Data-Driven Work Cultures: Vaibhav Vohra of Epicor On How To Effectively Leverage Data To Take Your Company To The Next Level

An Interview With Pierre Brunelle

Focus on key initiatives: Essential functions need data to thrive in a competitive marketplace. Distribution, supply chain, and retail industries should focus their data transformation on their key initiatives such as pricing decisions, etc.

As part of our series about “How To Effectively Leverage Data To Take Your Company To The Next Level”, I had the pleasure of interviewing Vaibhav Vohra.

Vaibhav Vohra is responsible for Epicor product strategy and management, learning, and design. He brings deep enterprise SaaS transformation and product development experience working across a variety of industries including manufacturing, retail, and aerospace. Prior to Epicor, Vaibhav built and scaled startups including the largest consumer facing robotics/artificial intelligence deployment in the U.S.

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?

My first job was building satellites. And that’s where I gained an appreciation for designing manufacturing complex hardware.

Then after that I joined a well-known ERP company, SAP, as they transitioned to the Cloud. We launched data as a service and other initiatives.

From there, I wanted to work on disruptive technologies in A.I. and robotics. One of startups focused on the largest fleet of consumer facing autonomous robots in the US. They aided in supply chain automation in the essential industries. This brought me to Epicor, a global leader of industry-specific enterprise software to promote business growth.

Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lessons or ‘take aways’ you learned from that?

A while back, we had to make a very complex satellite that would process signals in space and send valuable data to the ground antennae. When you work on something that complex, you cannot work in a silo. You must collaborate and stay connected with other teams to ensure that this satellite has the correct weight, temperature, design parameters, interface the ground station. But unfortunately, we were too focused to the functionality of “getting it to work” that we failed to consider integration use cases. After experiencing the pain of integration, I learned a key lesson: regardless of how disruptive an engineer’s innovation is, without careful thinking about the broader ecosystem, it doesn’t return value to society.

Is there a particular book, podcast, or film that made a significant impact on you? Can you share a story or explain why it resonated with you so much?

Richard Feynman’s “The Pleasure of Findings Things Out.” The reason it resonated with me is because it shows that finding solutions is more than just memorizing steps or processes. For me, it’s about the journey of finding the solution to a problem. Both enjoying the the journey and asking “why” are two important pieces to any innovative and productive culture.

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

We are doing a lot of fun things around data. But what’s interesting about Epicor and our work is we connect the data supply chain. This could mean connecting sensor to data to insights to action. We are also bringing powerful actionable insights to our customers. This will ultimately bring “makers”, “movers”, and “sellers” closer together to help tackle critical supply chain decisions.

Thank you for all that. Let’s now turn to the main focus of our discussion about empowering organizations to be more “data-driven.” My work centers on the value of data visualization and data collaboration at all levels of an organization, so I’m particularly passionate about this topic. For the benefit of our readers, can you help explain what exactly it means to be data-driven? On a practical level, what does it look like to use data to make decisions?

There’s often a sequence of progression of an organization: data-informed, then data-driven and then data-led. I think data-informed is when you have some interesting insights, but you don’t really know what to do with it. Data-driven is when you start making decisions on readily available insights. However, data-led is where a company has autonomy over their data. What I mean by that, is there is almost a constant feedback loop of data that the company is consistently getting and making immediate decisions with because it is baked into your technology. So I think for me, there’s a step beyond data-driven. Data-driven is where you have data. But if you are purposeful with having autonomy over your data and consistently using it to lead your decision making, then you are data led.

Which companies can most benefit from tools that empower data collaboration?

I think the essential industries — the markets that Epicor serves, actually — are the ones that need pragmatic approaches to data. I think essential businesses in the make, move, sell world are the ones that need data collaboration more than ever because they had to digitize over the last two or three years. Companies that ultimately succeed will be the ones spending their time being selective about the right initiatives and gaining intelligence from their data in a pragmatic way.

We’d love to hear about your experiences using data to drive decisions. In your experience, how has data analytics and data collaboration helped improve operations, processes, and customer experiences? We’d love to hear some stories if possible.

In manufacturing, distribution, and retail one of the biggest challenges is supply chain visibility. Manufacturers and distributors are looking to better understand what happens upstream and downstream. We’ve been collecting aggregated insights to help in the automotive industry, for example, parts failure rates and using it to better inform manufacturers of supply chain decisions to better run their businesses.

Has the shift towards becoming more data-driven been challenging for some teams or organizations from your vantage point? What are the challenges? How can organizations solve these challenges?

There are definitely some challenges for teams or organizations to become more data-driven, such as data silos. A data silo is a collection of data held by one group that is not easily or fully accessible by other groups in the same organization. Everyone wants the best data and knowledge is power, but it can be difficult.

One challenge for a company is having the desire to be connected but not having the technology to be connected. APIs offer flexibility, and are a type of technology that can connect data across an enterprise and beyond –most organizations need help automating and simplifying their approach with this.

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.

• When it comes to data, start with action: For example, if you’re looking to increase supply chain visibility, you need to figure out data on manufacturing schedule/delays. A company first needs to know the problem it’s trying to solve and then get the data, rather than seeking any data first.
• Automaton without people fails: Most automation initiatives fail because an organization may try to replace people instead of empowering them to learn the technology and digest it to make decisions.
• You need your ERP connected with the outside world: A standalone ERP system might not be able to solve complex solutions. Epicor partners with a variety of providers, such as iPaaS, to create an end-to-end solution for its customers.
• Understand who the data impacts: It’s important for a company to understand exactly what it is solving for.
• Focus on key initiatives: Essential functions need data to thrive in a competitive marketplace. Distribution, supply chain, and retail industries should focus their data transformation on their key initiatives such as pricing decisions, etc.

The name of this series is “Data-Driven Work Cultures”. Changing a culture is hard. What would you suggest is needed to change a work culture to become more Data Driven?

I would suggest putting the right incentives in place because collaboration is key. It requires collaboration across teams to understand what the problems are that need solving through data.

The future of work has recently become very fluid. Based on your experience, how do you think the needs for data will evolve and change over the next five years?

The need for data will evolve to be more about machine data and connecting to other technologies. Artificial intelligence (AI) & machine learning (ML) will also come into prominence in the next five years, begging the question of how we will govern that intelligence?

Does your organization have any exciting goals for the near future? What challenges will you need to tackle to reach them? How do you think data analytics can best help you to achieve these goals?

We are excited to continue developing integrated cloud solutions for our customers who make, move, and sell the things we all need that are people centric, open and connected. With our acquisition of GROW, we believe organizations will be able to use predictive analytics and our data supply chain to help solve major challenges.

How can our readers further follow your work?

Epicor Social Handles:

Instagram: @epicor

Twitter: @epicor

LinkedIn: Epicor // Vaibhav Vohra

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

About The Interviewer: Pierre Brunelle is co-CEO and Chief Product Officer (CPO) of Noteable, the collaborative notebook platform that enables teams to use and visualize data, together. Prior to Noteable, Brunelle led Amazon’s internal and SageMaker notebook initiatives. Pierre holds an MS in Building Engineering and an MRes in Decision Sciences and Risk Management.