Somesh Saxena Podcast Transcript

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Headshot of CEO Somesh Saxena podcast transcript

Somesh Saxena Podcast Transcript

Somesh Saxena joins host Brian Thomas on The Digital Executive Podcast.

 Welcome to Coruzant Technologies, home of the Digital Executive Podcast.

Brian Thomas: Welcome to the Digital Executive. Today’s guest is Somesh Saxena. Somesh Saxena is the CEO and founder of Pantomath, a next generation data observability and traceability platform for automating data operations and improving data reliability. Prior to founding Pantomath, Somesh served as the head of data and analytics at GE Aerospace, where he led a hundred person organization supporting 18, 000 data customers.

He witnessed his team’s stakeholders and organization struggle with data reliability issues and set out to build a first of its kind data observability solution to help organizations drive a data driven culture. Pantomath is trusted by PayCorps, WEX, G& J, Pepsi, EW Scripts, and several large Fortune 500 customers to enable AI and data analytics initiatives.

Well, good afternoon, Somesh. Welcome to the show.

Somesh Saxena: Thanks for having me.

Brian Thomas: Absolutely. Love doing this. I appreciate you making the time hailing out of the great city of Cincinnati this evening, as we traverse the North American continent just an hour apart. But I, again, I appreciate you making the time. This is so exciting.

So, Somesh, I’m going to jump right into your first question. We want to hear your story. Your career began in the culinary world, including time at Gordon Ramsey’s New York restaurant. How did this experience influence your transition into data science and eventually lead to founding Pantomath?

Somesh Saxena: Yeah, Brian, that’s a great question.

I moved here from India when I was 17 years old, grew up in Boneres in Mumbai, India, and moved here to become a chef, went to Le Cordon Bleu, it’s a French culinary school, and worked at quite a few fine dining restaurants, including Gordon Ramsay’s Michelin star restaurant, as you, as you mentioned. That experience working in fine dining restaurants and really, you know, fast paced, high pressure environments positioned me extremely well for The next phase of my life, which is what I’m in right now, right?

I transitioned from being a chef into I. T. and the data space. I went back to school, went through University of Cincinnati and got a bachelor’s in information systems that led me to my role at General Electric, where I eventually Came up through their it leadership program and, and led their data organization.

My last role with the company was where I was the head of data analytics, leading a hundred plus person team, supporting 18, 000 data consumers for the company, which eventually led me to pantomime where I’m the CEO and founder of this amazing tech startup. That’s doing really, really well. And in looking back at that experience, you know, I learned grit and resiliency like, like never before, because it’s a, it’s a tough world. It’s a completely different world than IT and data. And especially working at Gordon Ramsey’s, if you’re familiar with his TV shows, you know, his restaurants are exactly that or worse. Honestly, it’s, it’s pretty tough environment.

So looking back, it definitely prepared me for. The entrepreneurship journey I’m on right now.

Brian Thomas: That’s amazing. And it takes a lot. And I think that’s kind of helped contributed to your success as an entrepreneur, but also switching career paths into technology from culinary. But a lot of pressure, I would say Gordon Ramsey school or show or his restaurants is at the highest caliber.

And I’m sure there’s a lot of stress and pressure to do the right thing and be the best in the world. So I do appreciate that. Next question for you. I have Somesh is how are you and pantomath innovating around the data pipeline, observability and traceability space. And what future advancements do you foresee in this area?

Somesh Saxena: Yeah, Brian, we’re actually in the process of building some amazing features on top of the existing foundation we have in terms of technology and that foundation It’s pretty unique and innovative in itself, right? Nothing like it in the world exists out there. So what we do is we snap into customers enterprise data environment.

These are large enterprises and fortune 500s that have a pretty complex and disparate data ecosystem with several different tools and technologies that move, stitch, cleanse, transform, visualize data. And we snap in through pantomath. It’s an enterprise software solution that Automatically maps out their entire world for them, it snaps in and draws out every single cross platform and intersystem data pipeline for them, explaining to them exactly how their data is flowing from one hop to the other, and then in an automated way, also applies monitoring to that.

And so the observability and traceability elements, if we break them down, Step one is traceability to auto discover every single data pipeline without any manual inputs from the customer. Again, nothing like in the world exists out there, very unique tech to do that. And then applying monitoring observability to that is in real time monitoring every single data pipeline.

End to end observability of the entire data pipeline power data products and data reports, because the challenge we face today without a technology like this is not understanding when things break, not understanding where they’re broken or even why they’re broken. And so that causes companies to lose productivity.

Causes data downtime, causes data reliability issues and even lack of trust in data as an organization with sometimes poor decision making on bad data. And so this unique tech, this innovative tech didn’t exist until pantomath built it. And for me personally, you know, the idea came from the challenges I lived with in my past life.

So going back to my GE experience, we didn’t have a pantomath there. And so I saw my technical teams, business stakeholders, executives all struggle with. Data reliability challenges, data quality and operational issues that led to starting pantomath and definitely an uphill battle in the early days, trying to figure out how we, how we crack this solution and build technology that again has never been built before.

But fortunately it worked out really well with an amazing team. And here we are, it’s everything I’m describing to you is already built. And on top of that, there’s a whole lot more we’re doing when it comes to innovation with AI and so on as well.

Brian Thomas: Thank you. I appreciate that. You’re right. And we talked about this a lot on the podcast, how there is usually a problem or some sort of experiences happens negatively to the customer or to the entrepreneur, and they find a solution to fix this problem that is plaguing hundreds or hundreds of thousands of people.

