Improving Data Quality and Governance

min read
EDUCAUSE Shop Talk | Season 1, Episode 8

Data governance is foundational to institutional decision-making, digital transformation, and more. In this episode, hosts Sophie White and Nicole Muscanell talk with George Firican from the University of British Columbia and Melissa Barnett from Georgia State University about strategies for building a data-oriented culture in the higher education context.

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Sophie White: Hello everyone and welcome to our EDUCAUSE Shop talk on improving data quality and governance. I am Sophie White. I use she/her pronouns, and I am the Showcase Program Manager at EDUCAUSE. I'll be one of the hosts for today's show.

Nicole Muscanell: And my name is Nicole Muscanell. I am a researcher on the research and insights team here at EDUCAUSE, and I will be co-hosting with Sophie.

Sophie White: Great. So, we are thrilled today to have two fantastic guests with us to talk about data quality and governance. So, the first guest is George Firican. George is the Director of Data Governance and Business Intelligence at the University of British Columbia. Additionally, he is the founder of LightsOnData, which is an organization founded to share educational resources about data, including online courses, templates and guides. He also hosts his own show, which is the LightsOnData show.  We're really excited to have him here today.

George Firican: Thanks for having me.

Sophie White: And our other guest is Melissa Barnett. Melissa is a very involved member of our EDUCAUSE community. She is the Data Governance Manager at Georgia State University. And in her role, she supports university leaders, data analysts and data users and making decisions that affect the university and the next generation by ensuring our data are known, accessible, trustworthy, and secure, as well as facilitating conversations across the university and training users on how to handle data.

Melissa is also a leader of the EDUCAUSE Data Governance Community Group and an instructor in the EDUCAUSE Data Literacy Institute.Thanks, Melissa for all you do and for being here today.

Melissa Barnett: Thanks for having me.

Sophie White: Great. I'd love to kick us off. Just I think that this this topic we are reviewing at EDUCAUSE as part of our Showcase series, and it really came up because as we're looking at all these really important ways to look at data as a way to orient institutions on how to make decisions that the foundations, the fundamentals of data are so important and are sometimes overlooked as we're looking at kind of all of these other ways to use it. So, I'm curious, why do you all think that we need to sometimes return to the basics of foundations around data, and where do you think universities really need to start there?

Melissa Barnett: I can go ahead and start and say that the foundation is a great way to, to describe it. I typically describe it as like the foundation for a house. It's not something that you think about all the time, but you absolutely need it. And I think that people . . . I've also said that analytics are sexier. They're more intriguing, but it's really that foundation underneath of it, that going back to those basics, that really helps, that the trust in the analytics and what goes into all of that. So, I like to go back to the basics. I also am a methods person, research methods, so I think it's very important to go back to the basics.

Sophie White: Yeah. I love that as a foundation for building a house. I think we can talk more about your article that just released, but I think I saw an analogy used that if you're building your dream house, you wouldn't just say, okay, go build it. You have to start with a plan and figure out the foundation before you can do that.

Melissa Barnett: Exactly.

George Firican: Yeah, I second that as well. And the real challenge really isn't just having the data is transforming it into actionable insights. And of course, that also requires that cultural shift. And to your point about the vision, I mean, a vision without a plan is just a dream. So, yeah, we do need to have that strong foundation layer to create a plan on it.

Melissa Barnett: Absolutely.

Sophie White: Yeah, and, George, I know you talked to a lot of folks about data and your role, both at the university and through your show. So, I'm curious, do you feel like people are taking this idea of building a foundation seriously at this point, or do you feel like that's still a work in progress?

George Firican: Very good question. And I've been working in this field for a lot of years. And, I'm always hopeful that, hey, it's this thing is picking up and for the better, I think it is picking up. But it's a lot of times it's empty words coming from leadership, you know, there's a new trend coming up, and data is the foundation of whatever that trend is, such as GenAI at the moment. And, in a way, it's good that it's getting that momentum and it's getting more awareness to the leadership. But oftentimes it's not transformed into a lot of action. So, we're having these types of conversations. We've been having them for years now.

