Modern Analytics with Mike Palmer, CEO at Sigma Computing

action. They can do that live on warehouse data today. So we've had applications at this point built in literally the last months.

Richie Cotton

Welcome to data Framed. This is Ritchie in the last year. Generative AI for analytics has shifted from oh interesting demo to That actually works. This has huge implications for the analytics tech stack, for analytics workflows and for analytics careers. It also tantalizingly brings us closer to some longstanding dreams. Will we finally get self-service analytics? Will dashboards be replaced with something better?

Richie Cotton

Today's guest is ideally placed to help us navigate these new waters. Mike Palmer is CEO at next generation bi platform Sigma. He came to be AI from a background in data security, having been chief Product officer at Dhruva and Veritas. It's also had stints at Verizon and Seagate. Mike has a fascinating vision for the future of BI as AI powered analytics apps.

Richie Cotton

I'm keen to hear all about. Hi Mike, welcome to the show.

Mike Palmer

Thank you very much for having me. Glad to be here.

Richie Cotton

Yeah, great to have you. So, to begin that, let's talk about self-service analytics. It's been a dream that's been around for a few years now. How close are we?

Mike Palmer

Well, I mean, I think when we started on the path here at Sigma, one of the things that we tried to make happen was recognizing that the skill people had was spreadsheets, and that prior to spreadsheets, you had to write SQL. You know, obviously a more recently had to write Python. You had to know how to use complex tools.

Mike Palmer

And we figured if you could make a spreadsheet work, you could reach, you know, a billion people. Well, we're not quite to a billion people. We actually had great success there. And I would have told you we were in general, as an industry, I think, making good progress. I think in the advent of AI, we have accelerated that in a really sort of market way.

Mike Palmer

You know, at that, everyone, we thought everyone could use a spreadsheet, but we're pretty sure everyone can actually speak language. And that has really lowered the bar to complexity. So I would say that kind of the long standing goal of democratization, at least in terms of accessing data, is very attainable in the near future.

Richie Cotton

Okay. That's kind of cool. It's like you want to do something that is better than spreadsheets, but also has that same level of accessibility.

Mike Palmer

I think the two things work together, honestly. You know, I think for data discovery, asking a question, especially when you have a billion records sitting in the back end, I think it works as the best method. What I really want to tell an AI system to, you know, increment over a scenario model. I think that probably is still better done in, in a format like a spreadsheet.

Mike Palmer

So I think they'll work together.

Richie Cotton

Okay. All right, I see it. Do you want to talk me through the limits and, like. Like where does AI work and why doesn't it work right now? Like, how complex can you get?

Mike Palmer

That's a very broad question. You know, I think that, you know, one of the areas that AI and then those of us that use AI that we struggle with really are two things, you know, one is, you know, the very fancy words that we use around things being stochastic, a deterministic, and then we struggle with accuracy. You know, stochastic, of course, being things don't happen the same way every time versus enterprises love deterministic things when they they want to know that when they press A they get B.

Mike Palmer

And if you know, they should do it, a thousand times, it will always get B. When we think about structured data, we like right answers is the second area right? So if I'm building things using a model you tend to get random type of outcomes. Now the cost of going is very low. So you might be willing to suffer through that or so to speak.

Mike Palmer

But we have to get used to things not being exactly the same and behaving exactly the same every time we engage them. And we've seen in models that things that we do minute one, literally minutes later don't work. And on the other hand, you know, we go back to this idea of structured data where if I work for an investment firm and ask the question, you know, what's the revenue for Sigma?

Mike Palmer

There's really only one answer. And models don't do a great job of that either because you ask the question differently. It accesses the data differently. There are a lot of reasons why that it will come up with different answers. And so we're either back to providing models with deterministic things, in which case it's not really a model anymore.

Mike Palmer

Or we accept lower accuracy rates and answers. Or as Sigma is taking the approach both where we give customers the ability to ask great questions and add additional conversation and context, but then use tools that are a little bit more, if you will, traditional, so that they can go check those answers to the extent that they need to know them to % accuracy level.

Mike Palmer

So we're in this transition phase. I think there's some things that I does exceptionally well. Some things not so much.

Richie Cotton

Yeah, certainly. The idea of like something working. And then I guess it's like you show your boss and that's the point where he starts working, right?

Mike Palmer

Of course. Or the live demo. It's always in the live demo. Yeah.

Richie Cotton

Absolutely. So, I mean, this idea of, stochastic things, things being a bit random. This is something I think, data on this data scientist deal with a lot, but it's very new to software teams where that they're used to things being deterministic. Do you want to talk to like what the was the skill set that needed to be able to deal with these, stochastic processes?

