The Paradox of Cloud Computing Adoption – Part I

Cloud Computing has been a major factor for companies of all sizes and types in determining their IT strategy. The compelling nature of the pay-as-you-go model that has minimized large capital outlays for technology projects, enhanced agility that enterprises derive with the ability to scale up/down at will, and reduced costs incurred due to sharing infrastructure established at large scales have made it highly attractive to executives. These factors also appear to make a solid case for Cloud adoption; however, the ground reality is quite different. For any enterprise, Cloud strategy is not a one-size-fits-all and depends upon the intrinsic complexities that define an organization, which in turn is based upon the nature of its operations.

Looking at various applications that exist within an organization from the outside, it appears simple to decide which applications can be moved to the Cloud and those that should be retained in-house. Yet, this is a simplistic view that ignores the context of these applications and the organization within which they exist. While the nature of any given application seems to be an obvious driver in the decision to move it to the cloud, a close look at the current landscape across various organizations reveal that the organization’s size, maturity and integration with other applications are also significant drivers of the adoption. This paper attempts to take a closer look at the many complexities involved with matching any given enterprise to an appropriate Cloud strategy, and explores the drivers that explain such a disparity in adoption of Cloud services.

As a new startup, it is a simple proposition to use the Cloud for computing capacity. There are no large capital investments needed, or processes for acquisition and installation of equipment. A Cloud service can be used to provision capacity for a nominal cost. There is no need for expensive infrastructure that might be underutilized, or teams to manage the same. In case there is a need for more capacity, it can be obtained instantaneously. This increases the agility of a startup, while keeping down costs. As the startup grows, its infrastructure can also grow with it.

As organizations grow, the scale and scope of their IT needs/applications also increase. Large scales imply economies of scale from deploying applications in-house, reducing benefits resulting from migrating to the Cloud. With a large scope, multiple Cloud providers are needed to satisfy the organization’s needs, which makes it preferable to retain these applications in-house. Although most of these workloads are unlikely to provide any economies of scale, the integration between them represents a large barrier to migration.

As with scale, the complexity of the applications used within organizations increases as they grow/mature. In the early stages most companies just need email and spreadsheets. As they grow they need tools such as Salesforce, Workday, etc. They might also use computing capacity from AWS or Google, and migrate their email to Office 365. They also begin to establish workflows that integrate these services to mirror their operational processes. While it makes sense to use XaaS services for most needs, other factors accompany growth – gradual acquisition of proprietary information, data movement (between applications, organizational functions, and multiple sites), and regulatory mandates. These factors promote the establishment and usage of complex systems.

Examples of complex systems are ERP systems, MES systems, and Data warehouses. They impact multiple or all functions within the organization, make extensive use of automated workflows, and generate reports at daily or hourly rests that are needed to make operational decisions. They communicate with many other applications, and interact with multiple organizational functions and impact core operations at various levels within the organization. They communicate with systems and applications that belong to their suppliers, partners, customers, and regulatory organizations. They are organization-aware and have been extensively customized to closely match the organization’s operations. Losing these applications has critical impact upon an organization, bringing most operations to a halt.

It is this combination of organizational and industry awareness, criticality of business impact, and extensive integration with other systems that makes complex systems hard to replicate within a Cloud environment. In case these systems already exist in-house, it is quite difficult to upgrade, replace, or migrate to the Cloud.

CA_P11

 

Based on the chart above, we see that organizations with large monolithic applications, or organizations with complex applications (even at low scales), should maintain these in-house. On the other hand, smaller companies with low complexity of applications are better served by using Cloud-based applications.

What would be the recommendations for an existing enterprise? Again, for an existing startup, this would be a no-brainer; in most cases, it would be to leverage the Cloud to improve agility while minimizing expenses. On the other hand, a large existing enterprise has the necessary scale to retain its infrastructure and systems in-house. It is quite easy to recommend that a new startup rent its infrastructure at AWS or build its applications upon the Azure platform. It is similarly easy to recommend that companies such as GE or Merck maintain their systems in-house. The recommendation is not so clear for medium-sized companies; they do not have a very large scale to justify in-house retention. At the same time, this is complicated by integration between applications.

How do these recommendations change with growth? When does Cloud Strategy dictate moving Cloud systems in-house or vice-versa? Let us take a look at the chart below to understand the impact.

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Startups score low on both scale and scope of their applications, and hence are located at the top right of the above chart. As they grow, the scale and scope of their workloads increases, causing them to resemble mid-sized companies, and ultimately become large enterprises. This migration is also pictured above. At some point in the organizational lifecycle, it becomes economically imperative for applications to be migrated to in-house systems, as shown in the blue region. However, strategic considerations may pre-empt this move, within the brown region above.

What are some of the other considerations that dictate Cloud strategy and have a bearing upon customer adoption? We will take a look at these aspects in Part 2 of this paper.

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