What's Up with Tech?
Tech Transformation with Evan Kirstel: A podcast exploring the latest trends and innovations in the tech industry, and how businesses can leverage them for growth, diving into the world of B2B, discussing strategies, trends, and sharing insights from industry leaders!
With over three decades in telecom and IT, I've mastered the art of transforming social media into a dynamic platform for audience engagement, community building, and establishing thought leadership. My approach isn't about personal brand promotion but about delivering educational and informative content to cultivate a sustainable, long-term business presence. I am the leading content creator in areas like Enterprise AI, UCaaS, CPaaS, CCaaS, Cloud, Telecom, 5G and more!
What's Up with Tech?
Why Your AI Initiatives Are Stuck at the Starting Line
Interested in being a guest? Email us at admin@evankirstel.com
Data without context is just noise. Despite billions invested in data infrastructure, most Fortune 1000 companies struggle to extract meaningful value from their investments. Why? They've built complex, fragmented ecosystems focused on tools rather than outcomes.
Srujan Akula, founder of The Modern Data Company, shares how his frustration with this industry-wide problem led to a revolutionary approach. After witnessing global enterprises repeatedly fail to achieve ROI from massive data investments, he and co-founder Animesh Kumar developed an operating system for data that converges the entire management stack into a simplified, business-centric ecosystem.
What makes their approach unique is how they flip traditional data management on its head. Instead of starting with source systems and figuring out what to do later, they begin with business intent and work backward. This right-to-left paradigm dramatically improves efficiency and accelerates time-to-value. One $30 billion distribution company transformed their customer marketing capabilities in just six weeks after spending years and $70-80 million without success.
The timing couldn't be more critical as organizations rush toward AI adoption. Akula reveals that 75-80% of enterprises are "stuck at the starting line" with AI initiatives because their data lacks the context and business meaning AI requires to be effective. By positioning their operating system as the "brain" that provides this context, The Modern Data Company helps businesses move beyond experiments to embedding AI into decision-making processes.
Perhaps most refreshing is their business approach - founded on humility, empathy, accountability, and transparency, they measure success by time-to-ROI and charge based on value created rather than seats or data volume. As Akula looks toward a future where agent technologies will transform enterprise efficiency, The Modern Data Company continues expanding with federal partnerships and an upcoming SaaS offering that promises to be "an AWS for data."
More at https://linktr.ee/EvanKirstel
Hey everybody, fascinating chat today as we talk about a company that is leveraging data to understand your business at Modern Srujan. How are you Doing well, evan? Good to see you, good to see you. Really fascinating mission and vision at Modern. Maybe start with the beginning your journey to creating the Modern Data Company and what's the big idea? What's the vision?
Speaker 2:Yeah, so I started this company about five and a half years ago with my co-founder, animesh Kumar, and in the last three companies we both worked at, I was the head of product, he was the head of engineering and we were building these large-scale data platforms for the ad tech and the marketing tech verticals and in that process had a good, unique vantage point of looking at how large organizations across the world are working with data, how frustrated they are with the lack of ROI and the unpredictability of the costs, and that we saw across the board in Europe, in the US, with all the mobile first large companies that started in the last 15 years. In Asia, most of them in Europe, in the US, with all the mobile first large companies that started in the last 15 years in Asia, most of them were our customers in the last companies. And that led us to.
Speaker 2:You know there was a lot of frustration amongst us too is you know how overcomplicated every enterprise seems to be making their data management stack.
Speaker 2:They're over-investing in a lot of tools and they kind of became a tool-centric ecosystem versus what it should really be a data-centric ecosystem, and that frustration led us to thinking about you know there should be a simpler way to solve this.
Speaker 2:You know, when the problem is so repeatable, when the outcomes which are a lack of ROI are so repeatable, there probably is a way to do this better, do this in a way that is a lot more customer centric, and that's what led to the vision of our operating system for data that converges like your entire data management stack, to simplify it, kind of like you know how Heroku's of the world, you know, abstract out all of the underlying complexity and give you simple ways to work with the technology.
Speaker 2:We did the same thing with data, with the operating system, and second thing we did is in the data world today, everything starts from the left Go to the source system, pipeline, centralize and then figure out what to do. We felt that is one of the big reasons why there is so much of complexity and a lack of ROI and over-processing of data. So we kind of flipped that paradigm to say let's start right to left. If we can come up with a construct that allows you to clearly capture the business intent and you understand everything you need to understand about your data ecosystem at the metadata level, you can be far more efficient in what is processed, what is stored, what is governed in what way, and that's sort of what led to us starting this company and the traction that we're seeing so far.
