Jan 21 '21

Introducing Dr. Cheri Speier-Pero, Data Analytics Fellow

Data is an amazing thing. It tells us who we are and what we do. And while predicting the choices, preferences and behaviors of one person is nearly impossible, predicting this for many people is a lot easier through the power of data analysis. Once-hidden patterns are revealed, structure emerges, and our data becomes our vision into the future.

Holly Fearing

(00:13): Data is an amazing thing. It tells us who we are and what we do. And while predicting the choices, preferences, and behaviors of one person is nearly impossible. Predicting this for many people is a lot easier to do through the power of data analysis. Once hidden patterns are revealed, structure emerges, and our data becomes our literal vision into the future. Hello everyone. And welcome to the Filene Fill-In. I'm Holly Fearing with Filene. The Filene Fill-In is the podcast where we fill you in on what's been going on wherever Filene is and around the financial services world.

(00:53): So, if I told you that four out of every 10 members will have an interest in product X after they use product Y, would this be valuable information to have? While, generally interesting, it's much more than just a headline that gets BuzzFeed so many article clicks. We're able to use data analytics to make decisions in our strategies that will give us more of what we want and less of what we don't want.

(01:16): Think about the time and money saved alone in better targeted marketing campaigns. It's undeniable that successful data analytics can create immense value for your organization. This power though, like any other can be used for the good of many or exploited for the good of few. This is why there is both challenge and opportunity, especially for credit unions, to use their vast amounts of data, to grow and lift their mission, to fulfill their purpose of serving the needs of those otherwise left out of the financial services equation, and to take a step in front of the crowd and lead as an example of what ethical use of data looks like. In today's episode, you will hear what credit union leaders told us interests them about building cultures of an ecosystems for better data analytics in their organizations, and ultimately why this led Filene to dedicate a research Center of Excellence to this topic.

(02:16): You'll get the inside view on what this research will entail directly from the academic heart of the whole operation. AKA our research fellow for the Center, Dr. Cheri Speier-Pero, professor of information systems at Michigan State University. It is her mission to help leaders move ideas forward through the ethical implementation of new value creating technologies and systems. Take a listen. Now as Dr. Speier-Pero and Filene's, Paul Dionne enlighten us as to why no matter where your organization is on its data analytics journey, this research will appeal to, and is essential for, everyone on the path.

(03:00): Thank you for joining us. I am very excited to welcome Dr. Cheri Speier-Pero to the Filene family as our newest research fellow. Welcome Cheri.

Cheri Speier-Pero

(03:13): Thank you so much, Holly. I am very excited to be here.

Holly Fearing

(03:16):We are going to just dive in right away with all things about you and the Center that you're leading. So you, as our listeners may have seen in the news that you are the Filene Fellow for our Center for Data Analytics and the Future of Financial Services. Can you talk a little bit about what that means to be leading this focused research Center and talk a little bit about what this Center is even about?

Cheri Speier-Pero

(03:48): Isn't that the big question? So first of all, I am so excited. I am so honored to have been asked to serve in this role. And I am excited to be working with credit unions and partners in this space because I believe it is an opportunity to really make a difference for these organizations. I've been fortunate enough to have a history with credit unions, not just as a member, but as a board member. And so I feel like I have knowledge about the space and I certainly have a passion for analytics and see this as an opportunity to combine those two things in such a way that credit unions are able to benefit in ways that are appropriate for their strategic mission and their needs. So first of all, thank you to Filene, I am so honored and appreciative of this opportunity with respect to the Center.

(04:42): I think it's both a very needed capability within sort of this Filene research bucket, but it's also a very timely capability and research offering. And for those listening that say this analytics thing has been on our radar for, you know, eight years, 10 years, what do you mean it's timely? I think that's a reflection of the challenges that organizations across all industries have had with getting their arms around having a capability that is able to support the analysis of data in ways that contribute to actionable decisions that create value for the organization. And that value may be in the form of how members are better supported. It may be in the form of keeping valuable employees. There are so many different ways that organizations can use analytics to enhance the value, and value in this case is certainly not necessarily a financial value. It's a value in a very holistic way. So I see this as very timely. We know, there was a recent, Deloitte publication, I think it was in 2019, that talked about banking, both banks and credit unions have data. The having data is not the issue, but in so many cases, this data is not accessible, it's not clean, so we can't analyze it. And it's not, sort of distributed in ways that members within the organization can analyze that data in order to create these data-driven decisions. And so I think being able to spend some time understanding what capabilities and organization a credit union needs and then how to successfully build those capabilities. And ultimately then how does a credit union create an ecosystem, not just the credit union organization itself, but partners that can help support and enable and facilitate a much more robust set of analytics activities and really build the maturity of that activity within the organization.

