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Unpacking the Pay-As-You-Grow Storage Model

Posted by Courtney Pallotta on 4/4/17 9:00 AM

pay-as-you-grow

There are many things that, on the surface, seem like prime candidates for the “do it yourself” model. For example, it used to be that if your car needed an oil change, you could put on your gloves, buy some motor oil and get cracking. It was easy enough, and you’d save some money.

As cars have gotten more complex, and other options have become better and less expensive, doing it yourself doesn’t make sense anymore. You can now take your car to a drive-through shop and get your oil changed for less than you would spend on the oil alone, and you never have to get out of your car.

This logic applies to managing expensive pools of data, as well. With the complexity involved in today’s environments, and the sheer amount of data companies generate, there’s a better way to manage all your data needs: plug into a service that offers the performance, low latency and availability of the hybrid cloud, with a managed pay as you grow, on-demand consumption model.

To better understand exactly how this model can help your organization become more efficient in managing data, it helps to unpack each term individually. So, let’s take a look at just how “managed,” and “pay as you grow” can be beneficial options.

Pay as you grow

Enterprises have become accustomed to buying storage well in advance of what they need, planning for their undetermined growth – and paying for it in advance. That model is obsolete. In today’s on-demand model, as the amount of data you have increases, you can increase your storage on-demand, as simply as clicking a button. You never buy more than you need, you can elastically scale up or down on demand and pay only for what you use. This elasticity is impossible in the traditional CAPEX model, where you pay in advance for excess storage capacity waiting to be filled. Freed from the hardware provisioning cycle, your IT department can focus on what really matters, supporting your business.

Managed

 “Managed” is the base of the model. Having a provider manage everything is what allows for all the savings and flexibility outlined above. Let’s look at the specific areas of backup and disaster recovery as a use case. These tasks used to require organizations to set up a secondary location – if your primary data center was compromised, or you faced a disaster and went down, you could utilize that secondary location. 

The problems with this are many. First, you have to purchase, set up and manage this location. Second, you have to test it thoroughly and almost continuously. Third, what happens if the secondary location goes down? Utilizing a managed service for disaster recovery and backup removes these redundant costs and potential pitfalls. You pay monthly, your disaster recovery and backup is handled automatically and a single, durable copy of your data is available anywhere – on prem or in the cloud.

From the scenarios we’ve laid out here, it’s clear that it would be exceedingly difficult to purchase exactly the right amount of storage, manage it efficiently and avoid surprises. Even if you did live in this perfect world, pay-as-you-grow storage would still be less expensive. Take advantage of our knowledge, economies of scale and flexibility to take the guesswork out of your data storage, and save some money along the way.   

Click here to learn more about how our fully managed service makes all data accessible, no matter where it resides.

Topics: Bringing the Cloud to Primary Data , Backup & DR

About the Author

Courtney Pallotta

Courtney is a marketing professional and entrepreneur with experience in go-to-market strategy, product marketing, demand generation and client advocacy. She has a passion for authentic marketing centered around creating communities of users that share ideas, adopt and advocate for capabilities that make them successful.

As a founding Netezza marketing team member, she built the demand generation and client advocacy programs and led these teams from launch through IPO and acquisition by IBM. At IBM, Courtney led two acquisitions, grew the big data and analytics portfolio and launched the marketing of the data science and open source initiative for IBM Analytics. At ClearSky, Courtney leads the marketing team and is focused on digital demand generation, deep client understanding and advocacy as key measures of go-to-market success.

Courtney enjoys spending time outdoors with her children and husband running, hiking and skiing.

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