Why DevOps and data are C-suite issues that need addressing
In this guest post, Jim Cassens, CEO of open source DevOps automation tools provider Perforce, sets out how DevOps can help enterprises get to grips with their increasingly complex IT infrastructure estates
With the acceleration of new technology developments due to artificial intelligence (AI) and the continued escalating security risks and regulatory requirements that have arisen, boardroom attention has to be on the technology and the practices around technology that can make or break a business – even things that seem as technical as IT infrastructure.
With IT estates often comprised of disconnected elements, IT infrastructure is becoming increasingly vast, complex and difficult to manage. Yet, organisations are pressured to keep up with evolving markets, remain competitive, and meet customer demands. In reality, enterprises struggle to keep on top of everything with no time for real innovation, risking lost revenue and the chance that valued customers may go elsewhere if they have a less-than-satisfactory experience.
This potential perfect storm of challenges is why topics such as DevOps and data management must be viewed as C-level issues that require the understanding and support of the entire management team – and we are seeing more signs of this happening. When more boardrooms commit to supporting the right technology initiatives, their organisations can leapfrog the competition, create more content workforces and happier customers, and achieve greater efficiency, security, and compliance.
So, what might constitute the bones of the right technology strategy for 2025? That will vary depending on each enterprise’s needs and nature, but based on conversations I’ve had with CIOs, CTOs, CSOs, and many who sit on boards at recent industry events, a few standout themes are emerging.
Dealing with the data mountain
Data has been called the new gold, especially when both AI and software testing processes are so dependent on its availability and quality, but how it is mined for and managed is an essential priority. That precious resource needs to be secured and maintained, yet enterprises have giant, monolithic data sets that are extremely difficult to have visibility into or maintain, and recreating large data environments can be a long process that is fraught with errors.
However, data is another area where proven DevOps techniques and tools can play a significant role. For instance, being able to subset the data into smaller chunks will drive efficiency for QA and other development teams, while also making it easier to meet privacy requirements through data masking (which hides the real data). This approach also helps mitigate the risks of exposing unprotected confidential data to internal resources, which in turn would create a potential security and compliance risk for the organisation.
AI needs a measured approach
While AI has huge potential benefits (and as an organisation, we’ve already embraced AI internally and increasingly as part of our product portfolio), it must be considered case-by-case. Where is AI really adding value? Sure, organisations cite productivity gains, but what about the bigger picture? How is AI driving the business? Is it improving customer satisfaction? How is AI’s impact being measured?
Also, AI must be implemented together with rigorous security and compliance measures from day one, and given that many organisations are still in the early stages of AI exploration, now is the ideal time to lay those foundations. There also needs to be a mindset shift: a security-first mentality is not a block to agility but quite the reverse: putting the proper security, privacy and compliance processes in place leads to greater efficiency and velocity in the long run.
Adding in more tools is not the answer
This may come as a surprising statement from a company that sells software tools, but just buying and implementing more of them is not the way to solve problems. Adding another layer of tools can contribute to complexity, especially in environments where existing technologies are already disconnected. Even making the simplest of bug fixes is sometimes very difficult when that happens.
So, what’s the answer? We recommend taking a step back, starting with evaluating what the business really needs. Are existing systems still fit for purpose? How are they integrating with and supporting new directions? Are there too many suppliers and solutions that are unable to connect? How can that be consolidated? Also, look to vendors for help because, increasingly, we are hearing that businesses do not just want tool providers. They want partners who will help work out where the roadblocks are, connect everything, and invest the time to collaborate to work out technology that can help solve business issues because sometimes the answer may not be the most obvious one.
This brings us back to why DevOps and data (along with AI, automation, security and compliance) are C-level issues and beyond just CIOs, CTOs and CSOs. Having deep knowledge of how technology can drive and support a business is what matters above all, and while that should be a given, sometimes a return to fundamentals is needed. Technology, for technology’s sake, has no place in 2025. So, now is the time for the whole C-Suite to get behind supporting DevOps and the technology on which businesses depend.