Episode 55: Why Leaders Still Can't Get the Answers They Need

Host Richard Ellis interviews Michael Stahl, CEO and founder of Wombat Data, about why executives still struggle to get real answers from data despite modern dashboards, data lakes, and BI tools.

Stahl describes persistent issues across industries: siloed enterprise systems, closed or monetized APIs, difficult and costly integration, and the need to incorporate external datasets (e.g., CMS, demographic, geospatial) that are hard to organize and keep current. He explains the “streetlight effect,” where teams analyze only easily accessible data, and shares a healthcare example where layering drive-time and geospatial data helped explain patient no-shows.

Stahl says new AI capabilities can automate access and integration without migrations, keeping data live and enabling leaders to focus on better questions, a clear data strategy, and a few vital business signals rather than KPI proliferation.

Soundbites

• "Every executive has dashboards. Almost none have answers."
• "The parable is if somebody loses their keys, they actually look where the streetlight shines, because that's where the light is. But even if their keys may not be there, they look where they have the most ready access and visibility, even if that's not necessarily the best place to find good answers."
• "It's hard to know what you don't know."
• "You've got a very expensive Ferrari that's in the garage or just going to collect groceries, and we're not getting the highest and best use out of it."
• "Most people ready, fire, aim, and what you're asking them to do is ready, aim, fire."
• "We take the how off the table for you, and you can just focus on the why."
• "One data point may be interesting, but is oftentimes not useful. It's the collection of data points that actually helps us understand relationships."
• "What are the fewest variables that have the biggest impact and produce the clearest signal of success, health, and function in the team?"
• "KPI proliferation or dashboard proliferation actually creates more confusion than clarity."
• "If you dig and come to a point where your business, your team doesn't have the data it needs, it's probably out there, and people can help you find it, and you can access it."

Michael's business, Wombat Data: https://wombatdata.com
Richard’s business, Revenue Innovations: https://www.revenueinnovations.com 

Creators and Guests

Host

Richard Ellis

Richard is the co-founder and CEO of Revenue Innovations. With deep expertise in enterprise GTM, sales leadership, and organizational design, he has led revenue transformation engagements across technology, SaaS, and professional services firms.

Guest

Michael Stahl

CEO and Founder of Wombat Data

What is Some Goodness?

Some Goodness is hosted by Richard Ellis, a seasoned sales leader passionate about inviting top business minds to share their wisdom. Each episode is only 15-20 minutes, perfect for your commute or workout.

[00:00:00] Richard Ellis: Every executive has dashboards. Almost none have answers. When a hard question surfaces at four PM on Thursday, most leaders still call someone to pull a number, despite a million-dollar tech stack. Michael built Wombat because the gap between data access and actual insight is real and still unsolved.

[00:00:19] Richard Ellis: Welcome to Some Goodness, where we engage seasoned business leaders and experts to share practical guidance and tips to help new and future C-level leaders maximize their impact. My guest today is Michael Stahl, CEO and founder of Wombat Data. Michael has led commercial operations, served as chief integration officer at a legal services roll-up, and co-founded a healthcare company.

[00:00:42] Richard Ellis: Today, we're talking about the gap between data and wisdom and why so few organizations have cracked it Well, Michael, welcome.

[00:00:50] Michael Stahl: Yeah. Thank you. Thank you for having me. Excited to be on the podcast and to talk about this with you, Richard.

[00:00:56] Richard Ellis: Absolutely. Well, let's dive right in. I know you've been a leader at multiple [00:01:00] companies across industries, legal, engineering, healthcare.

[00:01:03] Richard Ellis: We're talking about data today, and I'd just like to start at a high level. When did you first notice that data problems still, in light of all the technological advances, still isn't being solved, and kind of what is your perspective on that?

[00:01:17] Michael Stahl: Yeah, I mean, I, I think I run into it every day, and as I started to move into the C-suite and participate in board meetings and higher level discussions and was responsible for finding the answers to some of those questions rather than being given a plan to execute on, I started to notice just how hard it was, one, to really be focused on asking really good questions that drove better performance, but then also once we had asked that and we felt like we had a good sense of what we needed to find, it was actually really hard to find, and even if we found the data, try to get it to the right place and analyze it well proved to be really difficult.

