On Cloud

At AWS re:Invent 2019, AWS introduced new, exciting, and evolutionary products and services in the cloud space. There's lots of buzz around IoT, machine learning, and 5G, making companies smarter—and helping developers move more operations to the cloud.

Despite the strong imperative to digitally transform, many companies still struggle with it. One strategy is to adopt a cloud-native mindset that leverages AIOps and shifts DevOps bottlenecks left to re-think software development, delivery, and deployment in a cloud-native world.

As the panoply of cloud vendors and products grows ever larger, so does the configuration and management complexity of the cloud. For many companies, this complexity can reduce the value of their cloud-computing investments. In this episode of the podcast, David Linthicum and guests, Intel's Lisa Davis and Deloitte's Doug Bourgeois, discuss ways companies can tackle cloud complexity and leverage multi-cloud architectures to help optimize cloud deployments in terms of performance, cost, and agility. Lisa and Doug also argue that companies need to realize that hardware isn't a commodity and that cloud architectures—and talent acquisition decisions—must be driven by mission-critical business needs and workloads.

There's little doubt of the value of agile and DevOps, but many companies still struggle to innovate with IT. It's often because they're stuck in a project management paradigm. One solution is to focus on product value streams and measure the business value delivered by software.

DevOps is a boon to software development and delivery, but companies sometimes struggle to get DevOps right. Most often, it's a people, rather than a tooling issue. Successful DevOps takes the right people and leadership, and a view from a product, rather than a project, lens.

Many companies struggle with technology in general, and cloud migration in particular. What's needed is a fresh perspective—a new look at how to accelerate what's possible. Savvy companies understand that to be able to drive increased value, they should increase diversity in the tech workforce. They also recognize the need to better understand and navigate the massive disruptions and cultural changes that come with their cloud computing journey. In this episode, David Linthicum and guest, industry thought leader Rashim Mogha, discuss ways companies can build a more inclusive, diverse workforce and manage the disruption that comes with digital transformation. Mogha's recipe for success: give everyone a seat at the table, automate, manage disruption, and focus relentlessly on building an adaptive culture from the top down.

Cloud architectures are incredibly complex and, often, it's all but impossible to predict and prevent failure scenarios. Enter the ascendant discipline of chaos engineering, which aims to discover cloud failure points, in in-production systems, before they become disasters.

Increasingly, companies are realizing that their cloud migration journey is defined as much by their culture as by their technology choices. Indeed, effectively managing the seismic changes that come with cloud computing is key to enabling a successful cloud adoption strategy.

Companies often struggle to develop efficient cloud-native application environments. Frequently, it's because they haven't implemented product-oriented, enterprise-ready application delivery methods. Those that can make the shift, however, can reap enormous benefits.

As DevOps matures, and the "shift-everything-left" philosophy gains ascendancy, there's a movement to automate all—or most—phases of testing. However, there are some critical functions that may resist automation. In fact, contrary to the "automate-everything" impetus, human testers won't ever go away. Indeed, human testers need to be more involved, and earlier in the development process. In this podcast, Mike Kavis and guest, Angie Jones, discuss the human aspect of testing and how humans add significant value by assessing the system as a whole, helping developers design better code, and determining the level of testing automation that should occur. Angie also shares the testing automation matrix she has developed. Finally, they cover testing of machine learning algorithms—ways to help prevent or reduce bias and make algorithms more effective, and the emerging field of visual testing, which uses humans to ensure that graphics-heavy apps function and appear as designed.