The good thing, though, I think data governance is coming more into focus now with AI governance also being an important aspect of what organizations need to focus on, and because of that, some more attention and resources are being dedicated to it. So, I think things are improving. But overall, across different industries we're seeing similar challenges with this as well. And that kind of includes having the data silos, lack of ownership, lack of a data literacy program and overall resistance to change. And I think that's the biggest impediment when something needs to be done with data.

Melissa Barnett: I would say too, that, I agree with George that that there's been, I would say there's definitely momentum there. Prior to the pandemic, a lot of, one of the groups I was working with, which was, organic, roots, sort of looking for, like, it was a foundational group that just got together on its own. And they were, they were it was hedge. It was the Higher Education Data Governance group. And we were talking about how a lot of schools at that time were stopping or putting pause on their data governance initiatives during the pandemic. Georgia State was actually because of it was being mandated by the system actually pushed through the pandemic with that. But a lot of schools didn't. And, people had started to say at the time, in the, in the early part of the pandemic, will this actually be at the end of data governance, but more of it's withering.

And I think that actually now we're starting to see especially with AI and other aspects coming in that, governance, the word people don't understand really. It's an abstract concept. So, I think things like the data quality and things like AI and what can generate—can we actually put our trust in it? Well, those go back to those foundational items that start with data governance. So maybe we need a word change for data governance, which some people have suggested, I think. But, people, they don't want their data or their outcomes, the output that they're looking at to be garbage or based on garbage. Right? Or not high quality data, I should say.

George Firican: I've also heard, by the way, synonyms for data governance that other institutions were using because like you mentioned, Melissa, they have an aversion against a governance piece. And I've heard data enablement, data excellence as an alternative, word for it.

Melissa Barnett: Exactly.

Sophie White: Good to know if it's meeting the same objective, I guess label it different ways.

George Firican: Yeah.

Sophie White: Nicole, I know you just did a QuickPoll on generative AI, and have been doing some of our research work at EDUCAUSE about AI. Is there anything related to data governance that stuck out to you?

Nicole Muscanell: Not I would say recently. I'm trying to think. I think when it comes specifically to AI, it seems that a lot of institutions, or at least the ones who have responded to our surveys, are still kind of early in the process. So when we ask a lot of questions about strategy and, where they are at with their governance, it's still like leadership's starting to drive those conversations. Maybe they don't really have engagement from all stakeholders at this point. And they're just trying to figure out what policies and guidelines do we need to put in place, which I think is challenging because of how rapidly things are changing. So, I would say my impression is that it's still early on for a lot of people, and they're just trying to figure out, like, what do we even actually do to get this going and to keep it sustainable?

George Firican: You know, I have to say that I'm honestly a little bit frustrated about the pace that where a lot of us are at, and as you're mentioning, it's something that's changing so rapidly, and we're so slow at catching up and investing resources and attention to data governance to support all these AI initiatives and projects, and deliverables. And to me, data governance is really the conscience of ethical data and AI practices. You know, it is because it provides those principles, policies, standards, roles and responsibilities, processes to ensure that data is used responsibly. So of course, this means not only complying with the laws and regulations, but also upholding the values of, transparency, of accountability and fairness in all of these data and AI initiatives.

I think it's so important, that yes, I am sometimes frustrated at the pace that we're adopting this.

Melissa Barnett: Well, we recently put out guidelines specifically on the, if you will, staff side of things. So, everything from meetings online to how that can be utilized and, and what those transcripts might look like, etc., and what we learned in preparing to write that was that many schools were actually generating guidelines as opposed to policies because of precisely what has been said here, that things are moving so fast and that there are so many people that, and things that are changing that they don't want to put anything too much in stone. And we also have found that it depends on whether it's in the classroom and from an instructional side and from a pedagogical side and the learning aspect versus what it actually means from an interaction with work that goes on, if you will, behind the scenes and even data that might be, recorded or something where people are sharing that sensitive information and how to handle that. I haven't seen, though, any, to my recollection I haven't seen any schools that actually have, policies on it, but more so guidelines.

