SmartBear Software has named John Purcell its new VP of Products, API Readiness.
Purcell is an accomplished technology and business leader with over 16 years of experience in the telecommunications and IT industries. Most recently, he was Senior Director, Products for LogMeIn, Inc.where he held various product leadership positions across the company’s key Customer Care and IT Management portfolios.
“Over the last several years, SmartBear’s API readiness business has experienced significant growth and become a multi-product offering to meet the growing demands for delivering reliable, scalable and secure APIs,” said Doug McNary, CEO of SmartBear. “John has extensive experience in product leadership and scaling product lines, and will be instrumental in taking SmartBear’s API business to the next level.”
Purcell served LogMeIn for nearly five years, where he delivered consistent business growth for the company’s IT management portfolio, leading a large products, business operations and engineering organization. For more than six years, he was Technical Director at Red Bend Software, the company that catalyzes change in the connected world by keeping more than two billion automotive, IoT and mobile devices relevant. He also served LogicaCMG, now Acision, as Technical Team Lead. He has written for industry outlets and spoken at trade conferences on a wide variety of topics. Purcell has a bachelor’s degree in engineering (electronic and electrical) from University College Dublin and a master’s degree in business administration from Babson College.
“APIs are a critical aspect of modern software development and are an integral part of everything from distributed software to connected devices,” said John Purcell. “SmartBear’s API business is at an exciting stage, and I am delighted to lead the mission to ensure our customers are developing robust API readiness plans and best practices in these quickly evolving industries.”
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...