Five Years In: Why Munger Moved Beyond Their Homegrown Lakehouse
Munger, Tolles & Olson was an early adopter of a cloud-based Azure lakehouse, building and operating its own platform long before most firms started their data journey. In this episode, Reanna Martinez shares what five years of real-world experience taught the firm about governance, security, access control, and the true cost of sustaining a homegrown data platform over time.
This is not a theoretical build versus buy conversation. Reanna walks through what worked, what scaled, and what ultimately drove the firm to step out of the DIY game. The inflection point was not technology, but resourcing. Maintaining and evolving a lakehouse requires sustained, specialized effort that eventually competes with higher-value strategic work.
If your firm is building its own platform, already living with one, or thinking about how to responsibly layer AI on top of existing infrastructure, this conversation offers rare, hard-earned perspective from someone who has lived the full journey.
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DescriptTranscription
ENTEGRATA | OVERRULED BY DATA | REANNA MARTINEZ
Episode Transcript
This has been generated by AI and optimized by a human.
[00:00:00] Reanna Martinez: The insights are incredible. What, what used to be just countless hours of human effort stitching data together and tracking down, you know, spreadsheets from this and that department. We don't do that anymore.
[00:00:19] Tom Baldwin: My name's Tom Baldwin. This is Overruled by Data, the podcast for law firms looking to start their data journey or accelerate the journey they're already on.
[00:00:27] Tom Baldwin: Brought to you by Entegrata. Today's guest sits at the center of one of the most important and most misunderstood conversations happening right now in large law firms across the globe. It's how to build a real data infrastructure that actually gets used. Reanna Martinez is the Director of Innovation, Systems and Data at Munger, Tolles & Olson, where she's been responsible for some of the firm's most forward-looking technology and data initiatives.
[00:00:54] Tom Baldwin: Munger was an early adopter of building a modern cloud-based data lakehouse on Azure, laying the foundation for work well ahead of where many firms are still today. Uh, Reanna brings a rare blend of deep technical fluency, practical operational thinking, and legal industry context. She's lived the reality of building data platforms within the firm, learning what works, what doesn't work, and what makes sense to evolve beyond a homegrown approach.
[00:01:22] Tom Baldwin: Today we'll talk about her personal journey with data, what Munger has learned from building its own Azure Synapse Lakehouse, and why the firm ultimately decided to pivot to a purpose-built data platform. Most importantly, we'll explore what other firms can learn from her journey. Reanna, welcome to Overruled By Data.
[00:01:40] Reanna Martinez: You happy to be here?
[00:01:42] Tom Baldwin: All right, so we always like to start these. Kind of just getting a little bit of background. So if folks aren't familiar with your journey and how you got to where you are, we always like to start with some basic stuff. So tell us about, you know, how you got to Munger, your background coming before, and you sit sort of at this interesting intersection I think a lot of folks are leaning into between innovation systems and data.
[00:02:02] Tom Baldwin: How did your career evolve to land you where you are today?
[00:02:06] Reanna Martinez: Well, I've always been fairly technical and in the early years of my career, I just kind of ended up in tech related roles, even if they weren't meant to be technical. When I was a work study at USC, I ended up building an entire ticketing system for one of this facilities and technology teams, and that was probably the first real database I I developed.
[00:02:29] Reanna Martinez: And I went on to build more data databases after college for the service industry and real estate and construction companies. I became a Salesforce architect and designed and implemented highly customized environments tailored to the unique ways that each business operated. In 2020 MTO posted their first systems architect role and a recruiter called me and said, you'd be great for this role and.
[00:02:56] Reanna Martinez: All the stars aligned and I was hired. The legal doesn't do technology for technology's sake. You need to be thoughtful and intentional and have a long-term vision of scalability. So what ultimately led me to my current role is that combination of deep technical work with strategic decision making, not only building processes and designing processes, but.
[00:03:23] Reanna Martinez: Clearly understanding the why we're building them and how they'll scale, and how systems and data can actually support the way the firm works. So sitting at the intersection of innovation systems and data feel like a very natural progression of that journey. It's still about solving very real world problems at the enterprise level.
[00:03:45] Reanna Martinez: With data as a foundational asset.