Sophie White: Yeah. I don't know about the policies. I'd have to double check with our EDUCAUSE folks, but, yeah, that's a great point. I think we're seeing that with AI too, that folks are really hesitant to create a set-in-stone policy when it's changing daily. So, it's a really interesting challenge for higher ed to address.

Melissa Barnett: Absolutely.

Nicole Muscanell: And it'll be interesting to see when we start seeing more AI regulations come out, and that's going to have to dictate as well what those policies are, and who knows how often those are going to change as well.

Melissa Barnett: Yeah. And I don't know if it's a new group, but one of the community groups to have an AI group, it's new to me. In the past couple months. They have been fantastic at resources and information across the board, whether it be on the staff side, faculty, or student side. And so I'm not sure if you know about that, but that's new to me, and I think they're a wonderful, great, wonderful, community group that they have there.

Sophie White: Yeah, the AI Community Group is really great. I agree, it's very active. I had my, I think the daily digest turned on first, which is when you only get one email a day. And then I was realizing I was missing things because I didn't get enough. So, I turned it on to real-time posts. But, yeah, it's a really great resource.

Melissa Barnett: Yeah.

Sophie White: So, Melissa, I mentioned this before, but I would love to hear a bit more from you and this Showcase that we're, launching at EDUCAUSE, Improving Data Quality and Governance, There's a resource in there that is called "You Can't Have Digital Transformation Without Data Governance." And I know you were one of the authors on that resource. Do you mind just talking a bit about why you all decided to write that piece, or what you think some important takeaways are?

Melissa Barnett: Yeah, that's so you're right. I'm one of the authors, there are I think there are five of us total. All of us are part of the Data Governance group, the steering committee that you mentioned. And this was the title of our presentation back in October that we gave, and we had a wonderful turnout. And we structured differently than the actual purpose of the article. Excuse me, the presentation is slightly different than the article. And so, we're basically making the case for why it is utilizing many of the things that EDUCAUSE has put out about digital transformation and the various elements of it. So, we are linking the fact that for digital transformation, you have data and analytics, and in order to have data analytics, you, of course, have to have a robust data governance program.

And then we go into also, showcasing how one might start. So, everything from pulling together a structure, getting the team together, and starting to go down that path. Because we, we get a lot of people in the Community Groups who are either early on in their journey or just a little bit further down the road, and so we find that people really can use a roadmap or an understanding and linkage between all the various components, and that's where that analogy came from, about the house, building a house because we were also talking about how it's not only important to have a foundation, but your house isn't going to look like your neighbor's house, which is goes to culture of the various institutions. And that data governance at Georgia State is not going to look the same, there say at UBC or other institutions across higher ed. So that's what that article is getting at. And I'm a strong proponent of digital transformation. But in those articles, we saw that governance, we wanted to take a more prominent role and let people know about it a little bit more.

Sophie White: Yeah, thanks for sharing and for writing that. I was reviewing it earlier today, and it's a really helpful resource. So, appreciate all that you do.

George Firican: To just to add a little bit to that. I don't know if you remember that quote from Clive Humby that sort of started this revolution, and he was mentioning "Data is the new oil." And I know it's a very controversial saying because, yes, there are a lot of comparisons, but there are also a lot of ways where data is not like oil. But really, the point of the message is that data is valuable. That was kind of what he was trying to say. So, regardless if it's a good comparison or not. And in order to get that value out of data, we need to have it defined and understood. We need to have it secure, accessible, consistent, and of course, clean. That's where the data quality piece comes in. So to do all of these and to achieve all of this so that we derive that good value out of it, we need to have policies in place, processes, standards, roles, and responsibilities. And that's what really data governance does and establishes. So, it's a really intertwined relationship between data governance and data quality. To me you can't have one without the other. You can't achieve great data quality without investing into the data governance, even though some organizations are saying, you know what, we do have data quality without investing in data governance. And in some aspects, they're doing data governance without knowing that they're doing data governance, without having it recognized as such. And in some aspects, if you focus on data governance, data quality is sort of the byproduct. It's that side effect. You end up with better quality data because of it. So it's sort of these, two sides of the same coin that I see.

Sophie White: That can be the structure above the foundation is higher quality after the data governance is built.

