Somesh Saxena Of Pantomath On How To Leverage Data To Take Your Company To The Next Level

Data Visibility & Traceability: Map your entire data ecosystem as complicated as it may be. Get an understanding of what data products you’ve built, the data pipelines that feed them, and classify the data and pipelines through tags.

The proper use of Data — data about team performance, data about customers, or data about the competition, can be a sort of force multiplier. It has the potential to dramatically help a business to scale. But sadly, many businesses have data but don’t know how how to properly leverage it. What exactly is useful data? How can you properly utilize data? How can data help a business grow? To address this, we are talking to business leaders who can share stories from their experience about “How To Effectively Leverage Data To Take Your Company To The Next Level”. As part of this series, we had the pleasure of interviewing Somesh Saxena.

Somesh Saxena is the CEO & 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 & Analytics at GE Aerospace where he led a 100+ person organization supporting 18,000 data consumers. He witnessed his teams, 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 Paycor, Wex, G&J Pepsi, E.W.Scripps, and several large F500 customers to enable AI and data analytics initiatives.

Thank you so much for joining us in this interview series. Before we dive in, our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?

I have spent the majority of my career in the data and analytics space. Prior to founding Pantomath, I was a Data & Analytics leader at General Electric Aerospace supporting 18,000 data consumers through a 100-person organization. I led multiple programs at the company, including enterprise data and analytics, self-service data, big data, data governance, and robotic process automation, giving me unique insights into the problems and challenges encountered by data teams.

Through conversations with peers in the industry along with my first-hand experiences, I learned that even though companies are striving to be data-driven, building dashboards, analytics, and data pipelines across the modern data stack, they struggle with data reliability issues leading to poor business decisions and lack of trust in data as an organization, directly impacting their bottom line. Resolving data issues is a manual and time-consuming process involving multiple teams all relying on tribal knowledge to manually reverse engineer complex data pipelines across different platforms to identify root-cause and understand impact. All leading to productivity loss and data downtime.

The idea of Pantomath was born out of this major pain point faced by data teams and data consumers alike. The vision was to build a product that could offer end-to-end observability and traceability across the data stack to detect problems in real time, enable data teams to troubleshoot those issues in a simplified way, and resolve the incidents instantly.

It has been said that sometimes our mistakes can be our greatest teachers. Can you share a story about a humorous mistake you made when you were first starting and the lesson you learned from that?

I have learned to seek advice more frequently than I used to. I was notoriously bad for trying to figure everything out on my own and internalize things more than needed. As a solo founder and a first-time entrepreneur, I learned pretty quickly that I needed to ask for help early and often because it was all so new to me.

Leadership often entails making difficult decisions or hard choices between two apparently good paths. Can you share a story with us about a hard decision or choice you had to make as a leader?

Prioritizing between growing sales and supporting customers the right way was a challenge in the early days. As a startup, we often faced chicken-and-egg problems and prioritizing the right places to invest time, effort, and resources was challenging. In the trade-off decision making process, I chose to prioritize customer health and satisfaction, optimizing for the long-term growth of the company and ensuring we’re doing right by our valued customers.

Are you working on any new, exciting projects now? How do you think that might help people?

We’re working on some really cool and innovative GenAI features to automate root-cause analysis for data reliability and quality issues and self-heal data pipelines in a fully automated way. It will be a drastic shift in how organizations support and maintain their data pipelines and products, driving productivity savings and reducing mean time to resolution, while also establishing trust in data and a data driven culture.

You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?

1. Ambition: Shooting for the stars!
2. Resilience: Building something from scratch and running a business are not easy. Things change all the time and you’re faced with new challenges every step of the way. Resilience is key in my opinion.
3. Influence: Enabling people to achieve more than they expect out of themselves is a very important character trait for any leadership role.

All three go hand in hand when it comes to my journey with Pantomath. My team and I set out to build a solution that no one else had been able to build. We didn’t shy away from the goal, cut corners, or settle for less. We faced several challenges along the way, but we rolled with the punches and got the best out of one another through the toughest of times leading to our success.