[00:01:55] Michael Stahl: And I think that's one of the things that stood out to me is that this was really [00:02:00] a persistent and recurring problem across verticals. It, it wasn't just the legal industry. It wasn't just the engineering industry. It wasn't just healthcare. It was everywhere, and I just saw it as everybody was struggling with fragmentation of data and enterprise applications and systems that were siloed.

[00:02:18] Richard Ellis: It's kind of surprising on the one hand because certainly repositories of data are everywhere, right? They, they live in underlying systems and ERP systems and data lakes, if you want to call them that. And then on top of that, you know, there's been lots of advances with BI tools, right? Are, are you finding it kind of...

[00:02:36] Richard Ellis: Is it surprising leaders and board members that, you know, getting the right answers from the data is still hard even today?

[00:02:43] Michael Stahl: A little bit I think it is, but I also think it's kind of how we've grown up. It's always been tough to get out of an application system, so whether you're in a CRM or an ERP, a lot of times that's where the data have been housed, and that's where the analytical tools are.

[00:02:58] Michael Stahl: Or maybe you were able to [00:03:00] put it somewhere else in a shared file in, in a data lake, but usually that took an internal team that had technical expertise about where to migrate it, how to connect it to those applications, but you're still really kind of siloed and trapped by the app, by the tools within that application.

[00:03:18] Michael Stahl: And what I started to find, even as I went to bigger companies with a lot of resources and the ability to build internal data teams and technology teams We still had trouble accessing external data sources that were really valuable to help us answer questions. So it wasn't just good enough to make sure our CRM and our ERP and our HRIS system connected.

[00:03:41] Michael Stahl: It was what about-- what did, what does CMS data actually tell us about our patient population? Or what do demographic data and, uh, geospatial data tell us about how our patients move through space and time to go to a clinic or maybe miss an appointment? And [00:04:00] all of that has a real impact on day-to-day operations and also revenue.

[00:04:04] Michael Stahl: And so even if people were solving the internal problems, and that was a big hill to climb and usually required a lot of money, we still weren't able to effectively solve accessing and integrating external data sources either. And I, I think it's just how a lot of data have either been trapped in systems that companies wanna create a moat around and monetize everything, or just the challenge of layering those different data sources, internal and external.

[00:04:31] Michael Stahl: So yes, a little bit surprising, but it's, in many ways, I think something that has been a struggle for many people, including my entire professional career. And it's really just now that we've started to find some technology that starts to open up possibilities and, and that's actually really the, the genesis behind my new chapter in life with Wombat.

[00:04:52] Richard Ellis: That's awesome. I'd love to dig into that here in a minute. But, you know, just kind of talking about this reminds me of my old value engineering days and many, many years [00:05:00] ago in supply chain management where, you know, you have these different data silos in your ERP system, your SCM system, your MES system, where we're finding that the same pencil was called five different things, right?

[00:05:12] Richard Ellis: And so there was a data translation problem that needed to be solved even back then. And so I resonate with kind of, you know, this geeking out on data for sure. Yeah. But I did wanna ask you, you know, you've written about something called the streetlight effect. And so tell me, I thought that was kind of a unique perspective and way to think about things.

[00:05:29] Richard Ellis: So tell us about the streetlight effect.

[00:05:31] Michael Stahl: Yeah. I think, so part of this is the benefit of working with some fantastic data scientists at Barge as well, and developers and, and we had similar experiences coming through different lenses. And really it's this phenomenon and a cognitive bias where, and the parable is if somebody loses their keys, they actually look where the streetlight shines- Because that's where the light is.

[00:05:53] Michael Stahl: But even if their keys may not be there, they look where they have the most ready access and visibility, even if that's not [00:06:00] necessarily the best place to find good answers or good information. And so what we experienced, how that translates into our professional day-to-day, is that where people have ready access to data is where they apply those business intelligence tools, is where they apply analysis, because that's where it's most easily-- their information is most easily obtained.

[00:06:20] Michael Stahl: Even if their external data sources, there may be unknown sources that people can go search for that actually will provide really insightful information and answer the questions more effectively that have a bigger impact on the business, it's tough for people to turn the lights on and go, go look other places, either because it's hard to know what you don't know.