Melissa Barnett: It's a point that a lot of institutions start down that road for data governance. They know that the quality of their data isn't as strong as they'd like it to be, and so they venture into governance. Another one is there's a new data platform coming online, and so they say, okay, now we're going to venture into governance. And one of the things that I'm sure the audience knows as well as all of us here is that there's an emphasis on the fact that data governance is a collaboration between business and IT, it's not solely from the information technology side, but that collaboration is absolutely needed. And I find that working together is a must. And being that in some ways translator for both sides, my background is more on the analytics and governance side, and so I've been getting more and more into the technical side so that I can understand where they're coming from as well. And that's when you hear things like, don't start with a tool, or that's not going to solve all of your problems or new challenges or your quality issues that you have. It has a lot to do with change management, getting people on board and so forth. So that's something that I think you hear a lot of. I hear a lot of statements that are repeated over time. Sometimes it helps to flesh them out a little bit more for people to really get a better understanding of what that means. And of course, data governance can be on the business side within the organization or on the IT side. It depends on the organization, how they structure things.

Sophie White: Yeah, we have a new video from EDUCAUSE Review included in this Showcase too that's about "Fostering a Data-Oriented Culture," and it highlights UC Irvine is doing this Compass initiative to bring together different data points to inform the institution. But a lot of what they talked about in that video is how to build that culture and how to be that in between that bridge. I don't know if you all have other tips for how to do that, because it's one of those things. I think that's easier said than done.

Nicole Muscanell: I yeah, okay. Go ahead.

George Firican: No, no. Please.

Nicole Muscanell: Sorry. I when I was looking over all of the materials for this, I was just wondering, like, how much of a role is it, how much of a factor is it the literacy component? Obviously, that's probably a really big factor, but once you do give the stakeholders like an understanding of data and how to use it effectively, is that really the major thing that's going to get them to value it more, or are there other factors at play that are making people hesitant to buy into a data-driven culture. Is it just literacy, or is that something beyond that? And I'm asking this because I'm curious because I used to be a professor. And Melissa, like you, I'm methodology. I have a methodology background, and I would teach research methods and statistics, and I just had a hard time with, like a lot of students, they thought they were signing up to take psychology and not learn about data. And I was like, well, you're not gonna be able to learn about psychology unless you actually have some understanding of research methods and data. But once we would cover those topics, even though they had developed an understanding of how those things work, I found that a good number of students still were like, even though I now understand this, I don't necessarily want to be actively involved with data. And so I'm wondering, like, to what extent that's kind of going on with, with stakeholders at institutions?

Melissa Barnett: Well, I definitely have thoughts, but I want to let George go first because I don't want to monopolize conversation. I definitely have thoughts on this, yes.

George Firican: Sure. Thank you for passing on the mic. I have to say that I think data literacy is really one of the pillars that need to be implemented. It's not going to solve all the problems. And I think what it helps with is maybe to remove the fear out of certain people on when did you hear about anything that has to do with data? They don't shut off or stop caring, and even you don't work with data, a lot of us would still consume it. So, I think even from that point of view, it's good to have, at least a broad understanding and again, removing that fear factor so that if you do see something that's not right, you have a process or you have the responsibility to let somebody know and look into it, or at least have that part of that responsibility.

To me with data governance overall as well, my philosophy really centers on three pillars, and that's transparency, empowerment, and collaboration. And I think success in data governance, too, requires that clear communication, so in the change management piece. Second, empowering those individuals with knowledge, and that's where data literacy comes in. And , of course, also tools to manage that data effectively. And third, to foster that collaboration across departments and individuals to ensure that data is treated as an asset that drives value.