Mike Palmer

This is great. Great question. You know, one of the fun parts, you know, working here at the company is just watching how different types of skills are coming into play and having huge importance inside the company as we run it. People that show or skills that I wouldn't even have thought of a year ago, you know, we we are able to do things because of AI that would have taken an enormous amount of human power in the past.

Mike Palmer

So think about going through, you know, thousands of transcripts every week to understand what what conversations are going on with customers, for example, or what sort of competitors are we facing? Those are the sort of questions we can ask on demand today because we've created product generated systems, that we've distributed to the entire company, that people can now ask some really important questions that were never even possible before.

Mike Palmer

And they were done by folks who don't have ten years of software development. Right. They are learning how to, they have a good enough knowledge of data. They're learning how to prompt engineer in the system, and they're creating very usable assets that are having significant impact. We had a very particular example of a thing that was, being done in our, you know, our stage two opportunities, right?

Mike Palmer

In sales. If you're in stage two, it's like the discovery call where if this one thing got mentioned, the conversion rate to booked one was % higher. That's not a marginal difference, right? If you were a salesperson and you realized that you could get one thing done in this call, that you would have a % higher chance of closing.

Mike Palmer

This opportunity is life changing, right? It's the sort of thing that you immediately start doing that all the time is the only thing that you focus on. But it was only discovered because of some, work that, someone on the team had done. Just kind of looking through trends, I guess, is the way that I robots put that and how people are engaging on these calls.

Mike Palmer

So massive impact. Completely different than a year ago, done at scale and done with folks that are in effect, training themselves on these systems on the fly.

Richie Cotton

Yeah, I love that. I mean, % sorry. Yeah. It's just a huge it's, that's the difference between, like, I'm, I'm getting five of the top performer and sales team, so. Yeah, I guess, yeah. Being able to, identify this is amazing, but it's not the sort of thing that sales teams would traditionally do, because going through call transcripts and then turning that into, some kind of data analysis, it's it was hard technical stuff.

Richie Cotton

All right. So, I felt like what the fund is, so, I mean, I guess traditionally, business intelligence, it's dashboards, it's spreadsheets, it's reports. You talking about the idea of, analytics apps? What does that mean in practice? Can you give me examples, some of these?

Mike Palmer

It's a good question, you know, and I don't very much talk about the AI anymore, and largely because as you just described it, I think the industry suffers from a bit of a almost year old definition of dashboarding, which was like a proxy for using data. And it was a proxy because giving users direct access to row level data was very difficult.

Mike Palmer

It was difficult to do because the infrastructure was difficult. If you remember the days of VPN ING into your system and getting, you know, networks to get to a certain types of databases. And oh, by the way, you really need to know SQL to do that. This is not something the average person could do. So they got a dashboard.

Mike Palmer

And most people do. They still think about reporting in that way. Unfortunately, the reality of the workflow though was they wanted to manipulate data so they would take this dashboard, they would download it into a spreadsheet, and this is where they would add their personal touch. Right. That was either I'm going to reformat the data, to try to view it in the way that I want to view it, you know, pivot table is a great example here.

Mike Palmer

But they would also add data. They would want to write their own scenario in, or they would want to reconcile the data in some way. And then it would create a completely different view than it was available on the dashboard. The dashboard of course, also at times obscuring like the interesting parts of the data in favor of showing you kind of aggregate trends.

Mike Palmer

But as I tell folks, is like looking at historical data and even forecasting data is not where productivity shows up. Predictive shows up when you act on it. And apps I apps for Sigma are now the ability for that same end user who, because of language and because of spreadsheets, can get direct direct access to billions of rows of data at like literally the row level, so they can do the same sort of discovery and analysis and come up with a perspective.

Mike Palmer

They can reconcile that perspective and they can say under this condition, having known all of this data, under this condition, I want this thing to happen. That was always something that would happen elsewhere, right? They would swivel chair into a different system and then they would take an action. They can do that live on warehouse data today. So we've had applications at this point, built in literally the last months.

Mike Palmer

Customers who are building inventory management applications, they're building HCM applications, they're building CRM applications, they're doing novel applications and investment management. Just amazing. Different workflows that range from standard departmental workflows all the way down to highly proprietary workflows that only their company does. But they're building it as business users, and they're taking super timely action on data. They know the best.

Mike Palmer

And so this idea, now that we've close the loop, we've looked at historical data, we've reconciled it and forecast it. And now under the condition we're acting on it, this is the full lifecycle of data. And so for us apps is the always been the part of the category that should have been there but wasn't possible until today.