Speaker 1:Fantastic, and you talk a lot about streamlining operational workflows with technology. Can you maybe walk us through a real-world example of how you're making impact to customers?
Speaker 2:Yeah, absolutely so. One of the customers we work with. It's a distribution company $30 billion company that is family-owned, grew through a lot of acquisitions and when I first met that company, I had a meeting with seven SVPs across different business units and across about everyone is like see, I know what I want to do to embed data in my decisions to be much more efficient, but I'm always struggling with getting the data after spending like 70, 80 million dollars in the prior three to four years. So in that sort of an environment, we went in and said you know what? Let's try to show you an alternate way.
Speaker 2:So one of the big areas where they were struggling is how do I make all of this data work in my customer domain for my customer marketing? Being able to improve the order values, being able to have more personalized experiences on their web properties, was very hard for them to do, mainly because of the availability of the data. So within the first six weeks, the first thing we did is took their entire customer domain and mapped it into a customer 360 data product, which is essentially a logical representation of their entire customer domain map, along with the metrics, the measures, kpis, whatever you need for that domain and once we created that data product, that becomes a foundation for them to be able to power all of their business intelligence through tableau, power BI, etc. The same thing became the heart of their enterprise search part of the enterprise search. The same data product is powering the e-com personalization and in this process now the marketing team doesn't have to constantly figure out where is the data lying?
Speaker 2:Is this data accurate? Who should I go talk to? They have all of that right up front given to them and if a marketing manager wants to embed this into Salesforce Marketing Cloud, that is a few clicks. If you want to build apps on top, that's a few clicks. So from that kind of a problem statement they gave us when we started working with them to now they're able to build a data app to solve a business outcome within hours on top of this layer. So that's what gives me the joy is how the complexity of that kind of an ecosystem was completely abstracted and business is able to bring data and the output of their AI and embed them into their decision making, which is, I think, lacking a lot in the broader data space the last mile of embedding the data in your business decisions.
Speaker 1:Absolutely and working with different customers. What are some of the most common blind spots or mistakes that you see your customers making again and again when it comes to adopting digital tools and, you know, a digital transformation in general?
Speaker 2:Yeah. So one of the things I've seen and I talked about it a little bit up front is do you want to? I believe in convergence as a better approach to solving this complexity in data. So during the data, like the last decade, decade and a half, the modern data stack people are buying a bunch of tools, unifying them and constantly living in the maintenance cycles versus the value creation cycles.
Speaker 2:So the majority of the money mindshare resources go into managing. What I'm seeing with AI is again the same thing. On top of your golden data sets, on your data, it's a snowflake. If you want to now take that data, make it AI-ready, make it ready for LLMs to be able to work off of it in the enterprise context, you're again looking at 8 to 10 semantic capability, more flexible and dynamic governance, being able to have a data product lifecycle management.
Speaker 2:There's a lot more tooling that you need. So again, I believe that again it's going to lead to the same problems we saw in the last 10 to 15 years. And what Gartner recently started saying, interestingly, is for an AI-native stack, you have to think about this converged data management place, and what we did at the data activation layer is also converge eight to 10 capabilities, from semantics to the lifecycle management, into a single construct, mainly towards simplifying the life of the business that is using this tool, and have them focus more on outcomes. So that's sort of what I'm seeing right now, and we see a lot of tailwinds with that approach, especially when it comes to AI, because we can layer our technology on top of any data maturity in your data management stack and in six weeks, we are able to get our large enterprise clients to be AI-ready in terms of their data. So that's what's really resonating.
Speaker 1:That's a great mission, and so, when it comes to AI integration and Gen AI tools, I'm seeing the gamut of interest in the enterprise. Some are excited early adopters, some are skeptical, some are overwhelmed and really can't get started. But what's?
Speaker 2:the demand.
Speaker 1:You're seeing and experiencing firsthand.