Melissa Barnett: Literacy is undoubtedly important, and I can tell you a little bit about what we're doing at Georgia State. I just had a meeting earlier this week about literacy, but I would take a step even prior to that, and I find that getting people to be, I don't want to say motivated, but incentivized, is to show them how data governance can address, or I should say aspects of governance can address their challenges. And so I find myself in a lot of conversations where, I'll say, well, we can assist you, or I'll hear about conversations and the word data governance or the phrase data governance doesn't come if it's like, that's precisely what we're talking about. So being in a, in the university system where it was mandated and we had various lists and so forth that we needed to check off in order to comply, those it not always align, the checkbox, if you will, or the task didn't always align with the people I was working with—what they needed in the moment. So, along the way, we got through the checklist, and we worked through that, and then, starting to shape things so that people can better understand how it can help them. I find that was a lot of the conversations I have with people, throughout EDUCAUSE and higher ed is that once you tap into something that is a hurdle or challenge for somebody, which might be data literacy, is to help them, through that, show them what the various aspects can do.

On the literacy side, I find that, and I've done this in multiple ways, not only with the EDUCAUSE, Institute, also in my prior institution, I started and oversaw a workshop series for staff when it came to data literacy because the group I was with, we did a lot of evaluations for leadership, and so they needed to make decisions, but they needed a different, if you will, level and aspects of literacy as opposed to, say, people who were, being asked to, I don't know, survey students or collect data, or something of that nature, whatever their requirements were. So, what we're doing right now is we have a cross unit, across collaboration, across units, creating resources for the larger community. We're currently building it out, and we've got everyone from data stewards, on the student side, financial aid side, etc., all the way to people are doing visualizations to IT, library, etc., so that we can address what the community needs from a literacy standpoint and start with a foundation, or foundational level, and then filter in those more intermediate and advanced components because we did it even with the Literacy Institute, trying to target the needs of the entire, all attendees is difficult because everybody needs it in a different component, and also they come to the topic with different backgrounds and understanding. And, you know, we work in higher ed, and no one wants to be seen as not being data literate. Everyone has some functionality with it and so forth. But it also, there can be some fear, as George said, with it or be a little anxious. People want to learn, but we need to reach out to them where they are.

Sophie White: Yeah. I love this idea. I think you said, George removing the fear factor. And I think that's a theme that we've seen in a lot of these conversations just around culture and technology and higher ed. We did one of these Shop Talks on cybersecurity earlier this year, and there was a lot just that had to do with making sure that users at the institution are comfortable reaching out if they think there was a security incident or if they think that there's something at stake instead of just hiding it, pretending they're comfortable, whatever it is, because that obviously causes more problems down the road. And I think it's the same with data literacy.

George Firican: Absolutely. And Melissa, I love what you're saying even before that. You need to address the why and explain data governance from the point of view of the individual or the department that you're having the conversation with. And yeah, I second that as well. You know, we always need to, I call it the, tune into the WHY-I-I-FM radio station. The what's in it for me? Everybody is asking about that, at least in their own head, like why do I need to listen to this person? And so it's always from an individual perspective or department or perspective or even organization-wide perspective, and you always need to tie back to those goals and questions and needs.

Melissa Barnett: And I'll also say there that repetition is needed, whether it be from governance or literacy or both. But also there is a mindset where, at least in my opinion, is that governance needs to also be an educator. That repetition and breaking it down into ways that people can utilize and explain it to them. You know, I've reached out to many of my, people I've worked as a pedagogue, colleagues, to understand better ways in which to get people engaged in it, and repetition is one of them, and trying different ways, and educating people at all levels is something that I really do find to be, first of all, enjoyable, but also something that's very important, and I think that that also builds trust with people, knowing that somebody is going to work with them, I think is, is also very helpful for people.

Sophie White: So, the mention of all levels made me think of just leadership in higher ed. And I wanted to back George to something you mentioned about being frustrated about the pace of higher ed and data governance. Why do you think that higher ed may be slower to adopt these data governance strategies than other industries that you've seen? And are there quick fixes that you think higher ed could make in order to address them, or are they more long term and embedded?

George Firican: Sure, sure. Well, first of all, I don't think we're alone within this industry. So I think other industries are facing very similar challenges. And there actually are a lot of commonalities across the board. And what differs is maybe the size and complexity of the institution, of the organization, company as well as their overall maturity level in various areas—being data management, data strategy overall, or data governance in particular. But what I think really differs for higher ed institutions, really in particular, there are a few things. One, a lot of them tend to have this decentralized structure. So, they often have autonomous departments and faculties, each having its own data practices and priorities. That's definitely causing some issues. I think for such instances, developing a federated data governance model that kind of allows departments autonomy while maintaining that overall governance standards is something that I would always recommend.

