Transcript
This has been generated by AI and optimized by a human.
Show ID (00:04):
The power of data is undeniable and, unharnessed, it's nothing but chaos.
(00:09):
The amount of data was crazy.
(00:11):
Can I trust it?
(00:12):
You will waste money.
(00:14):
Held together with duct tape.
(00:15):
Doomed to failure.
Jess Carter (00:16):
This season, we're solving problems in real time to reveal the art of the possible. Making data your ally, using it to lead with confidence and clarity, helping communities and people thrive. This is Data-Driven Leadership, a show by Resultant.
Jess Carter (00:34):
Hey guys, welcome back to Data-Driven Leadership. This week we're joined by Michael Schwarz, senior vice president of professional services at Resultant. Michael has spent his career helping organizations navigate some of the most complex challenges in business and government, building scalable digital solutions, guiding teams through transformational change, and using data in ways that actually drive decisions.
2025 has been a year of fast-moving technology, unpredictable markets, and rapid shifts in how organizations work. In this episode, Michael reflects on the lessons from the past year and gives his predictions for 2026: The innovations that will really matter, the trends that leaders need to pay attention to, and how teams can stay resilient and adaptable in an era of constant change. If you're a leader, a tech enthusiast, or just someone who wants to understand what's coming next in the world of data and digital transformation, this episode is for you.
(01:24):
One last note I want to make that's kind of personal. I've had the privilege of working with Schwarz for the last six years—we call him “Schwarz”—and I can't begin to tell you how much I've really appreciated his steady hand, his insights, his perspective, and what I really love about this episode is in that sea of constant change, he has this overarching view of optimism, of “What can we learn from this? How do we grow from this?” You just can't listen to him without appreciating his growth mindset, and I think that's something we can all use right now. So let's dig in. I hope you enjoy this episode.
Schwarz, welcome.
Michael Schwarz (02:00):
Thank you. Glad to be here.
Jess Carter (02:01):
Yeah, absolutely. And I do refer to you as “Schwarz” because there are a few Michaels. We have had the distinguished pleasure of working together through highs and lows for more than half a decade,
Michael Schwarz (02:12):
About six years, I'd say.
Jess Carter (02:13):
Six years. But before that, before you were in more general private sector, public sector, consulting, health care, most of your career,
Michael Schwarz (02:21):
Health care’s a good proportion of it. So prior to joining Resultant, and I spent about four years on the facility side of health care, so I led decision support, clinical search, some other IT functions within a regional health system, and I did that for about four years. My passion prior to that was still in consulting, but trying to use data to get into some of the areas that impacted social determinants of health. So really, prior to that was 16 years in market research. It was in consulting. I cut my teeth on the data side, hammering keyboards, building databases, but all of it culminated through learning about data, how it's best used for predicting human behavior, how you can join different disparate data sets together, and then the health care journey. It was a really great experience for me. Got to be on the non-consulting side of things, got to be an operator, really start to figure out how to move levers, how to create initiatives, create budgets, create outcomes for the system. But ultimately my journey did bring me back here to Resultant where we get to do a lot of that same work for many others.
Jess Carter (03:26):
This is why we do this is there's pieces this we've never talked about in six years. What did you go to school for?
Michael Schwarz (03:31):
So I was information systems. So I went to school, my undergraduate degree. I thought that I was going to be in media of some sort, so some sort of electronic engineering, some sort of production engineer. And pretty quickly I learned about a new initiative that was coming out in the mid-nineties around information systems. It was based in the school of business. It was right around how to build information systems, business systems, ERPs, databases, and I jumped in. So I was one of the first graduating classes for my undergraduate degree on that side, and I did spend the first five years of my career on the keyboard programming, learning technical systems. And then I did want to get out and start to work with people and I wanted to start to engineer ways where we can educate business users on how to use data. So I did go back and got my master's degree in business and started moving down the path of project management consulting and just all those areas that helps tie some of this together and apply use cases and business application and outcomes to data that we were collecting in that time period.