Richie Cotton

And this is absolutely fascinating stuff. And it seems like, there are two kinds of of I think about, like, dashboards. You have like kind of disposable analyzes where it's like, okay, I'm doing one off thing for me, and then you're like, like I've cast, analyzing like podcast listening data or something, and you've got the permanent, this is like, company's revenue.

Richie Cotton

It's a very different beast. Why do these apps fit in, like, I could, if you're building it yourself. Is it like, is it supposed to be a disposable app, or is this like an app you trust with your top level company data?

Mike Palmer

And the answer is yes. Answer is yes. Okay. Yeah. And that's why. And that's what the beauty of this system is. So number one, the data that you're consuming is all in the warehouse. So from a security and governance and lineage and accuracy and reliability and trust ability or so to speak, this is enterprise data. So there is nothing, you know, that that you would consider like informal about it or inaccurate or unauthorized.

Mike Palmer

Right. This is the company data. But it does turn out that people write apps and do analysis on data across the spectrum of permanence. So you ask really interesting question. It's been a couple of months since I looked at this exact figure, and I always tend to try to be very accurate. So I always qualify here. But there was a couple months I looked at this and we had some hundred.

Mike Palmer

It was like applications at that time that had no activity on them. And I thought to myself, it your immediate reaction is, oh, that's terrible. You know, they build things and they're not using them and they must not getting value that. We looked into it and it was the exact opposite. The cost of writing the application was zero.

Mike Palmer

So the bar to wanting an application could also be just above zero. So they could write applications that, for example, only lived for four days. Think about going tactical example like they wanted to build a registration page and bring in, user demographics. And they want to connect that to other parts of their software stack, if you will.

Mike Palmer

But the event was going to happen and be done. They could do that and Sigma with no cost because they're building right on top of the live warehouse data. They don't have to migrate the data or do anything, exceptional to it. Their business users who now have UI tools and AI, that can help write the workflow and the utility of the one day event was sufficient to justify the work to put into it.

Mike Palmer

And then they just stopped using it. And so that anchors you on the one end. On the other end, we have customers, large like Blackstone, large asset managers that are doing very significant, financial workflows that include, closing their books that the public markets rely on from them. So you do have the whole spectrum of critical to company financial performance all the way to throwaway work.

Mike Palmer

That because it had zero cost, could now all of a sudden be made, in an automated way.

Richie Cotton

Okay. I mean, yeah, certainly. Think about, whenever a company has, like, a sale or maybe a marketing campaign, these things do tend to be by then last a week or two weeks or whatever. And you want to analyze the performance of that. You don't care. So there are a lot of these sort of short term, requirements like, needs for analysis.

Richie Cotton

Okay. All right. So I, there is a sort of hidden, thing at the start, we said, okay, this is all enterprise data. This is all governed. So it sounds like the governance, the hard bit, like, what do you need to put in place to make sure that you can build, analytics apps sensibly?

Mike Palmer

And this, by the way, is where, both by and just you know, data analytics products help us a ton because you still have to have great data models. You don't want people using data that you don't trust. You still want to have metrics. You want them calling on formulas and definitions of things. You mentioned, the revenue dashboard, right.

Mike Palmer

You don't want people making up the definition for revenue for your company. So you want to give them those assets. You don't want to have them made up on the fly. And I think those are super important things for it. Folks that really do care about lineage and telemetry. They want to know who's doing what. When did they change things, what data did they use?

Mike Palmer

These are all things that are built into the administration part of the system. You of course want those because actually the more you build on them, the more the risk increases. And if you don't understand what those underpinning assets are. So inheriting all of that governance and structure, I could keep going with audit, logging and all kinds of other tools that are necessary to make you if you want to democratize things, you have to have even a more solid underpinning of administration capability.

Mike Palmer

Those still have to be there. And the better you are at it, I think the more leverage you'll get creating you will create out of democratization. Because when the th person at your company writes a workflow, you'll feel good that that workflow was using data that you were confident in. You know who you didn't have to know them, but you can still see what they built via the telemetry.

Mike Palmer

You know who they've invited to that application, because you can also see that in the logging. You know, you can do all your work on the back end. Know that, you know, the company is still safe and operating efficiently. So we we tell folks it's not so much a matter of, the user interface or the whether you can build things, whether they're analytics or applications.

Mike Palmer

It is the, the moat, you know, for for many of either the software providers or the enterprises is really is in the foundational data modeling. The security, the telemetry, the permissions management, all of the things that provide the guardrails around the freedoms that the end users then get afterwards.