(06:51): So even when I just said, I feel like that's a mouthful to even get your head around, but, it's truly, there's a lot of components to it. And I think that credit unions are at all, what would I say, sort of, if you think about of a maturity curve, some credit unions are at the beginning of this adventure and others are much further along the journey. And I really believe that the efforts and research that we pursue, will be able to speak to any credit union regardless where they fall in that space.

Holly Fearing

(07:23): Wow. There's so much in what you just said of different directions that credit unions can take this work and use the work and apply the work. So before we get into all of that a little bit more, I'm curious just about your background and how does somebody get into this type of research?

Cheri Speier-Pero

(07:46): Yeah, it's a great question. And it's one that I would say is probably an atypical, how did I get here? I think for a lot of academics we're trained for this very focused research passion, whatever it may be. And we pull those research interests into our teaching and whether it's undergraduate or graduate or executive teaching, we sort of inform the way we look at the world through that research lens. As I look at, at my passion for analytics, I actually think it was the reverse. So my background is information technology. I have a PhD in information systems and my work experience prior to getting the PhD was very much about applying information systems in organizations in ways that that created value. And so I have a very applied view of the world, I guess I would say.

(08:45): And so, as I have moved through my career at Michigan State, as analytics was beginning to get visibility in the practitioner community, Michigan State decided that we really needed to support this area. The demand for professionals who had these skills was just off the charts. And so we put together a Master's program that really was designed around our College of Business, our College of Engineering and our College of Natural Science at Michigan State because we felt we needed skills that were not just housed in one college. And that's one of the challenges that is true of an analytics professional. And I was asked to serve as the faculty director for this Master's program. And one of our real linchpins of the program was the importance of experiential learning. And not just because we felt experiential learning is always a very effective way for students to really develop expertise, but that the complexity of the analytics space and the depth of knowledge, any student or any professional needs in this area that requires such unique buckets of knowledge, that don't easily blend together. We needed to enable sort of laboratories for students to have a sandbox to develop these skills. And these aren't laboratories in the science sense, these are laboratories in the form of companies. And how do we get into companies and take on real life projects and work with organizations in order to design and implement analytics outcomes. And at the same time companies were saying, "we don't know what to do. We know this analytics thing is really important. We don't know where to start. We don't have capabilities. How do we do this?" And so not just the first year, but continuing today, so much of our Master's program is groups of students on live projects in organizations. And as I got into the, sort of, managing this and orchestrating partnerships with organizations, it just, for me naturally became a, well, what does this all mean? How do organizations gain success in this space? And how do we form partnerships so that a credit union is able to differentially increase its capability without making huge investments in people and having a really long learning curve? How do we accelerate the process so that a credit union or any other organization can see value more quickly? And so it's been fun for me because I think the role that I've had an opportunity to have at Michigan State has actually then resulted in a whole series of research activity that has come out of that. And that's been fun.

Holly Fearing

(11:49): That's so great. And you can hear the passion that you have for this, for helping people and businesses understand what data analytics is and the importance of the experiential learning and the complexities within it. I can tell you have a lot to give to this work in that way. And I've heard you talk about this briefly before that because of the complexity of the concept of data analytics, there's a lot misunderstood about it. So can you help us kind of just set the framing around, what is data analytics, what is not data analytics? And what is really most often misunderstood about this terminology or this area of study, and especially in a business context.