Jess Carter (04:31):
You have that information systems background, but marketing kind of got it before most other industries, right?
Michael Schwarz (04:38):
They did. It seemed like, at least in my experience during that time period, I think there was a lot of data collected internally and operationally. People were figuring how to use. And the manufacturing sector figured out very early on they were looking at control charts and defects and everything that goes into tracking a system, but then it started moving into business-to-business and relationships, and it did seem like the information collected through customer behaviors and interactions caught a little momentum, little more momentum specifically in the world that I came from around market research. The roots of that were knocking on doors and asking questions or standing in malls with clipboards and you started to think about, well, how do we get that same information without having to ask that question? And that's I think where we're at today and we're still even moving through the datasets we create through predictive analytics.
(05:31):
It really is rooted in, well, how do we create some of this data to drive decision-making without having to ask it and to look at different ways of looking at attitudes of people or behaviors. And so whether it's a purchasing decision, whether it's in health care, whether it's a relationship, whether it's social media, I think a lot of what was found and became interesting during that time period is how to look ahead at relationships. And I think that's over 25 years, that's what's changed so much for me is what's an objective bit of data and how are we using it to strengthen relationships or build different business decisions.
Jess Carter (06:07):
Yeah, yeah, that's awesome. This is the year-end episode, and so we're wrapping up the year. 2025 has been, I think for both of us, I could say just breezy, just a super breezy year, very worked as expected. All of our predictions came through.
Michael Schwarz (06:24):
Talk about a graduate degree or a PhD. 2025 was a real graduate degree for a lot of us.
Jess Carter (06:29):
LinkedIn should just give us all a badge, you know what I mean? Yeah. So obviously AI adoption has accelerated so much faster than I think I expected. Budgets are tightening like crazy. I was talking to a neighbor the other day about how they're thinking about procurement and we'll get into, maybe I'll bring some of that up later, but it's like everyone's rethinking how to do business. That's what's happening. There's so much of forcing functions of different tariffs and all these things, and so I'm sort of curious at your seat where you're helping so many pretty large organizations and small ones transform through 2025, what did you observe? If I could stare at 2025 through Schwarz's eyes, what were some of the defining things that occurred in your opinion in the market?
Michael Schwarz (07:15):
There was a lot of, well, what do we do now? Where do we go? And I think if you peel back that question a little bit, wherever you're at on your data maturity or your application architecture, there seems to be this balance right now of power and responsibility. Now that, if you base the assumption that your organization or your entity of any sort has a strong data foundation, now that these tools exist, that these methods exist, now you're thinking about, well, how do I create power while being responsible at the same way? And so for the process engineers and industrial engineers, it's got to be just chaotic. You create too much responsibility, you'll inhibit innovation in some ways, and if you create too much power, you're going to go rogue and there's real risks at this level, whether it's as far as deep fakes or some hallucinations.
(08:17):
It really is creating this smooth path down the middle of understanding where you're at from a data perspective, what problems you're trying to solve, the skillsets that you have, the risks that you're willing to take, and I think it'll be a little while before we can just completely lay blueprints. We have frameworks, experience, we have knowledge about business, but really it's a chaotic time for those with authority and responsibility over some of these assets to really find that balance between them and experience will take time. I think the use cases and the business problems that we set out to solve will make a big difference, but even beyond that, I think from the individual and the talent perspective, it's creating a balance between the very technical and the very business-oriented.
And as disruptive as the last 12 to 18 months has been, personally, I'm excited because it is this forcing function of bringing technical and business resources and teams together where for a period of time there might've been the technical organization and the business organization and the product organization. It's really just forcing it to come together and creating a good tension on where innovation comes from.
Jess Carter (09:34):
I love that last piece especially because I think most of us in our careers in the last five years, that was the reality. You had your IT team. Maybe there was this idea of a data team separately or within it, and those were different things. A chief data officer, CIO, how does that all work? Now there's a chief AI officer, so to your point, I'm not hearing you comment on, you have to have all three of those all the time. What I'm hearing you say is your business leaders need to work more closely together as the first team maybe than before.