Richie Cotton

Okay. So that's interesting. So, in order to make sure that everyone can be kind of be free to create the the analytics applications they want, you need to make sure that you've got all the infrastructure in place to make sure they don't build stupid things or, they used the wrong kind of numbers, the wrong metrics.

Mike Palmer

This is no different than, you know, driving on the road, right? You know, we our cars could go anywhere, but we still create lanes. We have stoplights. You know, we want to organize everyone on the road to make sure that they get home safely. You know, and I think, you know, we still need that structure in data.

Richie Cotton

That's a great analogy. I to okay. So, you mentioned before that, a lot of, the motivation for this is dashboards tend to be pretty static for every single user. People want to personalize them. Can you give me some examples of like, how different users might want to, personalize, a dashboard or, or how they consume analytics?

Mike Palmer

I mean, I think the most, simple example and BI has always been things like filtering, you know, people don't want to see the, the data, the, the everybody data. They want to see it for my region. They want to see it for my store. They want to see it. They want certain sort of conditional formatting. They don't want their dashboards complicated with five other elements on it.

Mike Palmer

They just want to look at this one thing and then traditional BI, every time you made a change you made a copy. And this kind of this created massive sprawl. And the outcome for a lot of technical organizations was they're supporting a bunch of assets that they knew weren't really being used and were marginally different. It also created the need to service requests from all these people like, hey, can you modify this for me and then create this new copy?

Mike Palmer

The desires are still there. Everyone wants to personalize. They want to see things that are the easiest way for them to get their jobs done most effectively. So you can't stop that. In fact, you need to service it better over time. There basic things that you can do in Sigma, for example, to address this, you know, we allow, a feature that our customers love called bookmarking.

Mike Palmer

I can build them a central asset. It can have, we'll call it elements of things. And there there could be charts. There could be tables, there could be dynamic text, there could be all kinds of things. But in my job, it might say a person number two job. That person could say like, I don't even I only need three of those elements and then go into that workbook and delete them.

Mike Palmer

But they're not being deleted from the workbook, they're just being deleted from their view. And then they save that bookmark called number two's bookmark. And every time they log in, they will just see that. And in fact, they can share that with you. So you will also be able to see their their dashboard, even though the back end workbooks.

Mike Palmer

Exactly. The same workbook, it still has the elements in it. So this ability to create personalized views that create efficiencies, time efficiencies, because you don't have to recreate that view every time that you want to get to the thing that you wanted to understand. These are, again, the sort of things that modern products that are operating live on the warehouses.

Mike Palmer

These are the things that I think make a big difference for people in their day to day work.

Richie Cotton

This is interesting. So I guess, Yvonne said this correctly. You've got, an application which is for a sort of broad group of users, and then you have views which are going to be personalized for individuals. Just the level of personalization effect, like how big an audience you want. I think often dashboards are built with a sort of a very common audience in mind, a broad audience, because it's it's got to appeal to everyone because you only get one of them.

Richie Cotton

In this case, you've got two views, different people. Like, how do you think about how big the audience should be for a particular application?

Mike Palmer

That's a great question. And and it's almost you don't have to answer that question because the ads the answer is you can create an ad things for an an of one. And then and the actually idea here is that no one's creating things for anyone else anymore, right? I'm able to create this for myself, and I am the end of one.

Mike Palmer

I don't have to worry about whether the time that I spent is going to serve as other people, because as long as I think it's valuable for myself, I can put the time in to do it. So, you know, just like you would never think, how many searches can I afford to do on Google today? You know, the answer for Google is limitless.

Mike Palmer

You don't have to prioritize the number of searches. How many different views do I want to create? Is it up to me as an individual that would make it useful for me, and it's not pressuring the system any further on the back end. So it's not a noisy neighbor problem. It's what we tend to call this, where you have multiple people going after the same resources.

Mike Palmer

You don't have to worry about the time of an administrator who has to create an asset for you and has a long list of priorities. You just do these things for yourself. So I think architecture has alleviated some of the traditional questions about prioritization and resource bottlenecking, you know, simply by allocating the work to the to the person who actually wants it done.

Richie Cotton

Okay. So, build stuff yourself. And then if it's cheap to build stuff, you know, you build whatever, okay.

Mike Palmer

If the cost is zero, you'll build more.

Richie Cotton

That's that's very true. Yeah. Okay. So, what's the new workflow then? So suppose you start with, okay, I've got, I've got some business questions I want to answer. It's going to involve some numbers. How do you go about solving stuff.

