Cloud maturity models grade how an enterprise is progressing to the cloud, but these rankings don't show the whole picture Credit: Thinkstock I use them all the time: models or frameworks that allow you to compare your cloud maturity ranking with a set of criteria, letting you know where you sit in your progress toward the cloud. Scores typically range from a low level of “nonexistent use of cloud,” to the highest ranks of “cloud optimized,” or something to that effect. Of course, most enterprises are somewhere in between: an “early adopter” to “cloud preferred.” These maturity rankings typically come with sets of criteria which allow you to self-evaluate your use of cloud technology. Questions range from: “Do you use serverless or container technology?” to queries around the use of cloud-based security and governance. In the past I’ve found these maturity models to be good tools, in that I could show leaders how their company ranked in terms of cloud computing maturity. This educates the leaders and shows them how they compare with their peers. At times the models would become selling tools. The idea was to conduct a scientific analysis of an enterprise’s current state and where it should be headed. However, it really invoked an emotional response, and perhaps pushed enterprises in the wrong directions at times. The issue that I have now with the many cloud computing maturity models out there—and there are many—is that people often rely on them too much. They can dilute the larger picture of the right way to do cloud adoption and how an organization should set the appropriate priorities. For instance, it never should be about using a specific cloud-based technology, such as serverless, containers, Kubernetes, or machine learning. It’s about leveraging the cloud for the right purposes that are consistent with serving the business. These maturity models do offer a beneficial measure of culture and internal processes, which are actually more important than adopting trendy cloud technology. Indeed, unless technology is employed specifically to serve the needs of the business, technology (including cloud technology) can take you back a few steps. You’re ultimately not aligning business requirements with the correct and pragmatic use of cloud and noncloud technology. Don’t get me wrong, there are some helpful and some not so helpful maturity models out there. As I practice enterprise cloud migrations, including assessment and planning, I use some of these models as foundational benchmarks at times. However, these will never be my only metric, or else we’re likely to move in the wrong directions. Just saying. Related content analysis Azure AI Foundry tools for changes in AI applications Microsoft’s launch of Azure AI Foundry at Ignite 2024 signals a welcome shift from chatbots to agents and to using AI for business process automation. By Simon Bisson Nov 20, 2024 7 mins Microsoft Azure Generative AI Development Tools analysis Succeeding with observability in the cloud Cloud observability practices are complex—just like the cloud deployments they seek to understand. The insights observability offers make it a challenge worth tackling. By David Linthicum Nov 19, 2024 5 mins Cloud Management Cloud Computing news Akka distributed computing platform adds Java SDK Akka enables development of applications that are primarily event-driven, deployable on Akka’s serverless platform or on AWS, Azure, or GCP cloud instances. By Paul Krill Nov 18, 2024 2 mins Java Scala Serverless Computing analysis Strategies to navigate the pitfalls of cloud costs Cloud providers waste a lot of their customers’ cloud dollars, but enterprises can take action. By David Linthicum Nov 15, 2024 6 mins Cloud Architecture Cloud Management Cloud Computing Resources Videos