Thank you for all that. Let’s now turn to the main focus of our discussion about empowering organizations to be more “data-driven.” For the benefit of our readers, can you help explain what it looks like to use data to make decisions?

Data-driven decision making is of utmost importance for any organization. It is easy to fall back to instinct while making tough open-ended strategic decisions in any company. And while that may be part of the decision making process, it is critical to back that up with the right data.

Empowering organizations to be more data-driven includes an investment in people, processes, and technology. The technology part is the easiest. Collecting data, transforming it, and building reports that people can drive insights from is only a piece of the puzzle. Building the right processes around data consumption and investing in the democratization of data to drive a data-driven culture is the more challenging part.

At General Electric, my team and I democratized data by leading a business-wide cultural transformation, enabling 3,000 business users to create their own analytics and reports in a digital and automated fashion, driving significant outcomes for the organization. The right tools, technologies, processes, and training and education programs all came together to make it happen.

Based on your experience. which companies can most benefit from tools that empower data collaboration?

Every company regardless of their industry that strives to be data-driven can benefit from data collaboration as well as healthy and reliable data.

Can you share some examples of how data analytics and data collaboration can help to improve operations, processes, and customer experiences? We’d love to hear some stories if possible.

Data teams tend to work in silos; building in silos, utilizing different tech between business units, and solving problems in silos. When you work in silos, you have little to no visibility upstream or downstream of your work and ‘improvements’ or ‘changes’ that you implement could have catastrophic outcomes. Having a tool that sits over your entire data ecosystem with clear and accurate end-to-end data pipeline lineage allows for teams to still be focused on their own work and at the same time know how their work affects other teams. This enables trust throughout your organization and gives you the ability to enable alerts for specific teams so that they can triage and resolve issues in real time.

From your vantage point, has the shift toward becoming more data-driven been challenging for some teams or organizations? What are the challenges? How can organizations solve these challenges?

The shift towards becoming more data driven has been challenging for most organizations to an extent. The spectrum is vast based on when an organization decided to modernize their data ecosystem, build data products, and drive data adoption. But regardless of where exactly an organization is in their journey, change management is never easy. Understanding data, utilizing it to drive outcomes, governing the data, and ensuring its accuracy are all significant undertakings. Data quality and reliability issues plague most organizations. Most teams cannot rely on their data with confidence. In today’s “data-driven” companies, expensive and costly decisions are often made daily with faulty data unbeknownst to the data consumers and decision makers. To avoid these pitfalls you must have a full view of your entire data ecosystem and real-time monitoring across each granular asset within the ecosystem in order to be able to mitigate and catch issues as soon as possible and ideally before the issue ever makes it to the end user. Data observability has the potential to drive data reliability and trust in data, eliminating poor decision making with bad data.

Ok. Thank you. Here is the primary question of our discussion. Based on your experience and success, what are “Five Ways a Company Can Effectively Leverage Data to Take It To The Next Level”? Please share a story or an example for each.

1. Data Visibility & Traceability: Map your entire data ecosystem as complicated as it may be. Get an understanding of what data products you’ve built, the data pipelines that feed them, and classify the data and pipelines through tags.
2. Data Observability: Ensure data reliability and streamlined data operations through data observability and traceability, driving trust in data through healthy and good quality data.
3. Data Governance: Build a data governance program supported by data stewards that own and manage data for their domains.
4. Data Products: Identify key use cases for the organization and build data products and reports to drive business outcomes. Democratizing data through self-service data analytics can accelerate this journey.
5. Data Adoption: Drive data literacy programs and adoption of data products and reports to ensure data consumers are making data-driven decisions.

Based on your experience, how do you think the needs for data might evolve and change over the next five years?

• Real-time data observability is going to become critical.
• Reliable and healthy data will become essential to support GenAI models and use cases.

How can our readers further follow your work?

Somesh’s LinkedIn, Pantomath’s Blog

Thank you so much for sharing these important insights. We wish you continued success and good health!