Mike Palmer

It really depends on how how the beginning of that cycle is you could have a question in your mind but not know where the data is. You don't even know if we have the data. Right. So the first thing you're going to do is you're going to go into segment, you're going to ask a natural language question, and you're going to say, you know, how many people, you know, put bets on, my favorite, football team yesterday and it's going to return information about, not just an answer, but where it got the data from.

Mike Palmer

So now you're going to see, oh, wait a minute, there's data on the customers. We had that place bets. We have, data on the teams that were playing. We probably have some additional things. So. Okay, great. I've got an answer, but I probably have now or more questions based on the data that I've seen. So I'm going to drag that in and I'm going to say, well, you know what?

Mike Palmer

I'd really like to do a scenario based on next week's game. So who are they playing? I'm going to bring in some new data. I am going to build a scenario and I'm going to do like a forecast. I have a variable or maybe a set of variables. So I'm going to add that data. Sigma is unique and its ability to do right back to these systems.

Mike Palmer

So I don't just have to leverage the data that's there, I can add new data. I can even upload a spreadsheet of data that I have on my laptop that I created relative to, what I think goes on in these games. So I can upload that. I could join it to the table. This is called an info table.

Mike Palmer

I could join this to the existing enterprise data set. So I've written back like conditions. I've added new data, I've taken the existing data, I've created this amazing forecast. And I said you know what? I didn't do this for myself. I did this because I want our marketing person to know what might happen next week. So what I'm going to do now is I'm going to invite them into this workbook and they can collaborate in live edit with me.

Mike Palmer

And we do that together. And we agree under this conclusion for next week, if we have this number of people that are starting to log in to our system, let's say to place a bet that we want to notify our CFO. So we're going to put a conditional action on this. The CFO is going to get notified under this particular situation, and they're going to come back into the system after that notification.

Mike Palmer

They're going to add some information about the cost of marketing this for us. Now that the user base has gotten to be a bit bigger. And then we're going to say, okay, great. We've got new financing coming in. Our CFO understands what we're doing now. We want to place and we want to take, the users that we've seen in the system, and we want to now move them into our into our payment system because they're going to be net new users, and we want them to be able to transact.

Mike Palmer

So we're now going to make a, an API call or via an MCP, server on the other side. And we're going to say, hey, we're going to take this data and we're going to send it to that system with the instruction to add that to the new user table for the payment system. You could do all of that from one interface.

Richie Cotton

Nice. This is really like this. It's the full workflow from okay. What is my data through. Just like let's build stuff to having something that's actually, usable by the end users.

Mike Palmer

I didn't even give you the part of that where I call a or I over the top, warehouse and model services where it can recommend things to me. It can give me additional services while I'm doing my work.

Richie Cotton

All right. Nice. So this all sounds amazing, but I guess there's been challenges where, okay, I generative AI doesn't always work. It doesn't give you the right answer. And then you start with debugging stuff. What happens when I goes wrong.

Mike Palmer

It's a great question. And it's funny that we have this. Everyone uses this term human in the loop. And I don't use that term. I always say is I in the loop? Because I think human in the loop sort of presupposes that AI is doing most of the work, and that it's asking a human for small interventions.

Mike Palmer

I think where we still are today mostly is humans doing the work where AI is adding value at points in time, but we're still dependent on the human in almost the end through the entirety of the workflow. So we see our customers leveraging AI. You don't really face the question of AI failed and now we're in trouble because again, in the example I gave you, humans are calling services, along the way.

Mike Palmer

And if I'm calling a service, for example, to do analysis for me, and I can get that as an overlay and I can look at it and think, that's interesting. I'm not sure I believe that. Let me go test that myself. It's an asset to me, but it wasn't integral, you know, to be able to accomplish my output.

Mike Palmer

It's an assist in the case of building an application and sigma, which, you can leverage a front end interface for Sigma, you can say, build me an expense management application. It will go to the lab. It'll come back and say, I think an expense management application has the following components. I'm going to build this, and it's going to leverage tools that Sigma gives the model to on the fly.

Mike Palmer

Build that application. If it fails, no harm done. If it gets the job % done, you can finish % with tools. If it gets the job % done, you can use tools for the last %. So again, we're still in this world where we're getting a big accelerator from AI, but we're not giving it the nuclear launch code.

Mike Palmer

So we don't really see like too much of the I'm going to blow things up if I gets it wrong.

Richie Cotton

Okay. Yeah. I mean, certainly if, you're drawing charts or things like that, then it's usually you've got like a visual indicator of when things are going wrong. I guess it's when you have things like maybe a number is slightly off and it's not clear whether it's gone wrong. So the tools around like, to help you surface any potential issues, like how do you, uncover what might have gone wrong?