Michael Schwarz (10:04):
Absolutely. Back to the power and responsibility shifts and waves that are going on out there. I mean if they're not talking, someone may go way down the power end and start creating all kinds of things that aren't great for the business that maybe create, introduce, create, and introduce risks, but it is forcing them to get together. It's forcing more conversations around the why and what do we want to get out of things versus building a thing. There was back to when I mentioned early in career, it was, yes, of course you have to have a CRM system. Yes, of course you have to have an ERP system. It was what you did. And then the next few years was, well, how do you link it together? Okay, well now we can link and they're communicating together and billing and purchasing and invoicing. We got it.
Now it’s, we have somewhat of harmony across these systems, but what are the use cases? What are the business problems collectively that we can now solve that all of this technology and all of this data and all this traction exists? It's not just the business applications manager, the data manager, like we said, they've got to work together and understand everything from how we're receiving the information, who's using it. Again, the problem we're trying to solve.
Jess Carter (11:25):
There's so many assumptions that you just baked in by being high level, which is perfect what I asked you to do, but there's also organization's ability to pivot. You just kind of walk through three or four transformations of a business of you got to first we're doing this and we're systems first. Maybe you used to have a spreadsheet, it's your CRM, now you got to get on a CRM and we got to have some of these source systems and then we got to realize the source systems aren't the end all be all, and then we have to operate as a team. And part of this to me is, and maybe you disagree, I feel like even me playing with AI right now, there's almost a new forced tolerance for sunk cost in just trying new things. There's just a little bit of like, hey, we just have to be flexible and give some things a shot and see what gets sticky. Do you agree?
Michael Schwarz (12:13):
Completely agree. I think there's a high curve with AI and we're at the top and there's some uncertainty and there's been some quotes that we've said internally and otherwise, but those that don't understand AI were the ones that are going to be left behind, and I think that's true. While there isn't total clarity into what the next one, five, ten years look like, this is an area that I think business leadership and government leadership and nonprofit leadership understand that this is going to be part of the way we do business to some extent going forward, and we have to dip our toe in the water. It causes a forcing function for some of the areas where you wanted to get into AI and something a little bit more innovative where you've got to harden your infrastructure or your foundation a little bit more. This is a tipping point where you really can push that conversation where in this whole evolution of, like we said, the business systems and maybe they're disparate, you're like, well, I really like to bring them together at some point I really think they should communicate, but I don't know why.
(13:14):
And now we're getting to that point where you can say, well, yeah, you can do this and you can predict this behavior. You can see around the corner, or you can get better at inventory management and purchasing. Now you can start to see all these things coming together and create that decision support from a business perspective. Some are moving ahead very quickly on the innovation side. Some are having a wake-up call of saying, alright, I know exactly how I'm going to create my foundation at this point. And so the movement's really just I, it's just started and everybody's at a different spot. Everybody's not at the end point despite what you may see in headlines. Not everybody's there. That's why it's headline and news cause they’re there. A lot of the rest of us are still figuring this out.
Jess Carter (13:56):
Well, I like that you're normalizing that because there is, there's this market FOMO, there's this market “I don't want to be left behind.” People use that for you. I'm going to be left behind, I'm going to be left behind. But you didn't say you're going to be left behind unless you adopt it fully. You said you're going to be left behind unless you don't. You use the word understand AI, and I think that's appropriate, right? You don't have to use it for everything. There's appropriate uses of AI. There's obviously inappropriate uses of AI and I think there's a level in which what I think is humorous in today, and we're going to get to maybe this is our pivot point to talk about what do you think is coming in 2026, if you had your Schwarz crystal ball, other than more golf trips.
To your point, there's some organizations that have, they're more strategic, they're forward thinking, they're really pulling their leadership teams together. They get strong leadership teams, they've got a real business strategy, they've got a corner on some part of a market and they're behind because they haven't leveraged AI yet. They will catch up so freaking fast once they harness it because they've done all the other hard stuff. Leveraging AI I actually don't think is wildly hard. I mean maybe it depends, but to me it's that other stuff that's the hard part to make it as valuable as possible. You know what I mean?