Something else that's unique to higher ed is that academic freedom. You know, faculty and researchers very highly value academic freedom, and rightfully so, but can sometimes lead to resistance against some standardized data practices and governance overall. So, it can make for some longer, more difficult conversations and more and more people that do need to be involved and be at that decision. table. So yeah, for this one, I don't know what the recommendation would be. Probably that early engagement is always helpful, and kind of highlighting what Melissa mentioned, how data governance can support and enhance the research and academic endeavors and things like that.

Something else, of course, that may be not as unique to higher ed, but as those resource constraints. You know, we often face budget constraints and especially because of Covid and where we're still seeing the effects of that. We have limited resources for data initiatives in general. So, I think all of us will need to kind of advocate for the long-term value and the ROI of data governance initiatives for that, and try and demonstrate, as much as possible, how it can lead to cost savings and improved efficiency, better decision making, and all that stuff.

And the last two that I can think of is cultural change. A lot of individuals working in higher ed, I think they have been there for a while in their particular role or previous roles. But it may be because of that it is harder to drive that change, whatever that might entail. And the second one is that you have so many different stakeholders in higher ed with so many different needs—anywhere from students to faculty to the admin staff as well as external partners. And all these have again, very diverse and sometimes conflicting needs. And that's a little bit harder to address for sure. So yeah to try and understand what all of these are early on, and incorporating that feedback into a data governance framework definitely helps. But it's not an easy road either way.

Melissa Barnett: Yeah, and to George's point about the decentralization or the silo-ization of higher ed, I think that while institutions are under the same, if you will, name, they don't necessarily function that way. So, I think for higher ed people, or I should rephrase for people coming from the outside of higher ed into higher ed, which there have been several, especially on the governance side or the data management side. And in higher ed is, like any other industry, unique if you haven't been there before and people get very confused about it because there's this overall institutional name. But the functioning of the institution can be very autonomous, where say colleges or schools function independently or various units do. And I think we can forget that sometimes, how that works, even if we look at another institution, say, oh, they must have all of this functioning. But the fact of the matter is not until you're on the inside can you really know how things function.

And the other factor, I think, is governance is kind of like going to get your annual, physical, right? It's a preventative measure. And with the restrictions and resources that George is talking about, as well as other aspects like the enrollment cliff and other things that are impacting higher ed right now, there's so many things to react to, if you will, for that. The other side of that, of preventative versus reaction, is that there's only so much time in a day for people. And so to have a whole group or team looking at something that is preventative, people are thinking about was on their to do list, and they really do have good intentions to be preventative, but it's not something always that's top of mind, and so I think that's where, for institutions that don't have someone who's dedicated to governance, I encourage them to look into that because it really is a full-time job—communicating, bringing people together, giving them also the guidelines and so forth. Otherwise, it becomes something that's part of someone's job and other things crop up. So, I think there's a variety of reasons, and I would not want to be in the situation where, say, a breach occurs and then governance is absolutely needed, right? And you have to go through that process because that's not an ideal situation to be in either. And so it's a fine line to walk, most definitely.

George Firican: Very good point. Something else I wanted to add that I just jumped in my head is that some of our institutions, they have quite a prestige, the tradition that comes with it, which is not a bad thing, but some of those traditions sort of, make their way into how we manage our data or not manage our data. And so we still kind of do the same things again and again. And I think the biggest enemy of progress is that status quo. And so we must be willing to disrupt some traditional approaches that we've had. So I wonder if that's also part of that impediment or roadblock that we're seeing in higher ed.

Sophie White: Do you have an example of how that prestige could affect data governance, just to be clear for the audience?

George Firican: Yeah, and maybe that was not the right choice of words, but more along, this is how we've always done things, and it used to work, and it's hard to change that mentality. Maybe there's new factors that we need to take under consideration, new requirements, new tools, new standards that we need to abide to. And, of course, regulations and whatnot. And that also requires a new way of doing things, of being more efficient on how we do some of these things, and we can't, even though maybe the methods of teaching have evolved as well, but maybe not as much, the way that we manage the data probably has changed at a higher pace.

