MarTech360 Interview with Ed King, Founder and CEO at Openprise

“Many of the last decade’s accepted GTM “best practices” may no longer be true. It’s imperative that leadership question everything and not assume that, just because the GTM playbook worked well when the market was strong and capital was cheap, it will continue to work in this environment”

Ed, as the founder and CEO of Openprise, you’ve had extensive experience in marketing and product management at companies like Axway, Qualys, and Oracle. Having deployed Marketo multiple times and faced data quality challenges, how has your experience shaped the development of Openprise’s solutions for data management, and what key features of Openprise specifically address the data quality issues you encountered?

There were three key lessons I learned when running marketing:
(1) Every operations problem in sales and marketing ended up being a data quality problem.
(2) Data quality is a much broader topic than most people understand.
(3) In order to have a sustainable and scalable data quality program, sales and marketing ops have to own it because IT just doesn’t have the agility and business context to support the fast pace and constant changes.
These three lessons fundamentally influenced Openprise’s platform and solution offerings:
(1) This is why Openprise uniquely focuses on RevOps data quality.
(2) Openprise addresses data quality as a much broader topic, covering technical quality, operational quality, and strategic quality.
(3) Openprise is a no-code platform designed specifically for ops builders.

Ed, can you tell us about your professional background and your current role at Openprise? Also, please tell us how Openprise differentiates itself from other companies in the same space.

I’m a mechanical engineer by training and started my career as a manufacturing automation engineer, working on computer numerical control (CNC) machines and robotics, so automation is deep in my blood. I spent most of my pre-Openprise days working in product management and marketing. After I moved into software, the industries I spent the most time in were supply chain management, security, and middleware. Because of my background in manufacturing and supply chain management, I have always been a process person and have understood that while automation and technology are fun and important, if they don’t support the people and the process, all technologies will fail.

I can give you a great example of this. One of the unique features of Openprise is the App Factory, which enables the Ops team to build no-code, self-service apps for processes like list loading and list building. Traditionally, these tasks require the Ops team to act in a helpdesk capacity to do such manual work for end users. At Openprise, we love automation, but we also know that there will always be tasks in go-to-market that require human interactions. If you don’t make the human tasks more efficient, they quickly become the bottleneck to scalability, so enabling self-service is as important as adding automation in ops.

Given the challenges of managing multiple point solutions for marketing operations, how does your single data and process orchestration solution help organizations align, optimize, and scale their marketing efforts? Specifically, how does your platform address data standardization, deduplication, and campaign attribution to improve targeting and achieve higher conversions?

Your average medium to large enterprise GTM organization now uses over 90 software as a service (SaaS) technologies. No organization can effectively manage a tech stack that complex. Point solutions and point-to-point integrations introduce technical and process complexity, which accumulate into technical debt and data debt, making processes fragile and impossible to manage and debug. But put that aside for a minute and consider the human cost of managing such a large complex tech stack: How big an Ops team do you need? How many product certifications and trainings do you need your team to maintain? How do you ensure there is always coverage when people are out of the office? How many cycles are you burning in tech evaluation, vendor onboarding, and yearly security assessment?

Adopting a platform-based architecture simplifies all of that. It enables you to have a smaller team that can become experts in fewer technologies. Having a single platform also means that,for foundational work like data quality, which includes standardization and deduplication, you do the work once and the solution can be leveraged by the entire Ops team with all of the processes built on the same technology platform. This means everybody can leverage the solutions others have built and solve problems faster than having to constantly reinvent the wheel on their own using different technologies.

What makes Openprise a leader in Revenue Operations (RevOps) Data Automation, and how does its no-code RevOps Data Automation Cloud empower non-programmers to automate processes and leverage customer data effectively?