[00:06:41] Michael Stahl: But a lot of times it's also even when they do, they wish they had that CMS data, or they wish they had that demographic data or drive time data. They don't really know how to get it, and so it's back to what can I do with what is most readily available to me and I can easily access, and that's how they try to solve [00:07:00] problems rather than continuing to push and dig at what's possible and, and what may be in the dark and, and looking for flashlights or the floodlights.

[00:07:09] Richard Ellis: I like that. I like that. So a little bit of cognitive bias there really impacting the way we try to get insights or, or answer questions. You have lots of great stories to tell. Is there, is there any just example from either the legal or the healthcare or some of your professions that, you know, kind of illustrate that problem and how it can be a big issue for businesses?

[00:07:29] Michael Stahl: Yeah, I mean, I think one of the problems that illustrates this pretty effectively is that we were trying to understand no-show rates in our healthcare company and why were certain patients, uh, not showing up and, and were there patterns and how could we address this more effectively. And we were really looking at what we had readily available to us, which was our ER...

[00:07:51] Michael Stahl: our, our EHR data, and then using that with modeling tools that we had that were more readily available. [00:08:00] And honestly, it wasn't until I was introduced to geospatial information systems and the possibility that I could layer other information and take where patients were in a zip code level, their no-show rates, but then start to take external information and run analysis and regression modeling to predict were there factors that otherwise weren't available to me in my internal systems that drove this outcome.

[00:08:29] Michael Stahl: And, and it was really fascinating to see and to understand that things like drive time, which once I saw it, it was like, of course. You know, you turn the lights on, of course that's where your keys were. You walk through that area. But until you shine the lights on that, it was hard for me to see it. But the ability to layer drive time data that's actually different than mileage.

[00:08:50] Michael Stahl: You know, we all live in cities, and just 'cause you live two miles from someplace doesn't mean it's actually quick to get to. And it was really important and insightful for me to [00:09:00] understand that these types of factors, one, were possible to integrate into and layer into analysis, and two, really drove me to ask deeper questions and continue to push at the variables and the levers that were gonna have the biggest impact on my business rather than just settling for what I had in my internal enterprise applications.

[00:09:19] Richard Ellis: Right. That's great. That... Well, that helps, you know, just kind of bring some clarity around connecting the dots between the streetlight effect and how we can maybe expose some of those risks or tendencies or biases in different businesses. Yeah. Well, let's talk a little bit about, okay, so how do we solve some of this, and why hasn't throwing a BI tool on top of your data lake solved it, and then where does AI fit in all of this?

[00:09:43] Richard Ellis: That's a lot we could get into, but let's start- Yeah ... moving to, you know, what is the answer and how do companies get it wrong, and how do they need to start thinking to get it right?

[00:09:51] Michael Stahl: Yeah. So I think there are a couple different ways that are pretty common, and I think at this point, even if you're not a technologist, people are familiar with ter- you know, terms [00:10:00] like APIs, application programming interfaces, and ways in which systems talk to each other.

[00:10:04] Michael Stahl: Not every system actually has open APIs that allow developers to connect those systems. There are some applications that keep them closed and try to monetize the data, which makes it hard for businesses to get the data out of. Even if you do, then you also have to find data sources, and this is back to the streetlight effect.

[00:10:23] Michael Stahl: I had to go for external data sources, and sometimes they're just downloadable from the web. CMS is a great example. That data is, you can download, but it's actually hard to organize, store, clean, keep refreshed. And so even when teams can connect data sources and connect applications, they run into barriers, particularly when it's not readily connected, like through an API, and then it's really expensive to have a team with the technical expertise to do that.

[00:10:50] Michael Stahl: So what we found was that, and what I found, was I either had to pay consultants and external teams a lot of money to do that, I had to invest a lot of money to build [00:11:00] internal teams to solve those problems, and I still ran into challenges like people love data lakes, but it's a great metaphor because if you're not constantly refreshing that data, the water turns stagnant, and then it's not very useful or very healthy for that business because you're making decisions on old data.

