Cloud technology: A synergistic environment for KM and generative AI
Cloud technology has been a boon to KM in many ways: It can unite disparate data repositories, making them accessible to users within and outside of organizations and in multiple geographic areas. It can connect not just data, but also people, by enabling collaboration. In commercial use for almost 2 decades, cloud computing accounted for nearly $500 billion in 2022 and grew another $100 billion in 2023 to $600 billion, according to Grand View Research. Depending on what components are included, other market figures for cloud technology can exceed $1 trillion.
Organizations turn to the cloud in order to reduce pressure on their IT departments, save on upfront costs of equipment, and allow for rapid adjustments in storage needs. More than 90% of corporations use cloud services, and they typically are using a multi-cloud strategy. About 60% of corporate data is now stored in the cloud, and more than 2 billion consumers use personal clouds to store documents and pictures. The trend toward remote working that was sparked during COVID made the use of cloud environments necessary, and working from home has continued to be appealing to many employees.
The biggest cloud provider is AWS, followed by Microsoft Azure and the Google Cloud Platform (GCP). These companies provide the infrastructure and storage, along with software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) options for software, platform, and infrastructure support. From a KM viewpoint, cloud-based applications such as Bloomfire, Helpjuice, and Notion provide workspaces that allow integration of data and provide collaboration environments.
Cloud technology may have become a commodity to some extent, but it is not a simple commodity. The technology that allows cloud computing to be dynamic and agile is composed of many interrelated components, which means that when one thing goes wrong, the problem can cascade. Despite the fact that nearly every organization is using the cloud, about one-third report that their cloud implementations have failed completely, and about one-fourth report that their migration has not met the intended schedule.
Implementing the cloud
As is the case with any implementation of technology, careful planning is a prerequisite for success. “Embracing a strategic, forward-looking mindset is essential,” explained Swanston Benjamin, director of managed IT, cloud, and infrastructure with RSM US LLP (RSM), a leading provider of assurance, tax, and consulting services for the middle market. “You also need solid, across-the-board stakeholder buy-ins and a designated journey champion.”
Overlooking the early stages of the journey, such as embracing the importance of cost, security, networking, testing, and setting realistic expectations, leads to problems later and is a primary cause of cloud implementation failures. RSM provides a variety of cloud advisory, delivery, and management services, including cloud migration from the initial stages of advisory and discovery, through planning and design, implementation, transition, and operations.
Aside from adopting a SaaS model, the three main options for migrating information and applications to the cloud are lift and shift, modernization, and cloud native. Lift and shift will move existing resources to the cloud, which is the most straightforward approach, but those resources are not necessarily optimized for the cloud. Modernization involves the refactoring of existing applications, which makes use of the abundance of serverless and PaaS resources in the cloud.