Mike Palmer

This is a great question. And, and the and even a bigger question is, how would you even know that things might not be right? Part of that is what we call just chain of thought. Like we want to understand when we get an answer from an AI system where it came from, we want to know what data set, was used.

Mike Palmer

We want to understand what filters or formulas that were applied, and then we want to pressure test that. So for example, in Sigma I can get that answer from the AI system. But I can also see the places it when the steps it took. And I can manipulate that data. So I can take a six step chain of thought.

Mike Palmer

And I can change any one of the steps and it will recalculate that number on the fly. So as a human, I can get to understand the the relationships of the decisions that it made relative to the outcome. And I think we're going to be in that, or at least to a certain degree, we're going to be in that I want to trust but verify.

Mike Palmer

You know what I'm getting from the system for quite some time.

Richie Cotton

Okay. So, yeah, certainly it seems like the chain of thought maps can be a big boost for explainability. Are there any kind of, like, testing tools or other things, ways of, like, evaluating quality?

Mike Palmer

I don't know that we have a one size fits all way of thinking about enterprise, way of testing quality to each and every user across each and every vertical. Not sure that's a problem we're tackling right now.

Richie Cotton

Okay. It's wondering whether like, is that an area of research or is it just like not a necessary thing for a dashboard for analytics applications?

Mike Palmer

It's a good question. You know, I think right now we're relying on the users test ability for for that sort of work. And of course we see plenty of observability tools just trying to understand the quality of the data over time. I don't know that, I see right now a need for that, partially because the folks that are engaged in the data in Sigma are the ones that know the data.

Mike Palmer

The best. And I think we've struggled for some time with centralized data teams having to serve data to users. And in a world where if I'm in marketing, I tend to understand my marketing data better than a data engineer does. And the more I can have control over that data, the better. Now, I may not understand our revenue data.

Mike Palmer

And by the way, in marketing, I may want to create metrics around marketing data for other people to consume, because I don't want them trying to create on the fly analysis of marketing data without some guardrails that I understand best. I do trust people. You know, I think that people engaged in data are going to create great outcomes.

Mike Palmer

I think we see that every day.

Richie Cotton

Okay. Yeah. Certainly it seems like, at least traditionally, I've always had to have like, the data skills and the tool skills. And then we also had to have the domain knowledge, and it's been quite rare for it to be one person with all three of these stats. Right. Okay. So actually it sounds like this is a very big change to, to workflows to like the the data analyst role itself.

Richie Cotton

So walk me through like how do you think, being a data analyst as a career is changing and how do you change your skillset to keep up I think.

Mike Palmer

Is great question. New data analysts are going to be more valuable in the future than even they are today. And they're already a, not only hugely valuable but, hugely constrained skill set and most enterprises, number one, if we just start with AI, it's because we're down to the same age old problem of garbage in, garbage out.

Mike Palmer

So we need to have highly centralized, good quality data. Data has to have great semantics. You want to have more metadata, not less, right? So we're still in a world where more data is better than less. And we're modeling that data for quality. And consistency is going to be super important because the more you get that, the more the system can train on the sort of data and the data outcomes that will create more reliable answers.

Mike Palmer

So fundamental to AI is a solid underpinning data strategy. I would say that there's a whole new area coming toward data engineers, though as I mentioned earlier, there are I think there's a revolution coming in software. I think that, if we take a little bit of a historical context and we moved compute and storage through this highly fungible cloud thing, and we've now consolidated a lot of what were formerly disparate databases in these cloud data warehouse platforms.

Mike Palmer

We are now going to see a dismantling of the silos of the software products that we then brought to cloud in the form of SAS. And as those highly specific, typically departmental software applications get dismantled, there's going to be a whole new skill and focus area for data teams to work with business users on, you know, which is, oh, wait a minute, I didn't need to buy an app to create that workflow.

Mike Palmer

I could I could create it here. So let's think about the underpinning schema that we want. What tables do we have available. We're going to create workflows that the software we purchased could never deliver because their schemas were fixed, but ours is not. So we can create workflows that formerly would have been or different applications, but now are just tables in the warehouse.

Mike Palmer

So we can work together on creating far more valuable, far more customized applications. We'll have to think about all the usual things. How do I make sure that those apps are available? How do I think about SDLC? How do I think about Permissioning? Now, these things are, you know, but now not just within the context of my analytics system.

Mike Palmer

I'm going to be applying all those concepts all the way through these thousands of applications that are being built. And I think that's going to really expand the, the, the remit, if you will, of the of the data team. And instead of being the team that looks at the output of applications dumped into the into the database, they're now going to be part of building those workflows.

