Economic Benefit of the Cloud

An organization typically has many different types of workloads to manage in its data center, and some of these workloads will be a better fit than others for a cloud environment. Therefore, to optimize your economic benefit from the cloud, you must first have a good understanding of your workload requirements.

Commodity workloads, such as everyday e-mail, collaboration, and messaging applications, are straightforward and well-defined business processes executed over and over again. The economic benefit for workloads with these characteristics comes from leveraging cloud capabilities such as standardization, optimization, and scalability. A commodity workload such as an e-mail application is a good fit for the cloud.

A customer-facing financial application in the heavily regulated financial industry is not likely to be a good fit for the cloud because of security concerns. The reason for this is that any potential economic benefit from the cloud is outweighed by security and compliance issues. An organization may have specialized workloads that are used occasionally by a select group of users. These specialized workloads may have run effectively for many years in the internal data center, so there may be no economic benefit in moving them to the cloud.

After you evaluate your mix of workloads, however, you will find many situations where the standardization, flexibility, and scalability of the cloud can deliver outstanding economic benefit.

A move to the cloud is likely to deliver an economic benefit if you have a need for

Increased capacity: Your organization is ramping up for a new but short-term initiative, and you temporarily need some extra CPU capacity and extra storage.

A Software as a Service (SaaS) solution: As your company has grown and diversified, everyone on your distributed sales force seems to be running a different version of your internal sales automation tool. You have recently lost out on some big deals based on discrepancies in customer and prospect data across different sales teams. You decide that implementing a SaaS solution to run your sales automation will ensure that all members of the sales team have consistent and accurate information when they need it (see Chapter 6 for more on SaaS).

Scaled application service: Running your e-mail system requires more and more servers and lots of system administration time spent on maintenance and upgrades. You decide that a massively scaled application service in the cloud will deliver the performance you require and allow you to move the skilled administration team to focus on other projects.

In the next few sections, you take a look at each of these scenarios from an economic perspective.

Filling the need for capacity

Some pragmatic workloads fit perfectly into the Infrastructure as a Service (IaaS) model. These include basic computing services to support unexpected workloads or test and development requirements.

Considering IaaS for workloads that are outside the normal day-to-day operations makes sense for these reasons:

Building out a full infrastructure for these unpredictable requirements isn’t economical. An organization would have to purchase much more capacity than is otherwise required. Given that these resources would be dramatically underutilized, this approach doesn’t make fiscal sense.

Being able to procure a resource when it’s needed streamlines planning and allows for much faster go-to market models. The IT staff can be more conservative in projecting requirements knowing that if needs expand, it will be able to respond to those changing needs in real time.

So an IaaS model is an economic choice because organizations can access what they need right away, without having to buy new hardware or go through the long process of manual provisioning. In practical terms, this means you must consider the following:

Software evaluation: Testing new software is both a cumbersome and a long-lived process. Typically, developers need to acquire servers and specialized development software. Although this is a necessary process, it doesn’t add to the bottom line of revenue. It can be time-consuming and expensive to evaluate new software. If that software is available as a service, an organization is more likely to try innovative software because it can quickly evaluate it.

System testing: Similar to software evaluation, resources are required for a relatively short time when testing a system. Despite this, testers typically want to own their own resources, which isn’t cost-effective because they will sit idle most of the time. In addition, if someone is testing a fast-growing workload, he has to spend much more money to achieve the same thing than he could via a service for a fraction of the cost. Testing as a service also means that the IT organization can test for situations that cannot be easily replicated within the data center.

Seasonal or peak loading: Some companies are already using IaaS for cloudbursting when there are unexpected or planned high-load periods. The flexibility of using IaaS means that the company doesn’t have to overinvest in hardware. These companies must be able to adapt to higher loads to protect themselves.

Selecting a SaaS for common applications

Not all SaaS applications are the same in terms of costs to the organization. If the application is fairly independent of the overall applications and information environment of the company, SaaS is a tactical and pragmatic approach. Also, because many SaaS vendors make their application programming interfaces (APIs) available to other vendors and customers, they are able to work in conjunction with third-party SaaS offerings or on-premises offerings. Moreover, SaaS has enormous benefits for organizations that don’t want to support their own hardware and support environment.

Reducing your hardware requirements may seem like an obvious benefit, but you may not have considered the additional economic benefit that accrues based on reductions in support and maintenance after you cut back on infrastructure. For example, when you implement a SaaS application, you shift the responsibility of managing new versions and updates to the SaaS provider. You can realize many economic benefits as a result of this shift:

You can cut back on IT staff or reposition members of the team to other projects.

End-user productivity improves with a SaaS model that is consistent with more frequent application and seamless upgrades.

Improved data accuracy based on the consistency and improved automation and availability of the SaaS solution.

However, one of the economic implications of SaaS is that they can create even more silos of applications and data in the IT organization. It is, therefore, important to evaluate the SaaS approach and choices based on how well Software as a Service needs to be integrated with other applications — both in the cloud and in the data center. A SaaS application can easily lead to even more costs if the IT organization has to go back and rearchitect the integration between various on-premises and cloud applications.

Selecting a massively scaled application

Some of the earliest cloud adopters are large companies that wanted to take a massively scaled application (such as e-mail) and put it into a cloud. Companies are finding that approach to be a more cost-effective approach. In essence, this is the type of cloud application where the economics can’t be matched by the data center. When applications support this type of massively scaled infrastructure, the cloud will often win out. Because massively scaled applications such as e-mail and social media are relatively simple, a vendor can easily standardize and optimize a platform, making it cost-effective to support vast numbers of users at a low cost. By taking advantage of the economies of scale in cloud environments, a massively scaled application is a win-win in the cloud.

When it’s not black and white

Not all situations are clear-cut. Accurately forecasting the economics of the cloud can be complicated. The problem for many organizations is that they don’t have an accurate picture of data center costs that allows them to consider cloud propositions on an apples-to-apples basis. For example, because companies pay per user per month for a typical SaaS application, the costs over time may appear to be greater than the costs of owning an application outright. The same argument could be made about IaaS services where the customer pays for a unit of work by volume or time. However, it’s important to consider the flexibility and agility of the organization to change based on the needs of customers and partners. Some companies are willing to increase their operating costs in exchange for reducing their capital expenses because it gives them long-term flexibility.