The demand for data-driven insights is rising, but many critical IT systems are nearing their end of life. A survey by Kyndryl found that up to 44% of these systems may already be outdated. Even companies that invest in updating their data infrastructure often struggle to see a return on their investment.
Paul Henninger, a partner at KPMG UK, emphasizes the need for organizations to establish clear priorities before embarking on any major project. It’s crucial to clarify key business outcomes to understand what you want to achieve. “Identify what you really need to fix, especially with AI coming into play,” he advises. “Successful modernization starts by defining business outcomes and working backward.”
Petra Goude from Kyndryl agrees, urging companies to focus on critical changes rather than an all-or-nothing approach. She warns against making sweeping tech changes that can blow budgets without ensuring a return on investment. A common pitfall is overcommitting to a single technology, like the cloud, which can lead to regret later on.
A significant issue hindering modernization is the shortage of skilled workers. Kyndryl’s survey reveals that around 40% of leaders cite skills gaps as a barrier to progress. Goude notes that without the right skills, modernizing technology won’t yield results. A lack of enterprise architects and data scientists limits organizations’ ability to structure and utilize their data effectively.
Once companies establish their desired business outcomes, they need to determine what data is essential and how to manage it efficiently. Henninger cautions against modernizing data infrastructure just for the sake of it, highlighting that understanding customer data is often more complex than it seems. Different departments may have varying definitions of fundamental concepts like “employee,” which can complicate data management.
Trust and security in how data is used have become critical in this digital age. Henninger points out that while AI makes data access easier, it also raises concerns about security. Many executives worry about cyber attacks, but only a fraction feel prepared to handle these threats. Confidently using and measuring data’s value becomes essential for organizations.
Goude emphasizes the need to evaluate each application and decide whether it should be sped up, made cheaper, or even discarded. “You might enhance applications without completely redoing them,” she explains. Companies might find that keeping costs low and optimizing workloads can lead to better performance.
Ravin reminds organizations to maintain a budget for innovation, rather than expending all resources on upgrades. He asserts that software might last longer than vendors suggest, and businesses should analyze usage to find efficiencies. A sound strategy might mean keeping operational expenses lower and allocating a larger portion of the budget for new initiatives.
While 76% of businesses are investing in AI and machine learning, only 42% report seeing positive returns. Kyndryl suggests there’s potential for significant cost savings by automating IT issue resolution. Ultimately, modernizing outdated data infrastructure is not just about spending money; it’s about making informed decisions that drive real business outcomes, ensuring that every investment addresses a specific problem.