Michael Schwarz (15:11):
Well, I think there's part of it is making it valuable. I think when I talk with the market organizations, really one of the biggest resistors to jumping into AI in some cases is that nasty behavior of self preservation and think that immediately it's going to take over what I'm doing and there's some areas of that, but for the most part where it can be used for good and where we want to use for scale is in that workforce multiplier. And so where you can become more efficient, where you can do more, where you can see around corners, the reality is business, government, whatever it is, I mean it's competitive in nature and you're trying to get places faster. And as long as if you use it towards that vector or that direction, there's great things ahead. But it still takes understanding the market, understanding business, understanding your customers and your stakeholders, how you'll get ahead. That's where it is. It's not just at the executive level, it's not at the mid manager level, it's not at the line level. There's been other forcing functions over the internet and email. Then it comes into slide decks, all these things that we're going to take away the old whiteboards and we've faced them, this is probably greater, but it's just the way that we're going to evolve moving into next year and the next four to five.
Jess Carter (16:31):
It's funny that we talked about marketing earlier, Seth Godin, Linchpin, I dunno if you've ever read that book, but it's like old, it's like 10 or 15 years old now, and the concept was right. People, they want to be the linchpin when the reality is if you broaden your horizons and create processes and systems and teams who can do things, that's how you become a linchpin. You don't become a linchpin by being the literal linchpin and we're doing it again, we're learning the, it's like these adult learning cycles of like, oh, here we are again. If I let AI do something for me that understandably could make sense and help me be a better leader.
Michael Schwarz (17:03):
Whereas if we really go back and look at just classic books, Jim Collins's Good to Great, the book has to be 20 years old at this point, and it's deciding where you're at that breaking point in your business or that opportunity point in your business where you change some of the behaviors. And I think over time it happened to other companies at different moments of their growth and this time it's a forcing function from the outside where it's saying, no matter where you're at, you got to consider this. And that's where understanding your operational or your data-driven journey is at. So no, you don't need to go from step one to step ten overnight, but if you're at step one, let's start moving to two, three, four pretty quickly or you left or before some opportunity presents itself that you're not ready for.
Jess Carter (17:54):
Maybe some of what your predictions are for 2026, but it's like, okay, we got through our…LinkedIn really should make a badge for 2025. We got through it. We're looking at everybody else's predictions in what 2026 will look like. Do you have any other insights or predictions of what Schwarz’s Crystal Ball is telling us
Michael Schwarz (18:14):
For next year? Couple of things. One, I think from a mid-market company perspective, I think from a government perspective, I think we'll normalize from ‘25 and I think opportunities will present itself. I think there will be more outreach with customers. I think that internally, I don't know that there will be as much of a push in some of the roles you mentioned earlier. I don't think necessarily everybody needs to go and put a label or a jersey on a new chief data officer, chief AI officer, chief technology officer. I think we've been through those waves, but that doesn't mean that there's an absence of leadership or advocacy towards these things. I think that there's facilitators, I think it comes from leadership and I think that it's on us in the services industry and I think the market perspective as a whole just needs to collaborate more because I think the resource mix will change.
(19:09):
I think it's going to be all of our responsibilities from a leadership standpoint to understand AI and to bridge that gap between power and responsibility. I think at the basic level it's understanding how to be efficient and plan for growth in business and responsibilities over the next year. I think it's especially on early careers and early graduates to transform the way that maybe even their education was presented to them in the last few years because it's going to change and it may not have been in a textbook, it may not have been in a classroom or that's the group that really is probably looking a little bit scared into the market at this point because they just have to pivot the things that they were told in the last couple of years and really get their hands on a keyboard, get involved in a meeting and start to do some learning outside of work or whatever it is to catch up and help us really understand where we're going.