Cheri Speier-Pero

(12:45): A lot of people would say we do analytics, we have data, we analyze it. And my response is often, yes, we call that reporting and we've been doing reporting for decades and reporting is this very backwards looking, "what was our performance last quarter? How many new members did we get? How many new products did those members add to their basket of products and services that we provide them?" And reporting is really important, but it does not allow us to predict what our members and what our products and services and what our employees may do based on decisions we make. And to me, that's the core of analytics, the ability to create models, leveraging the breadth of data, the wealth of data we all have in order to predict, I'll say the future, but in order to predict what we think will happen in ways that enhance the members experience the member satisfaction with what we as a credit union do. And, so that's sort of the reporting analytics distinction. And at the other end, I think you can create a distinction from analytics to sort of artificial intelligence. I mean, when I think of the artificial intelligence, it is almost a packaged capability that is certainly predictive in nature, but it is almost out, it's beyond analytics and that there is often a learning component and an added sophistication. So it's used in very targeted ways. A lot of the issues and challenges we have with analytics are the same as what we have with artificial intelligence. I think the analytics space is in some ways more tractable, we can apply it to almost any app, any prep business process or issue within any credit union. So I always tell leaders, you know, where are your pain points? What are the things that you're not doing well, where are the biggest challenges? Because those often become great opportunities to apply analytics. So I think that's a little bit of where does it fit it's in that predictive nature, leveraging our incredibly large and growing volumes of data and not just volume of data in terms of the number of transactions, but then we have all this great social media data, or we have our call center data that may involve voice transcriptions between a call center representative and a member. What can we learn from those that help us create much more effective and efficient member experiences? So there's all kinds of ways that we can think about types of data we've never had really access to before. And how might we build that into a predictive model, if for instance, our customer satisfaction with our call center experiences are taking a nose dive. What can we learn using analytics from that that will help us right that ship a little bit more effectively?

(16:00): So, I think that's sort of the pieces. What I find in especially organizations that are starting on the journey or have started and sort of faltered a little bit. I think a lot of times there's recognition that we have all this data and that by having data, we can just start doing this modeling development and learn all kinds of new things. And in some respects, there's some truth behind it. But as soon as we start focusing on some more integrated processes across the credit union where it's not just a marketing initiative, but it affects the way in which we service members beyond how we might market different products and services out to them. And you're needing to combine data and knowledge from different spaces. Often the data we have is not quite the data we really need to analyze. And the data that we have may not be clean, it may be really dirty with all kinds of, when I say dirty, there's the same definition for multiple data fields. There are a whole bunch of entries in our transaction data for members who may no longer be active members. And we don't know they are not active members. And so there's all kinds of things we need to do to make that data usable. And that can become an overwhelming challenge for some organizations. Some organizations feel like they have to have the perfect set of data before they can start the analysis. And that's a perfect world, but that's not necessarily true. We can start with some data we have, we just may need to re-scope the types of projects and activities we do in order to get our feet wet.

(17:58): So I think there's a lot of things that we can think about in what's a perfect strategic infrastructure that supports analytics, but based on where we are today, which is typically not in that perfect strategic state, how do I best move forward? How do I get things going? Because ultimately one of the things we know is that organizations that have a strong leadership culture of being a data-driven organization and expecting all associates and employees in the organization to have the capacity or the appreciation for analyzing data in order to make recommendations and push decision outcomes forward. That's the night and day change where analytics really drives successful performance. And that is often something that is, I'll say, overlooked or not thought about because we're down in the weeds with the data. And yet this is a strategic leadership issue and helping credit unions find the pathway to make that happen, I think is one of the opportunities in this center.

Holly Fearing

(19:06): And that makes a lot of sense. I know, as a marketing person too, I'm very excited to learn along with your work and with our credit union members as we go into the outputs from this Center. I think there's going to be just so much value in that for all of us. I'm going to call on Paul now and transition a little bit into talking about this from Filene's perspective, in the context of why Filene felt that this was such an important topic so much so that we wanted to make an entire research focus around data analytics. Can you speak to what the story is behind that a little bit?

Paul Dionne

(19:51): Sure. Thanks, Holly. As we all know, when you think about consumer financial services, credit unions are a small, but mighty player in that sector. And you know, Filene took a scan and we decided to make a bet that data analytics and I think it's a pretty safe bet, that data analytics is going to represent a critical competency for credit unions going forward. And it's actually a real opportunity for them to position themselves as the premier provider of financial services in the future. It's not, you know, as Cheri mentioned, it's been a lot of people say, "Hey, we've had this on our plate, it's on our to-do list for a long time now." And Filene felt like it was a moment where we could contribute to that conversation and start to provide some actionable insights and tools to help credit unions actually get from the to-do list to the, "Hey, we're doing it"-list.

(20:41): You think about the new technological advancements that are built on all this new data. That's all around us, really proliferating around us. And using those advancements to provide the means for credit unions to improve really all aspects of their operations. I mean, data analytics can be useful for the back office automation, for security and risk management. You think about robo-advising and chatbot services, predictive analytics, all of which, when aligned with their mission, can really equip credit unions to better serve members in really profound ways, in ways that we haven't even dreamed up yet. And our hope is that the strategic and responsible use of data analytics can be a key differentiator for credit unions going forward,

Holly Fearing

(21:24): And without embarrassing Cheri too much. Do you want to talk a little bit about why we selected her for this role of the leader of this research and why she was the right fit for what Filene was looking for?