[00:11:14] Michael Stahl: And so one of the things that really has changed fundamentally in the last nine months is the advent of some of the AI tools, and with the combination of my co-founders happens to just deep expertise in data science and engineering, but also in development and use of AI, is the ability to automate some of these connections regardless of whether there's an API or not, regardless of whether data are internal or external We can leave data where they are without the need or the expense to migrate it somewhere else, and we can access it even when there aren't open APIs to allow teams to be able to analyze it in real time, have it live, current, so that they can actually ask and answer questions that matter most to them in their business.[00:12:00]

[00:12:00] Richard Ellis: Okay. That's interesting. Yeah, I, I hadn't thought about... Well, those of us who have been in the tech world, APIs and the lack thereof, we're familiar with the challenges, but the unique way that you're kind of coming about solving that without batch uploads and manual linkages that require big IT teams, that, that's really, really clever.

[00:12:19] Richard Ellis: What has been some of the organizational challenges around kind of implementing this solution and getting it right? Because it's a, it's, it's kind of a nuanced way of thinking about getting access to your data and then being able to leverage that data.

[00:12:33] Michael Stahl: Yeah. So I think it's, oftentimes it's the same problem people run into with other technology, which is human beings and getting them to expand their mind to what's possible.

[00:12:43] Michael Stahl: And I think usually people, there are assumptions and, and grooved kind of neural patterns or behaviors about what is or isn't possible in terms of data access, and then old ways of doing things. And I think what we have really tried to do is we don't have religion one [00:13:00] way or the other about how they wanna organize their data because we can really sit lightly on top of their tech stack and their data stack and help them without the need for these big migrations.

[00:13:10] Michael Stahl: So if somebody wants to invest in a data lake, we're happy to sit on top of that, and there may be other business purposes that have it or that call for that need, although we can help them avoid the massive investment in paying for data twice when they do it. But if th- if it's important to them, we're happy to work with them on that, or we're happy to work with them to, to help collect and integrate and analyze the data in their native application, structured or unstructured.

[00:13:35] Michael Stahl: I think it's just, it's really refreshing, and once people realize the possibilities, they start to let go of some of the old assumptions about, uh, what they have to do with their data. Like, "Oh, I have to clean it, structure it, tag it." It's like, nope, you can leave it where it is. It can be unstructured, and sometimes that's really useful.

[00:13:53] Michael Stahl: And we can work with you and your teams to really focus on the questions themselves. And what we like to say is [00:14:00] the data and the analytical tools we can keep in the background. So we take the how off the table for you, and you can just focus on the why. Like, why does this matter to me?

[00:14:08] Richard Ellis: That's really cool.

[00:14:09] Richard Ellis: Thinking about AI a little bit, one of the things, uh, we're helping teams kind of figure out how to get the best out of AI, and, you know, we've talked a lot about throwing AI on top of broken processes or ugly or inaccurate data is, is a recipe for disaster. But one of the things I have found that it's exposed is new opportunities to gain insight or knowledge, uh, or better decision-making out of data.

[00:14:35] Richard Ellis: But then you start using these AI tools with some, some interesting prompts and, and then suddenly you're, you're realizing, oh, well, I can't ask it that because I haven't fed it the data, or it doesn't have access to what it needs to give me the right answer. So then there's a drive and a need and incentive to get the data right.

[00:14:53] Richard Ellis: And so that's just kind of, you know, my experience. When, when you think about your solution here and bringing [00:15:00] AI into the mix, what are some top, you know, kind of synergistic opportunities or just opportunities for improvement that come to mind that we as leaders need to think about for our businesses?

[00:15:10] Michael Stahl: Yeah, that's a phenomenal question because I think most people ready, fire, aim, and what you're asking them to do is ready, aim, fire, and think about how they're gonna use a tool before they invest a lot of money in bringing it into their tech stack, which is critical 'cause there's a lot of overspending right now in signing license agreements before they're ready.

[00:15:29] Michael Stahl: And I think you hit the nail on the head. First and foremost, there has to be a thoughtful data strategy. We can help with that. That's one of the reasons we designed Wombat, is that we can help expedite and lower the cost for that data strategy. But there has to be thought of what data do I really need to answer the questions or to feed into the model so that it can help me analyze it, because those tools are only as good as the data they can access.