Mike Palmer

That's what we see here at Sigma. We have ourselves on the order of applications that we've built in Sigma that run our daily activities from finance to sales operations to expense management and other things that, we've built all with our data teams and our departmental users. So we know this is possible. And the savings for these companies are off the charts.

Mike Palmer

You think about the millions of dollars that we're spending in what we call a system of records or, departmental applications that could be done for a fraction of the cost in platforms like Sigma. I think the people that can build those systems and understand, workflow without having to go to, buy something off the shelf, I think they're going to become hugely valuable in the future.

Richie Cotton

That's fascinating. There's a kind of key phrasing that you said dismantling all these SAS applications. That's right. So I think a lot of, well, probably almost every business now just built on top of, like, dozens and dozens of these SAS, applications. So which one do you think, like, organizations are going to be wanting to get rid of?

Richie Cotton

I'm sure we've got a few. Chief on to, and services listed. Go. Oh, we can save some money here. Yeah. Well, can you get rid of now, do you think?

Mike Palmer

Oh, I mean, the last meeting I was at even coming before the doors podcast was the progress we're making on our own, what we call our long range plan and our opex modeling and our FPA. So, you know, think about the Anna plans of the world. You know, no one loves that product. It's too hard to use.

Mike Palmer

It's expensive. Deployments often fail, and they're not really very customized to the finance flows that an individual company has. Those are the sort of products that are going to go away. Think about the highly focused applications that do things like sales forecasting, or they do things like territory management, or they do things like commissions planning. You know, if I'm a sales ops person, I have three, or licenses to do my job.

Mike Palmer

And if my job description requires five different software products, this is a terrible thing. It's complicated. It's expensive doing all of that in Sigma today. So those are the products. I do think that eventually we go after the, you know, the biggest of the big software applications, the ones that do things like, you know, human capital management, the ones that do the full, sales pipeline management.

Mike Palmer

We've already place a number of inventory management systems. This is a five year plus trajectory that we're on. But for the customers that I mentioned that are already built, these applications, they've already benefited from not buying third party software. They've already benefited from automating things that were too costly to get a developer to do for them inside their company.

Mike Palmer

And, you know, third party software is going to have a couple of renewal cycles ahead of it, but not without. But they're not going to have growth, you know, and I think that, AI is going to be a big fuel for this because the folks that understand the workflow are going to be very, participant, if you will, in the, in the ability to deploy these applications, which means that the risk and the accuracy, if you will, of what they get on and and the outcome as much as much better.

Richie Cotton

Well, I mean, that be a huge shift in terms of like the enterprise software stack. If all these kind of big name platforms are going away. Yeah. I mean, there's a ton of like, enterprise software that's clunky. You need to have like, people who is there. The whole job is just configuring it for your own, company. So yeah, if we can get rid of some of that and.

Mike Palmer

Customizing it. Etling. It's data applying security principles to it. I mean, the the cost of software is much higher than the even just the cost of the license of the software. It's the all of the other things that you have to do to make sure that that thing lives in your environment successfully. Those are all opportunity for cost savings.

Richie Cotton

Wow. Okay. All right. So, suppose I'm convinced for this. What's a good first analytics application to build? Like what? What's a simple hello world type thing?

Mike Palmer

Wow. That's a great question. You know, I think people only build things that they feel like, have the best ROI for their particular business, you know, and the first thing, you know, so what, in what vertical context am I living if I'm if I'm a retailer, the answer is almost always inventory. I'm going to build something that gives me a more accurate version of inventory.

Mike Palmer

I won't name this customer. We have a, particular customer. They've got chains of food, that they sell, and they ship, inventory to them basically on a daily basis. And they want to know things like what activity around this chain store is going to increase or decrease the foot traffic. I want to be able to pull in third party data sources to understand that they want to understand things like, I thought I shipped cases, but I only can see of them that were sold.

Mike Palmer

Where did the other five go? Oh, they were dropped on the floor during the day because of the activity level. I'm not able to track that data. I want a component of live data updates that come from the prior to the centralized system, so that I know I should ship more food to them the next day, because the last thing we want is for them to run out.

Mike Palmer

Those are the sort of applications that get people out of their chair because it's not, give me a better dashboard. It's like, Holy cow, this is what my business is. It is selling food. And my understanding of how much food I have is far less than it should be. And it could be much better. So I have less spoilage, I have less stock outs, I fewer stock outs like these are, you know, this changes the very bottom line performance.

Mike Palmer

So I abdicate to folks like, I don't think you should start with something that makes you sort of feel good. In terms of the I accomplished an app. I think you go right after the heart of where there will be the biggest ROI, because where there's big ROI, the momentum to keep building is going to be much higher.

