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Cisco : Consumption models helping to enable business transformation

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10/06/2017 | 02:10am CET

Cisco Blog > Executive Platform

Executive Platform Frank Palumbo - October 5, 2017 - 0 Comments

Co-Author: Larry Scherzer, Director, Financial Product Development

Digital transformation is really about business transformation and is driving the need for new consumption models. Gartner reports that 47 percent of CEOs are being challenged by their board of directors to make progress in digital business. The demands are becoming more diverse and complex as new business applications and advanced technologies emerge such as artificial intelligence, cloud-as-a-service, IoT, machine learning, and mobility.

In our fast-paced, multicloud world of digital, how can IT drive business outcomes? Specifically, is it possible for data center professionals to balance the need to rapidly scale and accelerate technology adoption while managing IT spending?

This is where a consumption payment model can help.

'Organizations can prepare for (or get ahead of) change by starting data-driven discussions around flexible payment options - opening up opportunities to rapidly scale and accelerate technology adoption.'
- Kristine A. Snow, President, Cisco Capital
Read more

Buy the car? Or Uber?

Imagine the differences between owning a car and hiring an Uber for your transportation needs.

If you plan to do a lot of driving, then owning a car may make more financial sense. If you stick close to home or live in a big city, taking an Uber or taxi and paying for transportation as you need it may be more cost effective. There are pros and cons to each method of transportation.

Cisco's Open Pay consumption model provides a hybrid-like experience, similar to owning a car and having the flexibility of hailing a cab at a moment's notice. Consumption models can help organizations better align technology investments with operational and financial objectives through an on-premise cloud-like experience.

The ability to provide capacity on-demand to support seasonal spikes or rapid shifts is a huge obstacle from a technological perspective. Consumption models allow for greater agility and the ability to better match expenses with revenue.

Why Open Pay, and why now?

Unlike cloud offerings where 100 percent of the cost is variable, Open Pay enables flexibility at a more efficient cost by incorporating a fixed base-level component based on business needs and opportunities for growth.

For example, the financial impacts of a 70 percent fixed quarterly cost and a 30 percent variable cost, based on actual metered usage. The result is a financially flexible total solution.

Consider these additional benefits of Open Pay:

  • Improve velocity. Think HBO on the night of a big fight - there is no need for downtime. With Open Pay, businesses can go through the process of procuring and installing equipment all at once. Therefore, when there is a need for more equipment or storage, it is easily accessible.
  • Improve access to ongoing technology innovation. Open Pay is better than cash because it avoids the depreciation of equipment and allows customers to have the latest software and hardware for a predictable monthly fee.
  • Cloud experience with on premise security. With Open Pay, customers pay for their variable cost only when they need it, and their data stays on premise inside their own data centers. The result is paying for resources only when needed, at a more efficient cost and without housing sensitive data in the cloud.

See how Open Pay helps Capgemini deliver flexible consumption models to their customers and expand into new countries.

Fast track to innovation

As technology innovation accelerates at an exponential rate, organizations can leverage Open Pay as a means of not only exceeding their business objectives, but also propelling themselves ahead of the competition.

Click here to learn more about what consumption models can do for your business.

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Cisco Systems Inc. published this content on 05 October 2017 and is solely responsible for the information contained herein.
Distributed by Public, unedited and unaltered, on 06 October 2017 00:09:06 UTC.

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