Jess Carter (20:03):
I think that that's super insightful because seeing that all over, especially in ed, higher ed, jobs right out of college, that's coding computer science, those were such stable, secure jobs and I don't know that it's fair to say that they aren't yet, but we are seeing waves where it's like, hold on, how do we leverage AI? And there's a human in the loop component. There's all these phrases. But I think the other thing I was going to ask you about, this is nerdy, nerding out, permission for us to put on our information systems conversation, which I don't have a degree in that. One of my questions for you in 2026. So if you look at one of the things that I think the AI sprawl can be confusing for a lot of people, and so my understanding that I think is, and you can correct it please, but it's like you have AWS, Google GCP and Azure sort of as these infrastructure companies that are helping you build the infrastructure for UA. You kind of have this middle layer and then you have the products, just tons of products that are adding AI. And so it's as even as a leader managing where and how to understand AI is getting complicated, but I think that structure of like, hey, it's interesting, isn't it? That there are three huge, big dogs at the bottom doing all the infrastructure stuff. There's already thousands of companies building out their own product-specific AI. Do you have any predictions of any of that being, I don't know, upended?
Michael Schwarz (21:33):
Yeah, I mean I think we've seen it in different iterations over time. So those hypercompute and hyperscalers, they provide that infrastructure that can scale to the level where we can start running some of the computation consumption that aren't for really sophisticated things. I do think there will be consolidation in some of the micro applications and accelerators and agents and all those things over the top. We've seen it over time when it comes to cloud databases, whether it's visualization tools, whether it's workplace productivity tools, if we look back over time and some of the companies that got acquired by the big ones or the ones that got consolidated together, I think it's a natural evolution of innovation where some of the other ones, the smaller companies will come up and create a real niche that then gets consolidated with another niche, creates a really great product and an ecosystem with that from a talent perspective.
(22:34):
Back to the comment on some of the folks coming out of school with the technical degrees, that's what won't go away in my mind, is really the educational understanding of how these things work versus just moving some of the accelerators around. And so if I were just about to send to kid to college and it's important that understanding why these things are happening, just how to use them at this point in life, I would know just how to use it, but I'm absent of the real in-depth knowledge really what's going on behind it. I think those are the skill sets and the talents that will stand the test of time through whatever consolidation happens in the market from an accelerator standpoint.
Jess Carter (23:18):
That makes gobs of sense and I really appreciate you extracting that, too. If I'm in this season of life, I do, I mean, I have younger kids and I'm almost grateful like we got to figure this out right now for some of my siblings that are younger to see what happens in the job market. And I think that guidance is sound, it makes a lot of sense and it's not reactive, which is part of the important piece here is how do we just do our best to understand you don't have 25 years’ experience. Your job is to understand maybe it's almost like just an engineering mindset, just tinker.
Michael Schwarz (23:54):
It is an engineering mindset. I mean, I remember when I was in school, when I started, it was if you had a CD burner, you were the coolest person in the dorms. And pretty quickly it turned into online streaming and these were other college kids figuring this stuff out and disrupting industries and look at us now. I bought a car ten years ago and didn't have a CD player and it blew my mind. But these things changed from a consumer standpoint. Usually, oftentimes, I think some of these innovations to that point will hit consumer industries before they hit business-to-business and government. And that's okay. That's just really kind of a change mechanism and a behavior of change individually versus an enterprise. But it will be interesting to see the next couple of years from the talent perspective how it changes and how we all evolve. The good part is, is we're not sitting still. So if you're excited for change and don't mind a little innovation, it's a great period and I think your level set some of that disruption and the noise will go away and we're getting a lot of direction from some of those product companies from the use cases. We're starting to see the benefits that some of those early adopters have realized in their AI and in their data journey and in great use cases and good materials that are out there.