Melissa Barnett: The other way it can also influence governance, and this varies institution by institution, and that is, I've heard some people say in higher ed, if somebody in business came in and said, well you just tell them that it's the bottom line and we have to do this, or regulation is there, and we've got to do this. Whereas in higher ed, because of that, which is beautiful about higher ed is we're more autonomous. We're entrepreneurial. We are encouraged to go out and learn new things and not always for the status quo or anything of that nature. We're encouraged to push the boundaries is that I think in some respects, people who have been in various institutions for a while may hold on to their data or want to not share it as easily, whereas somebody in business would say, well, we have to get in line. And because we want to honor other people and their decisions, I think sometimes, a lot of people find that difficult. And that, again, varies institution to institution. That isn't the way everywhere, but it can be where people will say, well, so-and-so's not going to share their data. And it's like, why won't they? Well, there's a history there, and so you get to know that story and journey once you get inside the institution.

George Firican: Well said. But I also want to mention that one thing that I think that's unique to higher ed, and one of the reasons why I love working in higher ed, is the collaborative aspect. And I know, maybe it differs from institution to institution, but within the industry, I do feel that we tend to collaborate a lot more often, share ideas, opinions, even, here's how I'm doing this, here's an example of my policy that I can share with you. So you don't have to start from ground zero. Here are challenges that I've gone through, and here's what I've done, so maybe you can use my experience, so you don't have to go through the same thing. And we don't see something like that in the for-profit work. And, yeah, so even, the things that EDUCAUSE is doing, it's really commendable and very helpful to everybody that's accessing your resources and what you're putting together. So I think that's sort of unique and definitely an advantage that higher education brings.

Nicole Muscanell: I'm just curious, in your experience, the both of you, is the hesitance to share data with others. What's driving that? I know from a research side of things, it's kind of like I don't want to get scooped, so I'm not sharing my data with anybody. But at a broader level, obviously, like something I know we're not just talking about researchers and publishing papers. So, what do you think is driving that?

Melissa Barnett: I would say that, I think this actually goes back to, and I'll explain what I mean by this, I think it goes back to the literacy component. So, what's quite frequently heard, a comment in meetings or to groups on campuses, "That data is wrong." And a lot of that has to do with context. So and so sliced the data this way and it's being presented. But people who are listening to it might not know that or they don't realize it, and then it doesn't match with how they utilize data in their area. And so I think that a lot of . . . the first example I'd give is that people don't want the data that they oversee to be misinterpreted, so that that context is needed. And that's why I say the literacy component. And the other is, of course, to the point George already said, is that resources, right? They don't want those comparisons being made because then something might happen to the areas that they're in, and the context is not there. Say it's a very small unit versus a very large unit. And people will take . . . people quite often will say to me, well, they're not comparing apples to apples. So, I think that there's a push, definitely among chief data officers and data governance, to put into the vision statements and missions policies that the data from an institution is the university's asset. But people still tend to treat it, like humans would, right? And you want to give that full story and that background, but we're processing so much information on a given day, we quite frequently fail to think about, oh, there might be a larger context here, and that's a very common saying. I'm sure you have experienced it too George, where they'll say those numbers are wrong. And it's like, well, they're not wrong, and let me give you some context. So, from my perspective, that might be one of the reasons why people are a little more, some people not all, are a little more close to the vest.

Yeah, that's a very good point. You know there are organizations that are facilitating some of these data shares. There's even a higher education data sharing consortium and other that are for-profit organizations are trying to, kind of be the bridge between higher ed organizations to make sure that the context is also present between them, and you're actually comparing apples to apples. But, yeah, it's hard for all the points that Melissa brought up, it's hard to just put data out there and avoid risks.

Melissa Barnett: Yeah, exactly. Or perceived risks, right?

George Firican: Mmm-hmm.

Melissa Barnett: Yeah, absolutely.

Sophie White: Yeah, and Melissa, I think I saw. . . Are you the first data governance manager at Georgia State?