[00:03:48] Tom Baldwin: Again, we're gonna touch upon that foundational asset here in a second. How has working at Munger shaped how you think about experimentation and risk and thinking long term about platform decisions?
[00:04:00] Reanna Martinez: I am incredibly grateful for the level of trust and support the firm has given me to explore and experiment.
[00:04:07] Reanna Martinez: That kind of space is a luxury and not every organization can afford it or is willing to create. It has allowed me to grow in ways that I couldn't have elsewhere. Legal by nature is a highly governed environment. Everything is viewed through the lens of compliance and security and risk, which I hadn't experienced as a constraint on innovation.
[00:04:30] Reanna Martinez: It just means that we experiment intentionally and we have a clear definition of success. You have to ask harder questions, really dig into security and scalability. Ownership data stewardship, even disaster recovery, and this has helped me become more deliberate about long-term platform choices and vendor relationships.
[00:04:54] Reanna Martinez: In many ways, working at MTO has taught me that thoughtful constraint can actually lead to better innovation. It pushes you to build things that are resilient, not just clever. Yeah,
[00:05:06] Tom Baldwin: that's a great way to think about it. Do you have any specific examples where that thoughtful constraint, I love that phrase, has led you down a path that you otherwise wouldn't have gone?
[00:05:15] Reanna Martinez: I would have to put some thought into a particular use case, but constantly, you know, things are popping up in the industry and especially around ai, where, where tools are all around and we are being bombarded with, we want this, we want this, look at this. And. It has allowed us to be very specific and intentional.
[00:05:38] Reanna Martinez: We can look at a platform and very quickly d determine if we need to look any further or if it's just not gonna meet our security standards or it's not going to play well with the rest of our environment. So we have a, an a very robust vendor and platform analysis program, and it allows us to get through these.
[00:06:00] Reanna Martinez: You know, sometimes hundreds of requests very quickly and deliver a reason. Not just say, no, we don't want to, but no. And here are the reasons that are grounded in protecting the firm and being as efficient and flexible as we can internally.
[00:06:18] Tom Baldwin: So you get hired to the firm and it's your first law firm.
[00:06:22] Tom Baldwin: You're thinking, what did I just walk into? And there, there had to be this sort of. A moment when you're like, okay, you realized we, we need a centralized data platform and it's gotta do more than just deliver a dashboard. What was that? Was there a seminal moment where that triggered a thought in your mind?
[00:06:40] Reanna Martinez: I was hired to specifically address system and data silos. It, a shared data foundation didn't exist across the firm and d different groups were doing the best they could with what they had. Some people were still relying on spreadsheets and word documents for critical information, but there was no consistent structure or system of record.
[00:07:02] Reanna Martinez: So almost immediately I proposed a two part approach. First, we needed to formalize and standardize data at the point of entry, and that led us to implement Microsoft Dynamics to capture data and manage processes that didn't have a structured environment. And second. We needed a centralized data platform that could bring together data from multiple key systems so we could not only share critical data between systems, but also see across the firm in a meaningful way.
[00:07:35] Reanna Martinez: And, and once you see data as an enterprise asset rather than a departmental byproduct, the conversation shifts from what kind of reports do we need from to like what? Questions should the firm be able to answer today, tomorrow, in the future.
[00:07:55] Tom Baldwin: Amazing. And how did you help firm leadership understand the importance of investing in this centralized data platform?
[00:08:04] Reanna Martinez: One of my biggest advantages, uh, was I had an incredibly innovation forward CIO, and he was truly the champion who helped articulate the full vision and translate. What could have been seen as a technical investment into a strategic one for the firm, and we framed data infrastructure as foundational.
[00:08:27] Reanna Martinez: Similar to how firms think about document management or core human capital or financial systems. You don't invest in those because they're flashy. You invest in them because everything else depends on them working well. So we talked openly about the cost of not investing. When data lives in silos. The firm pays for it repeatedly through duplicated effort and slower decision making, inconsistent answers.
[00:08:56] Reanna Martinez: And there was an over-reliance on manual workarounds. So having an aligned leadership partner made all the difference in helping the leadership team see this project as a necessary step. Towards operating more intelligently and more efficiently in the future.