Speaker 2:So we exclusively focus on the Fortune 1000 sort of a customer and we sell into the executives. So the CDOs or the CIOs, the CXOs, is who we sell to. So what we are seeing is pretty much every customer we speak with is in some shape or form experimenting or has experimented with a lot of the AI and the Gen AI tooling over the last, I'd say, 12 months. Everyone is stuck at the starting line A lot of experiments, a lot of gains in individual pockets in your enterprise. But now how do you actually level it up to start automating some basic workflows? How do you start embedding that more into your business decisioning? Are making some basic workflows? How do you start embedding that more into your business decisioning? And the scale is where everyone is or not everyone, but I'd say about 75 to 80% of the customers we speak with they're stuck at that starting line.
Speaker 2:The biggest problem is the readiness of the data, Because AI doesn't look at tables, columns and values. Ai needs context. Ai needs the business intent, business meaning of your data, which is a lot more to be done to your raw data to make it AI ready. And I think that's where a lot of our customers seem to be stuck? How do I figure out the readiness, how do I figure out security, governance, compliance at scale and how do I manage the lifecycle of all of these things in this new environment? And that's where we come in. It's to say, we don't worry about all of that. Run the infrastructure that you run and the way we position that is. Think about your existing data management stack as a body that's lacking the brains to be more efficient and think of the OS on top as the brains. That has the business context and the business intent so it can orchestrate all of your tools in a far more efficient manner towards your AI readiness, both from a cost and time to value. So that's sort of where we play.
Speaker 1:Fantastic. So there's so many tools on the market. Customers, enterprise, fortune 1000 are drowning in options. Many are sort of locked into one or two big tech roadmaps, I don't know, like a Microsoft shop or a Salesforce shop. I mean, how do you help clients choose the right tech stack?
Speaker 2:Yes, great question. So one of the things we do at the OS layer, sitting on top of your infra, is we can kind of avoid that ecosystem compute lock-in that you might be seeing with the large platforms that you adopt, because you know we can, being the orchestration layer on top of your infrastructure, depending on the workload that you're trying to run, depending on you know the need from the business, we can be more efficient in saying you know these kind of workloads need to run on a Databricks engine. These kind of marketing analytics might be better suited for Snowflake or some ad hoc stuff on DuckDB. That is something that we are really focusing on is giving the customer that flexibility. The reason is how nascent this whole market is when it comes to AI. It's going to significantly develop over the last two to three years.
Speaker 2:We don't want our customers to make that lock-in choice this early, but provide them ways to be able to experiment with tools that they think would be relevant. Like I'm already seeing multiple businesses in a specific customer environment say, we want to use different foundational models for what we're trying to do. So our approach always has been give the customer that flexibility to say bring on whatever tooling you want to, especially when the broader market is so early and I think it's risky to just say I'm going to be a one ecosystem shop. You need that flexibility and that comes from open data formats. Having these kind of capabilities that don't lock you into computes, give you, like open APIs, mcp sort of support so you can start bringing in third party tools and experiment very rapidly.
Speaker 1:Fantastic. So I assume you serve multiple verticals and while there are commonalities, there are very unique requirements in each of those healthcare versus a retail discussion I had the other day. Manufacturing, I mean, how do you look at the challenges across industries for digital adoption?
Speaker 2:Yeah, so what's been working for us? A good question, evan, is because we have such a horizontal play, you know in terms of, you know what we do and we can easily bring the vertical models in terms of data products on top. We are seeing the traction across multiple verticals, but for us, we are right now focusing on the banking, financial services as one of the verticals, industrial and manufacturing and retail, cpg as the three key verticals. We are starting to see a lot of inbounds coming from a federal side, which is interesting, and that's something we might get into a lot more next year in terms of the focus on the verticals.
Speaker 2:But the common point where all of these customers are looking at us is how do I get myself to be AI ready?
Speaker 2:I have such a fragmented ecosystem, so many data silos and the kind of size of customers we work with.
Speaker 2:They often are on multiple clouds in multiple countries with different data localization needs.
Speaker 2:So that is the challenge for them and in that sort of a fragmented, ungoverned ecosystem, how do I get myself to be ai ready so that I can start experimenting with conversational interfaces, you know, to actually improve my business?
Speaker 2:So one of the largest device manufacturers that we recently signed as a customer, was looking at a one and a half to two year roadmap to provide a conversational interface for the customer support to be able to search all of your device telemetry to better support you. That kind of a capability we were able to accelerate literally in two weeks, six weeks. So that speed, so whenever you have that air readiness problem, is something that we really shine. In addition to that, when you're really trying to solve some business critical problems and you have failed or you're stuck in the ways that you've done already, in that sort of an environment when we go in we more often than not are able to show them this alternative outcome, first way, the data OS way of solving that problem and that also significantly resonates with our customers. So business critical problems plus AI readiness are the two key drivers of customer interest right now.