Jess Carter (25:10):
So one of the things that I was excited to ask you is, in the six years I've known you, maybe the most anchored, stable coworker I've ever had. I feel like I could be like, listen, there are 42 dogs that just somehow got let loose in your house and you'd be like, okay, alright. So there's a little bit of, if I was a C-level executive in a disrupted industry or market that we're in right now, you are the leader I would want giving me advice. Because one of the questions I had was, with so much new tech and opportunity on the table, AI could be anywhere. How should I decide where to experiment? How do I put my money in the best, most viable because there is going to be some waste in the middle of a really efficiency-driven market. So it feels really dangerous to be like, I want to play, I'm going to have to fail. I better play with the right things, and I want to do that with someone who's not reactive, who isn't quick to just be excited to be a partner with me and tell me to go spend my money on whatever they want to help me with. So if you were going to advise me in that way, what some of the things would you say? What are you priority saying to clients? Yeah,
Michael Schwarz (26:17):
So I think some of that composure comes from when the 42 dogs come in, I'm probably stepping back and saying, well, everyone else has 40 or 50 or 60 dogs and it's on me or it's on us to navigate how we solve that problem. Even from AI and any innovation that we have in front of us going forward. I think similarly to how I think about creating a data strategy, there's kind of two ways that you can look at it. So one would be to solve your inefficiencies and the other is to maximize your opportunities or your expansion areas. I'd say generally, a lot of times your inefficiencies can be very procedure/process-based at this point, right? There's innovation that can be there, but a lot of times some of those inefficiencies are business rule-based or process-based. The innovation up here is where you can start to look at some of the AI and some of the innovative components and accelerators that we have right now, right?
(27:13):
One of the reasons I think AI is so disruptive is, think about AI on the other side of the vector of the process side. I said a couple of times when you create a process for maximizing efficiency in a manufacturing factory, you benefit manufacturing in that factory. You don't necessarily take that into other industries, but it doesn't necessarily translate into product development or life science or health care.
You take something up here on the opportunistic side that starts teaching you how to look for gaps in the system and how customer behaviors think and purchasing patterns and those types of things. You can start to take that across industries and that's where it becomes really disruptive. And I think the ones that look at those areas are going to be described as probably the most innovative in their areas. And I think that that's why some of those niche accelerator companies around AI are doing so well, cause they’re so transferable, and then it becomes on the business leaders and the middle managers and the line-level employees to help start to understand and say, this decision mechanism started looking at gaps in our inventory and how to fill, and maybe that's staffing gaps, maybe that's product gaps, maybe that's accelerators for our SaaS products.
(28:32):
So it just becomes very, very innovative. So long story short, I'd probably focus a little bit more on the opportunistic and go-to-market side just because the amount of innovation you can get there and the scale that you can get with focusing on some of those elements.
Jess Carter (28:47):
What I feel like you just basically said is if you can think about how whatever you're doing, if you chase the right strategy of your business, there's probably ways to extrapolate the way you're leveraging it to solve lots of other problems in your business, which is you're kind of building this infrastructure, the learning iterations, the framework to go leverage it elsewhere. So it's actually still an investment.
Michael Schwarz (29:07):
That's right. And it's growth minded, right? You're looking to grow managing a business or government or nonprofit. You're always managing the expense and the efficiencies, that's happened since beginning of time. It always will happen, and there's great ways that you can do it. All things equal at this point in time, at the end of 2025, I'd start focusing a bit more on how do we scale growth and how do we make things a little bit more seamless from a customer or patient stakeholder experience. Because we're all wondering on what happens with relationships post-social media, post-working from home, all these things that have changed over the last 10, 15 years. And I think that top end of engagement and how to create efficiencies and personalization is just, that's where I get excited. So it's more personal recommendation and a little bit rooted in the business side, but I think that's where things differentiate and companies will start to separate.
Jess Carter (30:08):
I love it. And again, this is where I think the value proposition of a partner in someone who's got this leadership experience on the people side and the tech side of, hey, it's not sunk cost. Like, yes, I get it in this month, in this quarter, sure, maybe, but the reality is you're learning. It's a growth mindset. There's whole companies and owners who don't understand that and wouldn't know to defend it that way, but that's real. It's just a major aperture opening.