[00:09:13] Tom Baldwin: I love how you framed it alongside kind of platform decisions that you, the firm just needs to make, whether it's document management, HRS, it's just one of those necessary, um, platforms.
[00:09:24] Tom Baldwin: Not even application, but is the platform's something that the firm just needs to operate its business? I love that way to frame it. So the firm makes the decision, like, yes, we need to move forward with this Reanna, and. At that time there weren't a, there were a few options. I'd be curious to understand how you landed on building in Azure and what was the decision that drove you to build internally?
[00:09:46] Tom Baldwin: And you know what? I'd be curious for our listeners, what, what other systems you looked at and what team did you have to build that? Did you made decision back then to build something in-house?
[00:09:56] Reanna Martinez: We didn't really find an all inclusive purpose built option that met our needs from. Both functionality and, and governance standpoint, we weren't willing to sacrifice security and flexibility and how deeply we could understand and control what was happening with our data.
[00:10:16] Reanna Martinez: Um, security in particular was a major factor, even getting accrual to export employee and financial data out of their source systems required careful, uh, review and trust building. If we had asked leadership at that time to send that same highly confidential data to a third party, I doubt we would ever get off the ground.
[00:10:40] Reanna Martinez: So building internally gave us control over where the data lived and how it was accessed and how it was governed and how it was modeled. It, it allowed us to design the platform around our specific needs rather than forcing our needs to fit a product. So, you know, looking back. It was the right decision at that moment in time.
[00:11:01] Reanna Martinez: It let us build foundational capability and develop a deep understanding of our data and to mature our thinking about governance and architecture. So that experience, I think, ultimately positioned us to make much better decisions later and, and now about when and how it makes sense to evolve beyond our homegrown approach.
[00:11:23] Tom Baldwin: Yeah, could you talk a bit more about what, if you're able to, the platform you settled on internally?
[00:11:28] Reanna Martinez: Yeah. We have, in Azure Data Lake, we use Azure data pipelines to send our data down into downstream systems. So we are syncing employee, client, and matter data. This is data that used to be entered manually into other systems, and you can imagine all of the.
[00:11:45] Reanna Martinez: You know, fat Fingers and yep, errors that happen. We are using Power BI as our reporting structure and because we've also implemented Microsoft Dynamics, which lives in the Microsoft environments, they all play nicely together. So we can, with Power BI, combine all of those Dinka sources in really interesting ways.
[00:12:07] Tom Baldwin: And just getting started and doing something, I think is one of the most important things that we talk about on the show. Just like people get into this sort of paralysis by analysis, what worked well? And, you know, getting started and, and what lessons did you learn, and you kind of hinted at this, there's some things you know now that are gonna help you make that next leap, but what did you learn early on that you wouldn't have learned if you just sat down and like tried to do a three year data strategy or do a bunch of kind of data cleanup and looking at stuff and excel, like what did you learn that you wouldn't have learned otherwise?
[00:12:40] Tom Baldwin: Well.
[00:12:41] Reanna Martinez: Building ourselves. We had a very clear vision, at least in, in our early stages of what we wanted and how we wanted to get there. And we were extremely detailed in our mapping and our documentation because we had to be, you know, we didn't know where we were going. And that level of rigor created an enormous amount of institutional knowledge.
[00:13:05] Reanna Martinez: So in hindsight. That knowledge may be one of the most valuable outcomes of the entire effort. It, it gave us this deep shared understanding of our processes and our data dependencies. So even as tools evolve, that understanding carries forward and it informs better governance and system and process design and better conversations with leadership.
[00:13:30] Reanna Martinez: So while the technology itself was important, the real success. The homegrown approach was it forced us to build maturity in how we think about data as an enterprise asset and all the different facets of how that data impacts processes and departments and outcomes.
[00:13:49] Tom Baldwin: I love that. And how long for, for context, how long did it take from when you started to where you had something you could start showing people?
[00:13:56] Tom Baldwin: And how long generally have you had the platform in place?
[00:14:00] Reanna Martinez: We started our first. Discovery conversations in 2020. We actually started building, I would say maybe the beginning of 2021, and in about five months we had what I call phase one of our data lake, and that was our financial system and our employee HRIS system, and that was feeding downstream systems.