[00:15:55] Michael Stahl: And if we're old, if it's incomplete, then it's really not that [00:16:00] useful. You've got a very expensive Ferrari that's in the garage or just going to collect groceries, and we're not getting the fullest, the highest and best use out of it. So that you hit the nail on the head in terms of data strategy. And the second piece I, I actually think, and, and I know you and I have kinda hit it off on this idea before, which is what your firm does at Revenue Innovations, and that is working with leadership teams to ask better questions.

[00:16:25] Michael Stahl: Mm. And then make sure they ask thoughtful questions and think about how to transform that information. We, we talk about it at Wombat, transform information into wisdom. But it's great. Why does that data matter to me? What am I gonna do with it? How does it help me translate something important into an actionable plan my team can use and go execute on that I know is gonna drive the strategic goals a- and objectives, but my field team just needs to think about one or two things and go execute and do it really well every day And so I think those are the two [00:17:00] things that are most important.

[00:17:01] Michael Stahl: It's like, you know, if I think about having a really great tool and I got a great hammer, I gotta know why I'm swinging it. I've gotta g- have a good house plan and a design, and I better have good nails and things that I can use against it, so otherwise, you know, it's just gonna be useless. And so I think about those questions as kind of the, the building plans, and the good data as the nails, and, like, I'm gonna go, you know, use something and I have the raw materials to, to make y- you know, good use of it.

[00:17:27] Richard Ellis: That's great. I, I noticed you used a particular word in there I wanna go back to, and that is wisdom. A lot of times we'll talk about, you know, turning data into insights or actionable insights. Why did you land on the word wisdom, and is there, is there a deeper meaning behind that that you guys have found just in practice?

[00:17:43] Michael Stahl: Yeah. So I think that difference in terms of transformation of information, and I... You mentioned it earlier, we're drowning in information. We're drowning in dashboards. We're drowning in metrics and KPIs. I think the question that I always come back to, and, [00:18:00] and what I learned from what I consider some of my mentors and best leaders, is continuing to ask the question of, "Why do I care, and how can I use this information to make some meaning or drive results with my teams?"

[00:18:12] Michael Stahl: And to me, that's the difference between information and wisdom, is that transformation process and applying it into something useful, thoughtful, and meaningful for my teams and my business. There's also, for me, like, the name of Wombat, something fun is that, like, wombats actually dig and they tunnel, and so when we think about digging and tunneling through organizations' data, that's what we're doing.

[00:18:35] Michael Stahl: We're helping them access it and integrate it, like the network of tunnels that wombats build. More than one b- Wombat is actually called a wisdom, and it's the collective that really matters and builds that community, and it's, it's similar to data. One data point may be interesting, but is oftentimes not useful.

[00:18:52] Michael Stahl: It's the collection of data points that actually helps us understand relationships, and relationships of key variables, key [00:19:00] information sets, key people that drive results, and the analysis is revealing those patterns in the relationships. So, you know, it's a little bit of a play on words.

[00:19:10] Richard Ellis: That's really cool.

[00:19:10] Richard Ellis: Yeah. Yeah, that's really cool. I love that. That's so good. I love the history of that and the meaning behind it. I wanna ask you a, a bit of a personal question, and that is, if I got your history right, you, you've shared the leadership seat across, you know, different companies, and I think you, you've left a, a C-suite role to build this, right?

[00:19:26] Richard Ellis: And that's a significant bet. What was it just initially? I mean, are you just that fascinated about data, or is the problem that magnificent a need to be solved? I mean, what kinda spurred you to just kind of, "Okay, let's make the leap, and let's go make this happen"?

[00:19:40] Michael Stahl: Yeah, both. I think the la- This would be the most recently, the second company that I started, with Integrative Health Centers being the first.

[00:19:48] Michael Stahl: And in both cases, there was a real passion for the problem itself, and then also what I believed was a really big opportunity and need in the community. So yes, economic, [00:20:00] but more than anything, I think where I get most excited is where my passion actually feels like it can be useful and helpful for people.