Melissa Barnett: Yeah, I am yeah.

Sophie White: OK. I was just thinking that from a cultural perspective that takes some scaffolding for folks to understand. This is why I'm here. This is how I'm supposed to support you. I can provide context to this data if you'll let me.

Melissa Barnett: Well, and they did a wonderful job at Georgia State, and they continue actually. The original rollout team that they had after the system said all twenty-six institutions need to institute data governance. They had a rollout team with a variety of units represented, and they really did a lot of that foundational work. And then part of that, of course, was making the argument why a position dedicated to this should, in fact, be carved out. And so, starting that message was definitely something that they put a lot of work into. And they continue today with a few additional people to that original group. We continue to share information, update each other, etc., but yes, getting that message out and having a communication plan and various, if you will, communication outlets is something that I've continued to grow, because, we have a large institution, right? How do you get the message out? It's not always easy.

Sophie White: Great. Well, I think we're getting to the end of our time here. This conversation has flown by, so I don't know how the whole hour is going by, but I would love to just wrap up and see if both of you have just a last thought. If you wanted to leave our audience with one tip on how to address data governance and higher ed that you can start doing right now, what do you think that would be?

George Firican: Wow. One tip. One tip. So just want to preface the tip that I think the future of data in higher ed is not really just about bigger data sets and smarter algorithms and tools. It's really about forging a future where data empowers humanity, respects our privacy, amplifies our potential. Everybody needs to get behind that message and understand that importance of data. That data is valuable—tying it back to what Melissa was mentioning, the what's in it for me? And, yeah, communicate as often as possible.

Sophie White: I love that. Data empowers humanity. That is a great message to take away.

Melissa Barnett: It is actually. You should have a hashtag. I would say, we say this in the article as well, the one that you referenced earlier, Sophie, and that is to start. Now that looks different at different places, but I would say just start those conversations, start building those relationships, which is another theme we hear quite frequently. Begin to see what might work, begin to forge those relationships and who might be on board, and also what might already be done. There are a lot of good reports and other things which actually, in the article that Jason Simon that I had come out that's about culture, we were saying utilize those, if you will, artifacts to begin and launch your journey. There are, like I mentioned earlier, a lot of people who are tasked, not solely but part of their position, in incorporating data governance. I've had a few reach out to me. How do they get started? And it is starting with what you have and sometimes going back to one or two people who are pro data governance, are really interested in it. That's a good way to start, but also looking to see what's already been done and starting to understand that so that you know the context in which you're proposing data governance going forward.

Sophie White: Yeah, I love that. Makes me think of that quote about. . . We're quoting quotes everywhere, but "A journey of a thousand miles starts with a single step." So, you just have to get that momentum going. One of our resources in this Showcase is also a Horizon Action Plan on unified data models. And they actually look at ten years down the line, what does that look like in an ideal world in higher ed. And some of the tips are, this is what will we'll work toward with intentionality, but let's just get started. So, I think that's a really big way.

Melissa Barnett: And that's a perfect thing, Sophie, that's a great resource as is the Data Governance group. I had somebody reach out to me yesterday or today, and I said, check our library. We have a lot of stuff that's been covered. But also don't hesitate to reach out to the Data Governance group or others in the community like myself or George. We’re going to offer George as being reached there. Hopefully he doesn't mind because I think that, like he said, we are very willing as an overall industry to collaborate with people. So, people aren't alone in this. And while some schools and institutions are further along, the vast majority are either (a) getting started or (b) starting to, they're slowly starting to walk. And there are the others who are further down the pathway. So I would utilize the resources that people have.

Sophie White: Yeah. The really great message. There's a lot out there. EDUCAUSE is here to help you. The community is here to help. So, please use the resources we have. And speaking of hopefully folks learned a lot from this discussion today. I know that I did, and I really enjoyed talking to all of you. George, Melissa, Nicole, thank you so much for spending the time with us.

You can also watch the episode on YouTube

This episode features:

Melissa Barnett
Data Governance Manager
Georgia State University

George Firican
Director, Data Governance & Business Intelligence
University of British Columbia

Nicole Muscanell

Sophie White
Showcase Program Manager