[00:14:28] Reanna Martinez: From that alone, we were able to develop some pretty. Powerful dashboards and reports that leadership had been wanting. So we've had the data lake now for a little over five years and it's going strong.
[00:14:42] Tom Baldwin: I say this to people all the time, I don't know that there's a firm that's been living with an Azure Lake house longer than you.
[00:14:49] Tom Baldwin: So you, you all have a lot of stick time with this technology and what's worked well and what doesn't. So now fast forward here you are five years later. What friction. Did you start to feel where you thought, Hey, we've taken this as far as we can, and now we need to reevaluate what was next?
[00:15:08] Reanna Martinez: I mean, we didn't experience friction from the technical or governance standpoint.
[00:15:14] Reanna Martinez: I think where we started to build friction is around resourcing. You know, building and maintaining and scaling a data platform like this requires sustained and specialized effort. It's not just about standing it up, it's about the monitoring optimization, that ongoing maintenance of it. We're a small team and for the data lake in particular, it's just me and the data scientists.
[00:15:39] Reanna Martinez: So over time, as our priorities and responsibilities have evolved, it's become clear that that level of attention internally competes with other high value work that, that we need to be doing.
[00:15:54] Tom Baldwin: I love that term, sustained and specialized effort. I think sometimes firms underestimate the amount of effort it takes to maintain it alongside the 10 other day jobs that you give a team like this to do.
[00:16:07] Tom Baldwin: When you started looking at options, what ultimately kind of differentiated the Entegrata solution from what you were doing in A DIY fashion?
[00:16:17] Reanna Martinez: You know, we've been. Having conversations with Utah for several years, we've been watching how the platform and the company has evolved. I think what ultimately differentiated Entegrata was your alignment with both the legal industry and how we want to operate long term.
[00:16:36] Reanna Martinez: The, that the platform is purpose built for law firms. Is a significant factor for leadership because you understand the security and compliance and confidentiality expectations that we operate under, and that understanding is reflected in how the platform is designed and governed. Another important differentiator is that the platform improves collectively as your client base grows.
[00:17:03] Reanna Martinez: So we benefit not just from what we build, but from what you learn and create across. Firms what's, what's working, what's changing where the industry is headed. So that gives us a valuable frame of reference and allows us to have more informed conversations about what's possible and what's next. I also appreciate the balance between support and autonomy.
[00:17:26] Reanna Martinez: So your team handles the heavy lifting around infrastructure and foundational maintenance, which frees us up to focus on insights and strategy. But at the same time, we retain administrative control and some flexibility to manage our environment and the outputs in the ways that fit our needs.
[00:17:47] Tom Baldwin: A lot of times we try and tease out some questions like this with folks that have been down this path, whether it's using Snowflake or Fabric or, or you know, kind of the Azure solution that you all built.
[00:17:57] Tom Baldwin: Knowing what you know now, if another firm came to you and said, Hey, Reanna, we're thinking about following in your footsteps and we want to build our own lakehouse from scratch, what would you want them to understand before they get they got started?
[00:18:08] Reanna Martinez: The perfect is the enemy of the good. Nothing is ever perfect, especially when it comes to data.
[00:18:15] Reanna Martinez: And if you wait until you have a flawless long-term strategy or a perfect perfectly curated data, you're likely never to start at all. Data strategy isn't something you fully design on a whiteboard, and it's something you refine through experience. So back to the previous theme, the fastest way to learn is to do the work, to roll up your sleeves and dive in.
[00:18:41] Reanna Martinez: And once you start building, everything starts to make sense. In context, do you see where your assumptions hold up and where they don't? And what truly matters versus what felt. Important in theory
[00:18:53] Tom Baldwin: that that's a, a, a, a hard line to tow because I think in a lot of firms, they, we are trained that perfection is the only outcome that's acceptable and to just say, jump in and get started as a hard leap.
[00:19:05] Tom Baldwin: I've learned it through lots of years of, of pain and suffering, but sometimes it's, that's a hard leap for firms to make. They feel like they need to have, they need to spend a year and a half doing a data strategy engagement, and they have to like, have everything mapped and perfect. We try really hard to convince them otherwise, but some firms just, they've gotta figure that out on their own.
