 00:44:02.720 --> 00:44:28.620 Cindy Williamson: be, we're going to keep collecting data. Of course we're going to update our dashboards. We will make them live. But as part of that process. We've also talked about a plan for informing campus about the dashboards, so not just putting them out there and saying, Hey, they're here. But you know, holding potentially short information sessions and 258 00:44:29.080 --> 00:44:41.549 Cindy Williamson: talking about how the dashboards can be used, what the you know, specific context around the data. Nick, you want to go on to the next one, 259 00:44:42.270 --> 00:44:50.289 Cindy Williamson: and then we'll continue to collaborate. I've already got some ideas for additional dashboards. Of course we gotta get these out and 260 00:44:50.560 --> 00:44:55.740 Cindy Williamson: get people using him first, st but I I don't think there will be anybody who is. 261 00:44:55.770 --> 00:45:17.009 Cindy Williamson: It's not happy with with what we've come up with. But yeah, making sure that faculty staff students, administrators, everyone understands what they're looking at when they go to these dashboards and we we do already have that dashboards. But at Utc as does system and across the system. But 262 00:45:17.382 --> 00:45:26.317 Cindy Williamson: I think these are different than than what has been expected in the past in the past, and we want to be sure and 263 00:45:27.160 --> 00:45:31.929 Cindy Williamson: have a balance between what we feel like is needed, and then what's wanted? 264 00:45:32.120 --> 00:45:34.710 Cindy Williamson: I think this will be a continuous process. 265 00:45:34.970 --> 00:45:41.938 Cindy Williamson: Once we close the loop it opens right back up. And you know, we just keep going with this. But 266 00:45:43.130 --> 00:46:00.370 Cindy Williamson: initially, I think additional dashboards that we'll think about developing our faculty rating of administrators, employment and placement data super important right now. And we actually finally, Ashley, I'm looking at you finally have a process in place. That 267 00:46:00.490 --> 00:46:01.370 Cindy Williamson: is 268 00:46:02.010 --> 00:46:13.379 Cindy Williamson: most efficient. We we have more and better data, regarding employment and placement than we've ever had. So then, also, as part of that you know, quality assurance funding 269 00:46:13.840 --> 00:46:25.000 Cindy Williamson: several of the things that we've talked about are part of quality assurance, funding, and I think being able to display that so that others can see. That's another one of those things that 270 00:46:25.130 --> 00:46:30.979 Cindy Williamson: just like I've reached about outcomes assessment. These things can't be done in isolation. You know. 271 00:46:31.490 --> 00:46:37.799 Cindy Williamson: campus needs to know about quality assurance funding. Yes, it's part of performance funding, and we know, you know. 272 00:46:38.510 --> 00:46:53.358 Cindy Williamson: money is tied to that right. But even even past that I'm I'm thinking about how different programs score on their program reviews or, you know what our senior exit exam data looks like? 273 00:46:54.460 --> 00:47:14.090 Cindy Williamson: it's just so much institutional satisfaction. I'm not going to go on and on. But there's lots of opportunity there, I think. And this has been a great process so far. And I hope that Nick will keep going with me on this. But I I think we've had a a 274 00:47:14.510 --> 00:47:16.160 Cindy Williamson: fabulous collaboration. 275 00:47:17.060 --> 00:47:17.910 Nick Humensky: I agree. 276 00:47:22.110 --> 00:47:26.999 Cindy Williamson: I think I saw a couple of questions like in the chat, but I haven't gone back in there. 277 00:47:28.390 --> 00:47:44.986 Rachel Borashko: Yeah, thank you so much, Cindy and Nick, that was a great presentation. I really appreciated that. I'll invite anybody to also come off mute. If anybody has any questions that they would like to share with the presenters. We do have a couple in the chat. At least one to everyone, and then a couple sent just to me. 278 00:47:46.130 --> 00:48:02.880 Rachel Borashko: Feel free. If you have any questions also to drop them into the chat. Now you can send them to everybody or to me. I can keep your if you're embarrassed about your question. If you think it's a dumb question, it's not a dumb question, and if you really want to send it just to me, I can keep your identity confidential. 279 00:48:03.230 --> 00:48:06.899 Rachel Borashko: Does anyone want to come off mute to ask any questions. 280 00:48:07.180 --> 00:48:32.890 Ashley Ludewig: I have one. So I used to be faculty at another institution. And so I've seen the faculty side of this data many many times, and getting these isolated reports of my students reviews of the course that are just one at a time. They're not put in any conversation with each other unless you choose to do it. So even on a 1 to one basis like, it's difficult to make use of that data. And so when I look at the scale of this. 281 00:48:32.930 --> 00:48:51.090 Ashley Ludewig: it's just mind blowing, and so awesome. And I'm wondering I know they're not live yet. But have you gotten to do some testing or like Beta, work with, like the faculty and administrators, at least a handful of them who will be using this? And and what do they think? Or do they see the same potential that that you and Nick see in this. 282 00:48:51.887 --> 00:49:03.429 Cindy Williamson: That is coming. We have previewed these dashboards to Provost Hale and so he is certainly on board with this. And yeah, I think 283 00:49:03.540 --> 00:49:13.070 Cindy Williamson: we've gone back and forth with some changes we want to make. And so once we get to a point where we're comfortable. You know, with what we have. 284 00:49:13.690 --> 00:49:26.610 Cindy Williamson: That's an excellent point. Yes, share with faculty department heads, Deans. They're all looking for something different from from each of these dashboards, right? So making sure that it's there and they can access it. 285 00:49:27.450 --> 00:49:29.150 Cindy Williamson: Yeah, that's super important.