Paul Dionne

(21:39): Absolutely. We are so excited to welcome Cheri and you know, we identified her to serve as the Fellow because, of course ,she brings a wealth of research experience and information systems and how to use data analytics to enhance decision-making. And she understands how to advise organizations on the appropriate technology adopted that they need to create value. I think you've probably already heard a little bit of that from her today. Now, of course, there are a number of folks that have these skills but Cheri really stood out to us because she has operational and strategic experience in financial services directly. She's a credit union member. She's been a board member, she's been on important committees for credit union boards as well as other larger information tech organizations, nationally speaking. So she really understands the opportunities and the challenges that are facing credit unions today. She gets the dual imperative for credit unions to both grow sustainably and provide for member financial-wellbeing. And so we felt like Cheri was just an excellent person to serve as the Fellow. And we just can't wait to get started on the research.

Holly Fearing

(22:46): Yes. And I completely agree. I'm going to dive into some more specifics now about Cheri's work and ask Cheri to speak a little bit about what we've kind of promised to credit unions that the Center will deliver. So in the kind of high level overview of what this research focus is about, we are saying that this project is going to offer an actionable framework for effective approaches to data management and analytics for the larger financial services industry. Can you break that down a little bit more for us and tell us what that really means for credit unions?

Cheri Speier-Pero

(23:25): I know isn't that a crazy question in terms of the detail, but yeah, let me try to break it into pieces. So actionable framework and this really stems from the experiences that I've had in my faculty director role that helping credit unions, not just understand what sort of levers that need to be pulled, but actually help provide a playbook on how to pull those levers. And part of that, I think especially initially will be what are those capabilities within the credit union that matter in terms of delivering meaningful and value added analytics outcomes. And that may be human capital questions of what type of new skill sets, not that new people to the credit union need, but employees that are already at the credit union, what kind of skills might folks need to have developed in order to be effective. How does leadership of the credit union position and consistently, and continuously signal how being a data-driven organization is a critical sort of post that all of our decisions and all of our activities are going to sort of, I will say, extend to today and increasingly over time. What are the data, what's the data architecture and infrastructure that we need to have just so that we can not only store data, but have it updated in an automated way so that the team, whether it's my existing sort of managers who now have some skills that can go in and look at the data, or it's a data scientist that I hire, or that I bring in from the outside can have access to perform that analysis both today, and then go back to that same data six months from now with the additional transactions that have occurred in that time in order to enhance and develop feedback on that analysis.

(25:39): So, I think there's sort of, what are those capabilities and how do we build them? And part of the action too that I've talked with the Filene team around is how do we then create this ecosystem around a credit union? So that, that credit union leadership is not feeling isolated in trying to enhance this capability on their own. And this really comes again, back to my experience from Michigan State in the faculty director role. We've had as a Master's program such fabulous partnerships with to say dozens, 60 - 70 different organizations, where in some cases we have had multiple projects with credit unions and non-credit unions, but we've had one-off projects with organizations and those experiences provided for such context for that organization, not just to see analytics project forward, but to begin to develop an understanding of how to create a culture and create a set of capabilities that could enhance that success. So I see that as an ecosystem. How do we help facilitate university partnerships and technology firm partnerships with credit union organizations so that they can more quickly build out their capabilities and begin to examine the pain points or the opportunities to increase the efficiency of the back office or whatever it is that sort of is their critical mission in the moment using analytics.

Holly Fearing

(27:24): The current environment that we are living in is quite a bit more digital because of the COVID crisis, as everyone knows that has impacted so many things, including data and data privacy and security issues. So this is a really interesting angle of the work of this research center. We know that consumers are more concerned about how their data is used and less trusting of the security of their private information now than really ever before. So can you talk a little bit more about how this research and how this Center is going to be addressing the issue of ethical practices in the use of consumer data and all of that related to consumer trust of financial institutions, as we look at mining all of this data to apply it to everything you've spoken about.