[00:20:08] Michael Stahl: And when I'm sitting in executive team meetings and having these conversations and we're struggling with, I mean, some of the best and most expensive business intelligence solutions out there, and we have phenomenal tech teams and, you know, a, a big private equity sponsor, and we're still having trouble accessing and analyzing data that we can transform, or the data are old because of how they're connected or not connected.

[00:20:36] Michael Stahl: It's just, it's, it's back to that consistent and persistent pattern that I've seen and a big opportunity and And one that I, I just enjoy working with passionate teams and leaders who are trying to help their business, and if we have tools that can help enable them to do that, that's a, that's a really satisfying working relationship.

[00:20:56] Michael Stahl: If sort of leading with the e- the question of, how can I help? [00:21:00] And if people find it useful, then, you know, just delighted to be a part of that.

[00:21:04] Richard Ellis: That's great. Good answer. As we wrap up here, you know that we work a lot with go-to-market leaders, so marketing leaders, revenue leaders, customer success leaders, channel leaders.

[00:21:15] Richard Ellis: Any kind of wisdom you would leave with them, uh, whether they look into Wombat or not, as they just kinda think about how data impacts their particular function and their business as they lead into the future over the next few years?

[00:21:27] Michael Stahl: Yeah. I think the one piece, regardless of how they get there, is that kind of discipline and structure of just always asking the question, what are the vital signs of my business or of my team?

[00:21:41] Michael Stahl: What are the fewest variables that have the biggest impact and produce the clearest signal of success, health, and function in the team? And really trying to limit it to those, you know, three to five core vital signs, and then making sure that those feed down into and you can [00:22:00] measure them to understand how to track and then manage and coach the teams well.

[00:22:05] Michael Stahl: It's a process, and usually it takes a while on the front end, and we have people, and I, I mean this is-- I've gone through it too. I think I have a sense of it, and then when I continue to dig, like back to the Wombat, just digging and digging and digging and digging, it's usually a process and takes a while to actually come to that answer of, okay, these are the three or four.

[00:22:25] Michael Stahl: But I think that discipline and the process of getting to that, those core signals and vital signs, rather than just having KPI proliferation or dashboard proliferation, which actually creates more confusion than clarity, that'd be the one suggestion that I have. And then obviously, once you have a good sense of that, you gotta make sure that you have access to the data and that they remain current and live and you can measure it consistently.

[00:22:49] Richard Ellis: That's great, and as you're digging and digging, make sure you have a great flashlight and you're not just relying on your streetlight.

[00:22:55] Michael Stahl: Yeah, exactly. I think that's well, well said. I mean, I think don't [00:23:00] stop. Like, if you dig and come to a point where you, your business, your team doesn't have the data it needs, it's probably out there, and people can help you find it, and you can access it.

[00:23:11] Michael Stahl: Particularly in the last 12 months, the, what's possible now and today is vastly different than what was possible even 18 months ago.

[00:23:20] Richard Ellis: Good stuff. Well, unfortunately, we're out of time, so we have to wrap up. The title of our podcast, of course, is Some Goodness, so let's wrap up with some goodness outside of what we talked about today.

[00:23:29] Richard Ellis: So my final question for you is, what is something that's brought you a little goodness lately?

[00:23:34] Michael Stahl: You know, my first answer, and I always like to go with the first thing that hits my mind, 'cause, uh, it's usually the most authentic. Actually, sunshine. And I say that because I've had to remember it. I am in front of a computer all day, or I'm traveling, or in, you know, conference rooms, and I've had to remind myself, like, 10 minutes outside has been a phenomenal lift to my mood and just helps me feel more settled.

[00:23:59] Michael Stahl: [00:24:00] And yeah, I've just had to remember, get outside. Like, you know, the, the fluorescent lights of offices, whether at home or in a building, you know, sometimes can be draining, so getting outside has brought me a lot of goodness recently.

[00:24:10] Richard Ellis: I totally agree with that one. So everybody out there, take a break after this, step outside, get some vitamin D, a dopamine hit.

[00:24:17] Richard Ellis: Get some of that sunshine in your eyes. Well, thank you, Michael, for being part of the show today. A fascinating conversation, and, uh, really appreciate all your insights and wisdom.

[00:24:27] Michael Stahl: Thank you. Glad to be here. Appreciate it.

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