[00:19:24] Tom Baldwin: Okay. So firms making a, you know, when, I wouldn't say pivot, but just an evolution in an the natural kind of ebb and flow of your data journey. A lot of firms have. Made investments whether even if may not even be in the cloud. A lot of firms have a very mature on-prem. Um, E-D-W-M-D-D. Lots of firms use different terminology.
[00:19:45] Tom Baldwin: It's basically an on-prem SQL environment. You all probably had something similar that you, you had little pockets of data for a firm that's in that state. What got activated? What got lit up once you got out of data silos and into a data platform like what you have in Azure?
[00:19:59] Reanna Martinez: Really it came down to operational efficiency.
[00:20:02] Reanna Martinez: So back to that. Entering the same data in multiple systems. We don't do that anymore. Our workflows are really well defined, and as we continue to reassess business processes, the foundational data behind that, we can leverage in different ways in super helpful. It's so valuable that we're not asking people to chase things down that we can, you know?
[00:20:29] Reanna Martinez: If you didn't have this data before, we can deliver it to you when it's right at your fingertips, when you need it, how you need it. So I think from an operational perspective, it has really helped to streamline and smooth out those processes. From a leadership perspective, the insights are incredible.
[00:20:48] Reanna Martinez: What What used to be just. Countless hours of human effort stitching data together and tracking down, you know, spreadsheets from this and that department. We don't do that anymore. We have an idea. It takes us a period of time, depending on the complexity. It could be a week, it could be a couple weeks to really refine the data points that we're looking at to validate it and make sure that this is accurate and and trustworthy.
[00:21:21] Reanna Martinez: Once we build it, it's there and you can come back to it whenever you need it. And we're moving on to the next best thing, the next improvement. So I think it's both from the professional level, operational side gets easier to the leadership level when they are extracting insights that they didn't have access to before.
[00:21:42] Tom Baldwin: That's amazing. Uh, and I think a lot of firms that go down this path have that same reaction, but until you see it yourself, it's hard to. Visualize the the are of the possible with a lot of these, these data platforms, it's hard to have any of these conversations without talking about ai. So as we look to the future, how do you see data platforms needing to evolve as AI becomes more embedded in the day-to-day workflow of lawyers and business professionals inside firms?
[00:22:09] Reanna Martinez: Yeah, it's hard to give a definitive answer on AI because everything is changing so quickly and what feels like best practice today might look totally different in a few months. But that said, I think there are some foundational, you know, fundamentals that I don't see changing, and data security and access control will remain absolutely critical as AI becomes more embedded in every solution.
[00:22:35] Reanna Martinez: As we introduce AI to more parts of the firm and processes, we're effectively extending access to data, and not just to people, but to systems acting on their behalf. So making sure that both users and AI tools only have access to what they're authorized to see is non-negotiable, and that means data platforms will need to be very intentional about how data is structured and governed and exposed.
[00:23:02] Reanna Martinez: AI is only responsible. That is the guardrails that you put around it,
[00:23:07] Tom Baldwin: right?
[00:23:08] Reanna Martinez: Without strong controls and clear permission in that risk profile increases dramatically.
[00:23:14] Tom Baldwin: We talk about this quite a bit, I think. It's something that if you're new to thinking about a, a data platform where you're meshing and blending data from many different systems, it's not top of mind and people make the mistake of not getting out in front of this access control issue when they're thinking about a data platform from day one.
[00:23:31] Tom Baldwin: We talk extensively about this as a big pothole that firms that don't think about until it's too late, right? How am I, how am I ingesting and honoring ethical walls? How am I ingesting and honoring access, native access control in the source systems you're drawing data from. And lastly, every firm, every firm manages by some level of exception reporting to say, Hey, Reanna's not a a partner, but she's the C.
[00:23:59] Tom Baldwin: You know, she's at a certain level where she should see access to certain things that her title doesn't reflect that she should have access to, but she has an override for that. Not building in a capability to manage all that and making it available to AI so that if you build a really cool, you know, most firms have some sort of chat bot.