Cheri Speier-Pero

(28:25): Yeah, Holly, I think you've certainly identified a bit of a sea change in consumer concerns with respect to data, privacy and security, and they're very valid concerns, both in terms of how any organization might use data, but also how any organization is securing that data, particularly data that is of high value sort of in the open marketplace, so to speak. So credit card numbers and other things that are particularly personal identifying information. So I think part of the goal around the Center developing capabilities in analytics has to be what we do to create not just a robust data environment in a credit union, but, you've used the word that responsible use and protection around data. And, and how do we then, I think the question becomes an organization struggle with this, whether it's analytics driven or not, is how do we communicate to our members what data we store about them and how we secure that data. So where do we use encryption? What data do we choose not to store because it is unnecessary to our to day-to-day business? How do we limit some of the data we store, because while they be a member needed to share that with us, when he or she applied, maybe it's not data that we need to maintain? So there's a number of things that organizations are doing a much more aggressive data governance assessment to make those decisions, and then ensure that the organization is living up to sort of those expectations.

(30:34): I wish I could remember the number at Michigan State and I don't, but it was in the millions, the number of social security numbers that were stored across the university in, I can't even begin to remember how many different unique computer systems and databases. And it was, information that, for lack of management, had just been dispersed across the university systems and universities tend to be really decentralized, but I think any organization and credit unions certainly fall into this aggressive data management challenge. What provisions need to be put in place to, to help mitigate the, I don't want to say eventuality, but I'll go with eventuality that we will have a security breach of some point? I think Robert Mueller was when he was FBI director said there's essentially two types of organizations, those that have been hacked and those who have not yet been hacked. And I mean, the activity we're seeing around cyber threats continues to increase. And so those are very real concerns. And so I think having responsible use and protection of data is essential for all organizations. And I think that's even more true for firms that are in the financial sector given the type of data that we store and we have available to us about our members. And so credit unions are on the front line there. I think beyond the privacy and the security as we move into developing predictive models. I think then we get into this whole sort of contextualization and ethical responsibility as well. So it's not just what we store about our members, but how are we using that data to make decisions? Is it done in an ethical, appropriate manner so that we are not in any way disenfranchising or biasing the outcomes we make? Kind of the digital red lining ideas. And so there's such growing awareness of these issues and thought that is going into how to do analytics and artificial intelligence in appropriate and ethical ways. But I think that becomes a clear pillar of this research to help ensure that, not just leaders, but those that are actually applying and developing these models have sufficient understanding and awareness and that we test those models in ways that we can detect if there's inherent and implicit bias or an unintentional bias that may have been overlooked based on the data we use to develop the model or the analytical tool we created.

Holly Fearing

(33:34): And we talk about in the overview of the center that there's an opportunity here for credit unions specifically to stand out within financial services and maybe even broader businesses as leaders in modeling the ethical use of data. I think that's a really exciting prospect. Can you talk a little bit about what is really that opportunity for credit unions?

Cheri Speier-Pero

(34:01): I very much believe that. And I really think that one of the advantages, that credit unions have is the strategic initiatives and thinking around the importance of creating financial wellness for members, as opposed to quarterly profit and of your profit and sort of those financial indicators. And I think that when it's not just the leadership, it's the entire sort of mission, vision culture of the organization sees that financial wellness of the members and the members wellbeing is our mission, since the members are the owners. It creates such a different way of thinking about what data is appropriate to use. What are the ways in which for using that data? I always use some examples in class where as I can see, as students in the room are hearing the stories, you kind of see their shoulders go up and it's like, yeah, that's that creepy factor, right? It's sort of this creepiness that any company knows that much about us. And the example I always use is, you know, Target, the retailer Target, has a well-developed analytical model to be able to predict which of its customers are pregnant, because if they can identify pregnant customers, one, they can, promote some things out to that customer in order to, hopefully make them a more loyal Target customer. But it's a customer lifetime value proposition that the earlier I can get a customer committed to my retail establishment from all of those great baby clothes and toys up through food and teenage electronics and clothing and beyond, that’s a tremendously profitable customer, lifetime value proposition. But then when you think about being able to detect who's pregnant, that's curious some people get that creepiness going.