Richie Cotton

Okay. Yeah. I love that just giving greater visibility into like, what your business problems are you trying to solve so you can make better decisions? Seems absolutely essential. Do you want to expand on that? Like, suppose you go all in on this, improving your analytics capabilities. Like what does success look like? Like how do you even measure it?

Mike Palmer

Great question. I think that have a very, very simple way of looking at what the what the future should be. I think that a few years from now, customers are going to have maybe % as much purchase software. I think they're going to have like % as much. What I refer to is proprietary software. I think they're going to be building at an increased rate for me.

Mike Palmer

What signal is going to look like three years from now is an AI apps platform that has a very solid underpinning in data access and governance so that as those workflows are built, they're built on, on solid understandings of accurate data. But I think success in the end should always be based on productivity and productivity. Either I made people more efficient or I reduced risk, I reduced cost, or I found a new way to engage with my customers.

Mike Palmer

Obviously, on the revenue side, that almost always happens through applications. I don't think the world is changed. I think in the end, software is still the means for most companies to grow more efficient over time. AI is going to be a big part of creating that software, and AI is going to be a big part of adding value in the workflow process, where AI understands patterns, for example, that a human might not easily pick up on at the moment, that they would most benefit from that data.

Mike Palmer

So I think success in the end is we've all built our own workflows ourselves, that we're able to do that for zero cost or near zero cost, and that the work that we're creating is highly customized to create competitive differentiation for what I do from what you do.

Richie Cotton

Okay. Yeah, I love the idea that like at the end of it, you go back to like top level business metrics, like you have you increased productivity, have you save some costs, have you increase your revenue of you improved your customer satisfaction, all that kind of stuff? That's right. It does feel like the buying software is the easy part.

Richie Cotton

Getting, people to change the processes. That's going to be tricky. Do you have any advice on, like, how do you go about changing, to new analytics workflows?

Mike Palmer

Great question. I do agree with you, by the way. I would say that like technology changes first, products change second, and people change third. And you have to have all three, right? Because I think we've seen a massive change in hardware which is driven has made AI possible. Right? I was oh, it has been around for many decades.

Mike Palmer

And the theories behind AI, but it's really in the hardware that we got performance building products that leverage AI effectively, I think is we're kind of in the early stages of that today, and therefore we're seeing a bunch of exploratory use cases. The obviously the best known right now are things like code generation. There are some others in things like call center, but everyone's trying, you know, and my suspicion is over the next couple of years, we'll start to see more of these moments where people think, this is not % better than what I'm doing.

Mike Palmer

It's times better than what I'm doing. And then you don't have to convince them because people recognize X improvements. If you try to put a % improvement in front of somebody, typically just the cost of changes to AI, like why, yeah, it's % better, but I have to learn something new. I have to work to bring this system in.

Mike Palmer

I'm not going to do that. Like, it's just not worth it for the %. So I don't know. It's so much, you know, recommending a great place to get started. As I come back to my other answer is like, where's the biggest difference going to be made? And and just do that. Don't waste your efforts. Don't waste your time for the just the sake of trying AI stuff.

Mike Palmer

Apply it to the thing that would have the biggest possible outcome for you, so that your efforts are going to pay off.

Richie Cotton

All of that is just thing, but like, what's a cool use case? Like what business problems are really trying to solve are my wasting time and then yeah, go for it. There. Okay. So, just to wrap up, I always want more people to follow. So whose work are you most interested in right now?

Mike Palmer

In the tech space. Yeah. Yeah, that's a really great question. You know, I if you're not fascinated by the model providers, then, you know, I think you're not paying attention. And I find it a very interesting industry because of the neck and neck race that we seem to constantly be running out there. You know, you've an open AI model update comes out, you have a Gemini model comes out like a and it's just advancing.

Mike Palmer

And I love that because they're they have to be so laser focused on what they're doing. It will extract the best out of them. I also think it means that those of us that are building on those products as platforms have a great opportunity to find differentiated value, to take them to to end users. I have to watch them the most, because it's those capabilities that give us capabilities that benefit our customers.

Richie Cotton

Yeah, definitely. Fierce competition is a wonderful thing, especially when it's like someone else's industry, not, you know, industry.

Mike Palmer

Well, trust me, we're all we're all competing every single day. Adam Smith would be very happy with us.

Richie Cotton

Wonderful. Yeah. Zillow have exciting stuff going on. So, anyway, thank you for your time, Mike.

Mike Palmer

It was an absolute pleasure. Thanks for the conversation.