Michael Schwarz (30:35):
It is, and it's a change of leadership because it's a little bit more unknown. It pushes some conversations. But in my experience in 2025, even if AI didn't hit on a project, the amount of knowledge that was gained about the openness in the process and the customers is you can't put a number on it. And so it's part of the journey in pushing some of those conversations and doing some of those trial and error to be cut out.
Jess Carter (31:03):
Okay. I have chills as we wrap up 2025, that you're basically like, look at. Yes, it was a wild market. Look at how much we learned. That's just a great, that's so cool. Okay. Alright. I know we have to wrap, but I was going to ask you one more thing. So quickly here, if I put you on the spot, so if I warp you backwards, but with 2025 in mind, 2026 predictions in mind, you're suddenly back in a leadership role in the health care, private health care, do you immediately have one to three priorities you'd focus on right now?
Michael Schwarz (31:36):
For sure the patient experience, and that means a lot. It's a pretty loose definition, but everything from sophisticated and scalable scheduling to my time in the facility to billing to insurance, all of the things that go in to, really, a relationship that a provider will have. I think coming from that industry, the amount of compassion, empathy that most providers and caretakers have as far as taking care of you in the room and engaging, I think is just tremendous. They're some of the greatest employees and servants that we have out there, but I think there's a lot to be said around making sure that it's seamless, that there's access, it's not complicated. So I think there's a lot that we can do around, in that way. And that goes, like I said, patient making sure we have the right staff, making sure that there's capacity. If there's not being able to redirect everything that goes into great health care.
(32:33):
I think that's one. The other areas then become how do we efficiently run healthcare system? There's a lot going on right now from a reimbursement standpoint, from how health care is funded from an individual standpoint, what it means to companies, et cetera. And so make sure it's done efficiently. While there's been tremendous moves made in remote health care, self-diagnosis, other things, the amount of opportunity that we have in creating efficiencies inside the system I think is just tremendous. And making sure that folks are taken care of and we reduce readmissions and coming back and some of those other kind of components. So I think that whole journey, if I think back to the first parts of our conversation on market research and that journey and looking at attitudes and behaviors, so much of that transfers into health care. Your attitudes and behaviors from a health care perspective can be so different.
(33:28):
You can say, I want to eat better. I want to take care of my wound when I had surgery. But then the behavior of actually doing some of those things is different. And for each person, it might be a personalized experience. You might have different rewards, incentives, penalties, et cetera based on that. But making sure that we can educate patients, making sure that they have a good communication stream. If we're writing care plans and discharge plans, is it in the right language? Is it the right reading level? Is it the right brevity? These types of things that we can start to personalize and cater to our patient populations just help a better educated experience. So I think you asked for three, and maybe you have two with a little run on, but I think there's a lot we said on that side just around really the patient engagement and experience through the whole time.
Jess Carter (34:17):
I said one to three, so you knocked it out of the park, my friend. And, I don’t know, Schwarz, I am super grateful as a coworker because I do think that growth mindset, that perspective of when you go through hard stuff, let's go through it with resilience. Let's recognize not just to brace for impact, but to be open to what we have to figure out together and how to leverage it in the future. And so I think while you've provided incredible amounts of insights and opportunities for anyone who's listening to take forward and leverage something very practical, which I really appreciate, that's something we try to focus on here. It's not just chats, it's really helpful to leaders. I just want to make sure I'm really grateful that you apply that every day where I get to work. And so thanks for being on the episode. Thanks for joining me today. It's a gift. It's a gift to wrap up 2025 with you.
Michael Schwarz (35:06):
I love it. It's a gift for me as well. Thank you for having me. Really appreciate it.
Jess Carter (35:09):
Thank you for listening. I'm your host, Jess Carter. Don't forget to follow the Data-Driven Leadership wherever you get your podcast and rate and review, letting us know how these data topics are transforming your business. We can't wait for you to join us on the next episode.
Insights delivered to your inbox