Speaker 1:Wonderful. So let's talk culture, business culture. As the old saying goes, culture eats strategy for business. I mean, how do you help your customers? Not just deploy tech but, you know, embedded into the way they're working and their sort of cultural mindset?
Speaker 2:So I'll take a step back Before I do that. When I started the company me and my co-founder the first thing we spoke about is what are the core values we want in this company? What do we want our employees to feel like when they work here? So we came up with humility, empathy, accountability and transparency as the four key pillars of our culture and values, and we've been very strong in ensuring that that culture prevails as we are starting to grow. Testament to that very little churn, like less than 5% churn for a company that has a lot of engineering in India, which is this hottest skill. So that's just one thing that we do and the same approach we take to the customer as well. We are very empathetic towards the customer. It's always customer first.
Speaker 2:Internally, we have this metric called time to ROI. How quickly am I delivering ROI to our customers is something that we really look at and broadly in data, one of the things I see is hey, I'll provide the license and then you figure out what to do with an SI or you hire your team. We actually want to solve the problems for the customer. We are not just trying to make a pipeline faster or an inference better. Our mission is all of these investments you made have to work at the edge in actually your business decisions. So that's where most of our focus is, and whatever it takes for us to ensure that the customer is seeing the ROI is very important to me and that's sort of been our ethos.
Speaker 2:In fact, one of the customers that signed up with us we signed a multi-year license their internal teams were not ready to give us access to the environments and the data. It took us about three to four months. I told them don't pay us until you guys are ready. So that's sort of our approach. Why are you paying me when you are not ready? I'm more than happy to wait Because I don't want to get paid when I'm not showing value.
Speaker 2:So that approach has been really, really helpful in being customer-centric. It flows down not just to this way of engaging or the values that we create internally in how we operate as a company, how we are working with customers, our business model too. Unlike any other data company in the market, we never penalize any customer on the number of seats or the data sources or the amount of data you process. We always charge them based on the number of seats or the data sources or the amount of data you process. We always charge them based on the value created. It's always based on the number of data products you are running in production towards your use cases is what we charge our customers on, and we also give them tools to manage that data product lifecycle so you're not overspending. I strongly believe that as you start providing, if the customers are happy with the ROI, they'll stick with you and we are seeing the constant expansion happening in most of the accounts we work with Wonderful. That's a win-win.
Speaker 1:So you're a trendsetter, but obviously you're also tracking trends in cloud data automation. What are the biggest trends that will define the industry for the next year or two?
Speaker 2:I think the rise of agents and agent tech workflows I think is going to be big. I see this as again, I'm being a little dramatic but pre-internet to post-internet or pre-mobile to post-mobile how all enterprises transform themselves. I see we are on the cusp of that kind of a transformation towards running your enterprises far more efficiently than what you're doing Right now. We are experimenting with maybe taking small workflows and starting to automate them. But as we mature the agent tech capabilities, mature the MCP protocols and the things you need to make these agents work, I think you'll see a lot of significant improvements in efficiencies. Internally, we want the entire data I wouldn't say entire, but about 80-85% of data management to be agentified. There is still always going to be 15-20% human in the loop, necessary especially when it comes to data, but that's where I see the market. That's what I'm most excited about is the agentic future.
Speaker 1:Yes, I would agree. We could do a whole two-hour discussion on that alone. What's next for the modern data company over the next few months? Any new services or partnerships, events, innovations on the horizon that you're excited about?
Speaker 2:We are excited about a couple of, like I said, strong interests coming from the federal side. So we are starting to line up some really strong partnerships, which we'll announce probably in Q4, to really start accelerating our federal aspect. Currently, we deploy our solution as a PaaS platform, as a service in the customer's cloud, and we are actively working towards launching a SaaS version as well, which we hope will be a Q1 release. It's like a true data cloud, the way we think about our SaaS, like an AWS for data.
Speaker 1:Fantastic. Well, lots to look forward to. Thanks so much for joining, absolutely. And appreciate the insights and everyone. Check out tmdcio, the modern data company, for more and also check out my new TV show now on Fox Business and Bloomberg at techimpacttv. Thanks Rudan, thanks everyone, thanks Evan.