(36:10): And so I think within the credit union framework where strategically, we're focused on members as their well-being, their financial wellness, I think there's a different orientation by all of the folks within the credit union environment. They're less likely to create those applications and those analyses that engender that creepiness for our members. And I know creepiness is not a great word. I can't measure it. There's not a scale for it. And it's really important. I think there's changing and differing demographics around who feels that way and who doesn't. I think a lot of our younger members are like, "yeah, they have my data. Of course they know those things." That's a stereotype that may or may not be empirically true. We'd have to test that. But but, but, so I think that there's, there needs to be a sensitivity and I think organizations in general are growing, have growing awareness there. I think credit unions is organizations will always keep members and the member satisfaction and concerns about anything the credit union organization would do to harm that satisfaction front of mind. And so are far less likely to find themselves, I think at odds with some of the analytical work that gets done and the types of perceptions that their members generally,

Holly Fearing

(37:43): I know that it's certainly is a factor, the creepiness factor, even though it's not a data set that you can measure, it certainly exists because we're human beings and we don't all follow logic. And then it's interesting too, with different generations and how the different generations may feel more accustomed to having less privacy or giving their data up in exchange for what they want or need online. And then when you factor in the credit union cooperative element, I know I personally feel more trusting of a credit union or a cooperative when the structure of the business is set up so that I am the owner of the business. I naturally feel like there's going to be more respect given to my data as a member, rather than something like Target where I truly feel like just a set of data to them. So lots of really interesting dynamics there.

Cheri Speier-Pero

(38:45): There really are, and I think it's hard for any organization because as those dynamics change, whether it's across demographic groups, or we just become more accustomed to recognizing our privacy. We've lost a lot of our privacy over the last decade or two decades. That it's hard for those folks within the organization to recognize that there are such differing views for our members and our customers. So that, you know, I may come with a certain view because I don't feel privacy is as critical of an issue. And yet I don't recognize that my members may feel very differently. So I think there's a lot of challenges and awareness we have to work with, but I do think credit unions probably don't tout that, because we have this co-op environment where owners are our members. We do come to the table with a different sort of world view on how we use data and what data we have. And it's perhaps not a critical part of a marketing campaign or a story. Right. But I do think it might be interesting to think about how that messaging may be very subtle messaging becomes part of how organizations do move analytics forward.

Holly Fearing

(40:12): So with the flood Gates now open on this research project, what are some of the things that you're most looking forward to doing in the first few months of this work and what are some of the early projects that you've got going?

Cheri Speier-Pero

(40:26): Yeah. So we've been talking about doing some initial research to be able to have some benchmarking and know, not just generically where credit unions are, but maybe across different types of credit unions and whether that's size or whether that's sort of maturity in the analytics space, what what's happening in the credit union space and what are some of the most critical capabilities that we need to focus on. And then I think it's opportunities to do some deep dives in those areas and understand, no, I won't say necessarily understand best practice, but to develop some perspective on where have we been able to see significant success by different credit unions and how then as a credit union that maybes at the beginning of the journey, how do I get to that stage more quickly with less cost and effort? And I think those are all pieces that are sort of on the first couple of months. And then I look really forward to sort of this ecosystem side of things, and that's probably further down the pike, but I just really believe in these partnership models and want to be, I think it's exciting to think about how do you deliver a partnership ecosystem on sort of an industry scale. Because we, I don't know of anybody who's really thought about it that way. It's usually, you know, one organization and its partners, but to sort of, how do you do that at the credit union industry level, I think is really exciting. And I know ,in speaking with some of my academic colleagues at other universities, they're very excited to be part of this, so it will be fun.

Holly Fearing

(42:08): Definitely. And so Paul, for the credit unions that are just eager to get involved in this work and to go along with us on the journey and follow along and dive in, what are the things that credit unions can do to get involved as we begin this work,

Paul Dionne

(42:23): This event, this very exciting event, that's going to kick off the whole center coming up in January, which will be a real opportunity for Cheri to bring in some of her strong cast of researchers and industry leaders and other practitioners who can bring some cutting edge insights that participants are going to be able to draw on to activate data analytics in their organization. And you've heard her talk a little bit about that ecosystem approach, and we'll start to, you know, we won't be able to get a lot of that research done right away, but we hope to start at least fleshing out, what does that ecosystem approach look like for a credit union when they want to get involved in data analytics. And we'll provide some insights during the event on how does those first steps to get that process underway? In the short term, there's the benchmarking survey that Cheri mentioned, and I expect and hope that every listener that your credit union will participate. Stay tuned for an announcement about that survey. And we hope you'll participate. We're actually hoping that the survey will be something that we might do every year on an annual basis. So we can track over time where the credit union system is from year to year and ideally identify trends and be able to address those. And then the more in depth research projects as well, we really want to partner with credit unions to help Cheri identify, you know, those more specific applications of data analytics that are taking place, or that are about to take place that are going to drive business processes and member service. And so we're hoping that the research that, that credit unions who partner with us in the research will also benefit they're going to learn from Cheri's insights and experience. And ideally we will start also exploring this partnership model that Cheri has done over at Michigan State and connecting universities and Master's students in data analytics with credit unions. So that's another project that we want to hopefully get off the ground in the next year or two to provide an opportunity for credit unions who maybe don't have a data analyst on staff, you know, an analytics person that they can devote to the work, but maybe they have a university nearby with students who might be willing to help out.