Openprise is the only—and we are not exaggerating here—no-code, full-stack, end-to-end data quality platform for the enterprise Ops team. There is a fundamental challenge in every company that the business needs to run at 10x the speed that IT can support. Traditionally, IT has been the only team with the skillset to use the middleware that’s capable of solving complex automation and data management use cases. For over a decade, IT has tried to improve its alignment with the business by creating business IT teams, which has improved the situation moderately, but the gap still persists because IT doesn’t understand the business data, and IT is designed to build things that last. Making IT more businesslike has helped a little, but the only solution left is to give business users technology they can use. With a no-code platform like Openprise, the business user can quickly create the data-driven solutions they need and, importantly, do it in an agile manner so they can iterate quickly with feedback and keep up with the constant changes the GTM requires.

Also Read: MarTech360 Interview with Rytis Lauris, Co-founder and CEO at Omnisend

How can companies effectively manage and mitigate ‘data debt’ in their tech stacks, especially within the realm of RevOps and GTM strategies? Additionally, how does the three-tier data quality model proposed offer a solution to these challenges?

Data debt, just like tech debt, is primarily a byproduct of complexity, so the only way a company can pay down its data debt is to simplify. Simplification means having a clear understanding of who owns what data and giving the data owners tools to manage the data effectively. Simplifying also means that teams have to be able to leverage each other’s work without reinventing the wheel because when people have to recreate work, they further create data silos, which makes data debt worse. Having a single data quality platform that all teams can use goes a long way to making this possible. The first thing to do when trying to get data debt under control is to stop the bleeding. This means stopping the inflow of new poor-quality data before you try to improve your existing data. A single platform that can control all the major data ingestion points into your system of record is critical. This is why many Openprise customers call us their “data firewall.” The threetier data quality model gives the GTM team a structured framework to holistically think about GTM data quality. Most people think about GTM data quality too narrowly, at what the framework calls the technical quality layer. The three-tier framework helps the entire GTM team, including senior leadership, understand that data quality’s impact is pervasive and literally every GTM problem has a data quality aspect.

How can organizations ensure high data quality in their RevOps processes to enhance the effectiveness of AI-driven decision-making and business operations?

AI may be our generation’s biggest data-driven solution. Whatever advancement AI has in the near future, it’s still a pattern recognition machine. Some people jokingly call it auto-complete on steroids, which is not inaccurate. All that means is that the proverbial garbage-in, garbageout problem applies to AI in spades. Since most AI technologies are black box in nature, the only way you can affect the output is via the data input, whether it’s training data or the prompt. We still have not seen a “killer app” yet in GTM AI solutions, but it’s not unreasonable to think that a few may emerge in the next 18 to 24 months. While we don’t know what those killer apps will look like, we know they won’t work correctly if you feed them bad-quality data. So, I encourage companies to get their data quality in shape now, so that, when the killer app emerges, they’ll have a running start compared to the competition.

What advice would you give to other leaders which helped you personally? Q. What is the biggest problem you or your team is solving this year?

The tech industry has fundamentally changed since mid-2022 and this slowdown will likely persist for another 24 months at a minimum. Market demand is lower, and operating efficiency must be higher. Brute-force GTM strategies fueled by cheap venture capital that worked in the last ten years don’t work anymore. Many of the last decade’s accepted GTM “best practices” may no longer be true. It’s imperative that leadership question everything and not assume that, just because the GTM playbook worked well when the market was strong and capital was cheap, it will continue to work in this environment. Measure everything and be relentless in making every decision a data-driven decision if you want to be the few companies that can survive this market and come out stronger on the other side. At Openprise, we are doing precisely that. We are re-evaluating our ideal customer profile and tweaking our target markets to be aligned with strong market segments. We are tweaking our GTM campaign investments and tactics to align more with these lean market conditions.

Is there anything that you’re currently reading, or any favorite books, that you’d recommend?

My reading list is quite eclectic and I wormhole into specific subjects. Two subjects I’m obsessed with at the moment are religion and human history, especially books by Yuval Harari. I also read everything Michael Lewis writes. The common thread to all these subjects and authors is that they explore the fundamental forces that make us who we are and drive what we do. As I get older and hopefully wiser, I realize that even though our world is extremely complex, it is fundamentally shaped by some basic forces and human nature. This is also why I founded a data quality company. I like to learn about root causes and foundational forces and see how we can improve the world through better understanding of these atomic forces.

Thanks, Ed!