And I appreciate your thoughts and insights around what you’ve done to bring some clarity to this data pipeline observability. Obviously, having insights into all that. Data and those connecting points are so important. So I appreciate you sharing that. Somesh, what strategies do you recommend for organizations aiming to cultivate a data driven culture and how does pantomath support this transformation?

Somesh Saxena: Oh yeah. There was quite a bit there, right? Uh, companies have to focus on everything from people, process and technology. There’s quite a few elements in being a data driven culture, but the piece that I see companies. Invest in is mostly the technology piece, which is, Hey, we’re moving from our on premise data environment, the cloud, we’re going through a digital cultural transformation.

They also don’t shy away from investing in building amazing data products, building reports that finance reports, marketing reports, or any other business unit and domain that needs to be data driven. So I don’t think companies shy away from an investment in data. The one piece that still blocks them from having a data driven culture, I feel, is, is lack of trust in data.

And that’s what I have seen, not just in my past life, but even in the hundreds of companies I’ve talked to at this point, uh, quite a few of them were partnering with and helping them move the needle for their data driven culture. But The problem Brian is that you can invest millions and millions of dollars in modernizing the data stack, having amazing technologies to house and store and visualize your data and build great analytics and, you know, models and reports, folks want to use any of that if they don’t trust the data and they won’t trust the data.

Once they’ve had a bad experience with it, right, if you go in front of your boss or a leader and are putting your neck on the line to say, here’s the data, here’s what I’m presenting to you to make a decision off of it, and the data is long, that’s unfortunately going to leave a sour taste in everyone’s mouth.

And so the piece that’s, I think, important to focus on for organizations that are trying to be data driven is about trust in data and data transparency culture. Part of that is a technology piece, which hopefully a technology like pantomath can help with to surface up exactly what’s broken, where and why.

So the very least you’re aware and not making a poor decision with bad data through real time observability that a technology like pantomath could offer, of course, also hopefully help with resolving the issue pretty quickly, reducing data downtime. But then there’s a piece that isn’t technology focused.

It comes down to the culture as well around being able to raise your hand and say, you know, I don’t trust this report. I don’t understand this data. And that’s where data literacy programs, data governance programs are very important as well. I think it all goes hand in hand, right? Being able to trust your data means understanding that data and being able to make trustworthy, reliable decisions knowing the data is actually accurate and it’s of high quality and integrity.

So again, it’s a cultural element with a technology element. I think collectively those two can help drive a really good data driven culture for any organization.

Brian Thomas: Thank you. You’ve highlighted a really big point, obviously, is trust in the data, right? You and I have both seen this in our careers in technology where, you know, there’s something presented, whether it’s a pivot table in Excel or you’re using something like Tableau or another visualization software, and then somebody plugs something else in and they get a totally different answer.

And it does leave a lack of trust in this environment. And we just need to help people get there, but also. Mapping the data properly is a big chore as well. So you need to do that correctly, but I appreciate you sharing that. There’s just a big gap there. And I know that you’re helping to solve that issue so much looking ahead.

What are your long term goals for Pantomath, and AI, and how do you envision the company’s role evolving in this space?

Somesh Saxena: Yeah, the vision of the company, Brian, has always been to automate data operations. That’s the world I lived in in my past life. That’s where the idea of pantomath came from. There was a lot of back and forth troubleshooting, relying on tribal knowledge to manually reverse engineer data pipelines.

So identify root cause, understand impact, and then try to figure out how to resolve all of it end to end, how to rerun everything and, um, you know, get everything up and running, refresh the data across the board. And again, this is not a one to two to three, there’s like 50, 60, 100 hops to power one report across different data platforms.

Where Pantomath already helps with it today is, as I mentioned, is, you know, we ought to discover data pipelines and monitor them in real time. But there’s still a human loop element, right? Pantomath’s a SaaS application, and it’s an application that users need to use. And that human loop component today is a significant improvement on top of the world people are living in today, which is very manual and cumbersome and reactive, where your customers are telling you there’s a problem.

But again, it’s still a manual product to use like any other application. The vision, as I mentioned, Brian, is to automate data operations. AI. We want to fully automate data operations by automating the root cause analysis process with Gen AI models. We also want to give a guided path to resolution with recommendations of how to resolve these issues and ideally self heal data pipelines.

We’re talking about a closed loop remediation process where we identify an issue and detect the problems in real time. Automatically troubleshoot them ourselves and resolve those problems without any manual intervention. It’s a pretty ambitious goal and there’s a crawl, walk, run approach in how we’re building all of this.

But that is the end state, that is the goal we’re striving towards is to fully automate the entire Data operations and support process for enterprises and large organizations that today spend a lot of human capital in manually doing these things, even with a Pantomath that is a significant improvement, but without the GNI I features I’m talking about, you know, there’s still, again, that missing piece.

And that’s what we’re striving towards innovating with, uh, with this next phase we’re in.

Brian Thomas: I really love that. And it’s never too late, obviously, to get into the AI game, but GNI is going to be a huge piece of this when it comes to RCA or root cause analysis and doing some automated processes to check performance and produce reporting and that sort of thing, and pulling that human capital out to do higher level tasks.

So I, I really do appreciate that. It’s going to be amazing to have you back on the show to talk about That next phase of pantomath Somesh. It was such a pleasure having you on today and I look forward to speaking with you real soon.

Somesh Saxena: Thanks for having me, Brian.

Brian Thomas: Bye for now.

Somesh Saxena Podcast Transcript. Listen to the audio on the guest’s Podcast Page.

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