Holly Fearing

(44:29): Great. We'll definitely put some of that information in the show notes. So our listeners can follow along with all of that great stuff. So I wanted to spend a few of our last couple minutes here, just letting people get to know Cheri a little bit more on a personal level. So as we were talking, Cheri, I just had the question around just like your analytical thinking and kind of mindset to data. Is this something that you've kind of always been an analytical thinker and driven by data, or did this come about at some point in your life?

Cheri Speier-Pero

(45:05): Yeah, really good question. I'm trying to remember what my provost uses as her data label, and it is not coming to me, which that does happen more and more often, unfortunately, but yeah, I need data, and have always been that way. And, I like to think that I'm strategic in how I think about opportunities and problems, but then I want to be able to drill down in the data too, whether it's confirm or to understand if where we are today and the changes we have made in different initiatives, the impact those have had. And then as we trying to build out in a more strategic way. So for instance, at Michigan State, we talk a lot about student success. How do we increase student success is often defined as seeing students persist through graduation. And then we get to satisfaction and other kinds of issues as well. But I think that really understanding where are we seeing success? Where are we not seeing success? What have those students experienced? What have they not experienced? Can we isolate it to a course? Can we isolate it to a human interaction that they have or haven't had? And so it really gives us having that level of detailed understanding, I think allows you to set up a game plan for the types of initiatives you want to take, and then being able to monitor those initiatives and assess if you're on the right track. And if those initiatives you're moving forward are sort of changing the trajectory of where you want to go. And so, yeah, I've always had the analytics kind of data geekiness in me and it's a good thing. I like it. I just think it's important to have it coupled with a strategic view at the same time.

Holly Fearing

(47:01): Of course. Yeah. And so outside of data analytics and data analysis, what are some other research topics or themes that you just get really excited about?

Cheri Speier-Pero

(47:13): Yeah, so a lot of my early career was really how does using technology support outcomes and often those outcomes were decision-making types of outcomes. And a lot of times my research became experiences. So, if I go back to some early work, it was how to interruptions, both interruptions that get created by technology, but that may get created for other reasons, how does it affect what we're doing? And you know, at some point there's a negative effect but then how can you build capabilities into technology to help you more meaningfully recovered from those interruptions? So sort of just some really interesting personal experience is that kind of made that light bulb go on. So that holds sort of decision-making and effective adoption and integration and use of technology. How do we know that technology is making a positive impact and what has to happen in an organization or to support an individual to make that technology effective for the organization in particular. The other space that I've spent some time in is, a lot of my early career before going back to get my PhD involved, manufacturing and organizations and warehousing and logistics. And so it created a real passion for supply chain issues. And, and what I love about supply chain is, certainly at Michigan State, we see it not just in the product space, but also in the service space. So anything we do to deliver a loan opportunity for a member, there's a supply chain there that, that we have to deliver upon and execute well, so that the member is satisfied. And so a lot of work has sort of gone into what technologies and how do we implement and sort of design those technologies to support those supply chain interactions. And I think that that continues to be an exciting workspace, especially in today's very global digital environment.

Holly Fearing

(49:20): And outside of your academic work, what do you like to spend your time doing?

Cheri Speier-Pero

(49:26): I wish right now I had time that certainly that's been one of the challenges in our pandemic environment that every day at a university. And I'm sure every organization creates some significant shifts in how we try to support our students and, and create an effective learning environment. I know, but I do have a variety of interests, I'm a runner. So I run and, and sort of focus on other sort of physical, trying to stay physically fit, which in again, in our current times has also helped with some mental fitness, I think. My husband and I, we have four boys that are not boys. They are young adults and maybe not so young adults at times. And so being able to spend time with them and enjoy them is certainly a priority. I'm a big reader. And so whether it is, as I call it my bubble gum books of mystery and suspense, or lately, it has been a lot of history and we have as I'm sure a lot of organizations are really putting forward significant effort in the diversity, equity and inclusion space, doing a lot of reading in those spaces and, and finding it enlightening and helpful in the conversations we're having in the context of our organization.