[00:24:16] Tom Baldwin: Maybe I wanna point the chat bot at my data platform. But the, the data platform has comp data, it's got all kinds of things that shouldn't be available to everybody. So how are you securing that? Are you doing it kind of ad hoc? That's not very good. Am I making that same access control layer available to Power BI?
[00:24:31] Tom Baldwin: So those same reports I'm looking at dynamically change depending on who the person is that's looking at the data. Firms don't think about that. And you already know that because you've been dealing with this for five years. You've been living with it. It's a, it's a big problem. It, I'm talking about it a lot because it's top of mind for us as we help firms think about planning out their, their data journey.
[00:24:53] Tom Baldwin: What excites you most about where. MTO is headed now with its data and analytics capabilities and, and what are you looking forward to trying to sort of kick the tires on as you look the next 12 to 18 months out?
[00:25:04] Reanna Martinez: What excites me most is I genuinely feel like we've only scratched the surface. We've done a lot of the important foundational work, and that alone has created meaningful value.
[00:25:16] Reanna Martinez: But the real opportunity is, is what happens next. So as we continue to. Combine dataset, then look across them holistically. We start to see these stories emerging and patterns and relationships and insights that weren't visible when data lived in isolation, and that's when data moves from being informational to strategic.
[00:25:41] Reanna Martinez: I'm excited about the role that AI is gonna play with amplifying that value. I think the idea of chatting with your data is not just going to be a novel idea, but that's gonna become an expectation. It's not a replacement for judgment, but it's a way to service insights faster and identify trends earlier and help us ask better question of our data.
[00:26:06] Reanna Martinez: I think it's gonna be interesting.
[00:26:08] Tom Baldwin: It, it's interesting. We're starting to see more firms evaluate financial delivery of data in different ways where. You know, in the old days it was everything has to be an SSRS report, and then it was everything needs to be Power BI. And now there's this mode of thinking where it's like, well, most partners don't need 10 layers of drill down.
[00:26:30] Tom Baldwin: They have a very specific question in mind and they just want the answer. They don't want to have to remember what dashboard to go to or who to call In accounting, that chat with your data is starting to sort of bubble up. A part of, not a replacement of, but part of that sort of, you know, ethos of how we deliver financial or information generally to, to the lawyers and the firm.
[00:26:53] Tom Baldwin: How are you all thinking about that
[00:26:55] Reanna Martinez: exactly like that? We have made a concerted effort. What I recall, like the analog way of, of dashboards or reports, you know, sometimes, uh, in the early days, dashboards or reports themselves would be that drill down, like, here's a bunch of data on some filters, and do what you want.
[00:27:15] Reanna Martinez: And that is great for some circumstances. But I also come from a, a marketing background where a dashboard in. Five to seven KPIs and I just wanna see at a glance, and that gives me, is it green, is it red? You know, where do I need to focus my attention? I don't wanna have to do dig a lot, do a lot of digging to give me some quick answers.
[00:27:41] Reanna Martinez: So that, I think is is part one of the AI strategy at least. Drilling down into what are those important pieces and really honing in on that. And then AI on top of that allows you to really ask those informed questions and get more detail without having to do the digging and figuring out what data points you need.
[00:28:07] Reanna Martinez: The AI understands what you're asking, and it has all the access to the data. It can surface that information and bring you back a reasonable response. In the short term, I think that reasonable response is kind of a jumping off point where, you know,
[00:28:24] Tom Baldwin: okay,
[00:28:26] Reanna Martinez: this looks like a good answer. Let's go see if that makes sense.
[00:28:30] Reanna Martinez: As AI evolve, I think we'll be able to trust that answer a little bit more, a little bit more, but just that efficiency of. Getting that answer back. You know, imagine talking to your star, SME and just being able to throw questions over to them and they got that answer right there. That's your AI personal assistant.
[00:28:52] Tom Baldwin: Love it. Well, Reanna, we're at time. Thank you so much for sharing your journey and insights with us today. Um, that's it for this episode. If you all enjoyed the conversation, hit the subscribe button so you never miss another one. Thanks for listening, and we'll see you next time on Overruled By Data.
[00:29:09] Tom Baldwin: That's a wrap for this episode of Overruled By Data. If this podcast resonated with you, if you took one or two things away from it, you want to hear more from law firm leaders that have been there and done that hit the fall button.
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