Holly Fearing

(50:57): What inspires you to work as hard as you do?

Cheri Speier-Pero

(51:02): I think I've always been sort of challenged oriented that. If I think about the first couple jobs I had, that I left probably earlier than my bosses thought I would have it, I got to a point where the job become routine. There was no challenge to it. And I lost interest. There was nothing motivating me to go out and do something new and different and make a difference. And I think for me, that that sense of making a difference is a really critical component. And that's, I think where, as I have told people, I have the best job in the world as a university faculty member. Because I can research things that interest and excite me, and I get to spend every day with some young people who are amazing and they are energized and so excited, and they do more in a year than I think what I did in my college experience in terms of giving back, being part of charitable organizations, getting back to the community, lping their members, student members within our community. And it's just such an invigorating experience that it really is exciting to be part of.

Holly Fearing

(52:27): Wonderful. Our parting comment, I guess I'll say our last question here. What advice might you have for any credit unions listening that really want to start this work, start their application of data analytics and start standing out as a leader in the responsible use and protection of their members data. What advice do you have for them to get started?

Cheri Speier-Pero

(52:53): Yeah, I would say what we've learned from agile design and thinking is, and I know this is going to sound facetious, as I say it, but just do it, just get started. Don't try to build the biggest, most complete sort of mouse trap in terms of data and tools so that you are starting with the most critical, most value added, probably most complex project go after something and, and go with your, take your existing employees and, and find someone who's who has that little data geekiness and who's a really good spreadsheet user who is willing to learn about some tools that are accessible. So whether it's a visual tool, whether it is Tableau or Power BI or something that there's a lot of training and tutorials out there, and for somebody who's kind of comfortable picking up technology, he or she can do that and have them start exploring the data.

(54:01): And, what we have found is that just applying visual tools, whether you're in marketing, whether you're in finance, whatever the disciplinary area is, if you can just sort of learn a little bit about that tool and apply it to data that you use all the time, it's been shocking, what these individuals see in those visualizations of their data, that they never detected when they were making their day to day, week to week, month to month decisions. And so I think there's some really small step ways that credit unions can get some wins without spending a lot of money to begin to build some momentum and interest, and then getting to a little bit more, a robust set of individuals who are excited about and interested in moving forward and are well-positioned to identify what some of those business process issues are. That if we look to find analytics models, we are likely to find and create that value for the organization.

Holly Fearing

(55:04): All right, Cheri, that covers pretty much everything I had for you today. Paul, did you have any final comments or questions for Cheri?

Paul Dionne

(55:15): We may have to record another podcast Cheri, because Holly and I are both avid runners. So that might be another topic for another time, but we'll talk.

Cheri Speier-Pero

(55:28): Sounds perfect.

Holly Fearing

(55:29): Well, thank you so much, Cheri, for all of this. This is we're off to a great start and I know there is just going to be more and more and more of this. So this is just the beginning, but thank you so much for kicking us off like this.

Cheri Speier-Pero

(55:42): You're very welcome. Thank you so much for having me.

Holly Fearing

(55:44): All right. That's it for the Fill-In folks. Thank you for listening. And of course, a huge thank you to Dr. Cheri Speier-Pero and Paul Dionne for taking the time to talk with us today. If you feel inspired to move your organization forward on your data analytics journey, you'll want to follow along with Cheri's work by visiting filene.org/data. There, you can see all research published to date within this focus area. Also be sure to check out our webinar with Cheri this past fall. You'll find a recording of that on our blog page at filene.org/blog. And if you're listening to this in January, you'll have an amazing opportunity to hear from Cheri live at our first ever data analytics research event, register over at filene.org/analyticsecosystem. We will have some of the session recordings available after the event, but not all. So if this content is top priority for you, be sure to join us live if possible. Okay. If you liked this episode, please do rate us on Apple podcasts. So more people can find us and make sure you're subscribed to the Filene Fill-In podcasts so you can keep up with what's going on at Filene. You'll find us on Apple podcasts, Stitcher, SoundCloud, Google podcasts, or wherever you get your podcasts to get in touch about today's show, email me at [email protected], or find us on Twitter @fileneresearch. Until next time. Thanks everyone.