Moving to the Public Cloud? Do the Math First

A manufacturing executive claims that many companies “didn’t do the math” in terms of rushing to outsource key functions to outside suppliers. Are companies making the same mistake in terms of rushing to public cloud computing infrastructures?

Image courtesy of Flickr. By peachy177

The herd mentality—we know it well. Once a given topic (i.e. agile development, Hadoop “Big Data” implementation, etc.) becomes the darling of business and management publications, a gold rush usually follows to implement. Unfortunately, sometimes there isn’t much, or any, thought put into gauging enterprise fit or building a business case for the latest and most fashionable idea.

Take for example the concept of outsourcing. During the early 2000s, cheap labor rates in China and India caused senior managers to see dollar signs as they could cut labor costs nearly in half, while gaining a specialized workforce dedicated to developing and building products and/or servicing customers.

There was a catch however. When considering topics such as delivery lag times, transportation costs, loss of corporate agility, language and communication barriers and more, the so-called cost savings often failed to materialize.

“About 60% of the companies that offshored manufacturing didn’t really do the math,” says Harry Mosler, an MIT-trained engineer who runs the Reshoring Initiative. “They looked only at the labor rate, they didn’t look at the hidden costs.”

The concept of shifting compute needs to public cloud computing infrastructures is an idea gaining traction. As the C-suite contemplates methods to deliver better, respond to market changes faster and reduce costs, cloud is an increasingly tantalizing option. In fact, the market for public cloud computing is said to be $131B in 2013 and growing, according to a tier one analyst firm.

While companies are choosing cloud for myriad reasons, it’s readily apparent that procuring public infrastructure, development platforms or applications from a cloud provider is really just another form of outsourcing.

This then brings some challenges to the forefront, specifically the need to understand the business case and use cases for cloud computing for your own company. And the needs must go beyond simple cost savings analysis.

Don’t make the same mistakes of those executives who rushed to outsourcing in the past decade. Tally up the cost savings, but also spend time diagnosing “hidden risks” of public cloud in terms of well-known issues of costs of downtime/availability, data security/privacy in a multi-tenant environment and data latency.

In addition, think about the level of control you want over your IT infrastructure. Are you comfortable relying on another vendor for critical IT infrastructure needs? In case of the inevitable IT failure or worse case cyber-attack, are you one of those who would want to start working a problem right away, rather than opening a trouble ticket and waiting for an answer?

You’ll also need to consider skill sets (tally those you have, and those you’ll need), in addition to architecting your various workloads for cloud infrastructures.

Please don’t get me wrong. For many companies, sourcing computing needs to public infrastructures makes a lot of sense, but when only supported by a thorough business case, and detailed risk analysis.  You’ll need a thorough understanding of what you’re jumping into before “joining the herd,” especially when an on-premise solution might work better.

In other words, “do the math” (figuratively and literally).

Cloud Computing – More than Simply Cost Savings

In a very competitive macro-economic climate, companies seek to reduce costs and drive those savings towards either the bottom line or re-purpose savings towards innovative projects. Cloud computing is often seen as one such avenue towards cost reductions as companies can ultimately reduce capital expenditures and data center operating costs. However, as good as the concept of “cost savings” sounds, you might be surprised to discover that according to one analyst firm, cost reduction isn’t the primary driver for cloud computing.

Courtesy of Flickr. By M Hooper

Too many under-utilized data marts and application servers and too many wasted kilowatts. That’s what consortiums like Energy Star say as they report most corporate servers are only utilized 5-15% of the time. Besides wasted energy, companies are saddled with a poorly utilized asset that’s still costing precious IT dollars in maintenance and possibly software subscriptions.

Cloud computing, then, can often ride to the rescue in terms of creating shared pools of system resources via virtualization technologies. This in turn helps reduce the number of servers needed, slashes application licenses, and ultimately trims power and cooling costs.

While cost savings are no doubt important, a recent analyst survey cites the primary driver for CIOs to approve cloud computing are: “(delivering results to the business) better,” followed by “(delivering results to the business) faster.” Cost savings comes in a distant third.

That’s because global product lifecycles are speeding up as companies adjust to niche consumer demands, and more nimble/agile competitors.

For evidence of faster product lifecycles, see GE appliances. In an Atlantic magazine article, GE appliance manager Lou Lenzi says that a refrigerator model design was formerly good for at least 7 years before a complete product refresh was necessary. Now because of accelerated product cycles, models are only good for 2-3 years as customers regularly clamor for new features, colors, styles and models such as “$3000 smart refrigerators.”

Cloud computing can help companies respond to faster product cycles. With cloud, customer demands can be met, almost instantaneously because analytic resources for product and customer analysis are “at the ready” and can grow and/or shrink as business demands. No more waiting for capital budget refreshes, or IT to find cycles to accommodate immediate business needs. And best of all, these resources are generally available on a pay-per- use or subscription basis, so they’re easier to fund from OPEX budgets.

And user friendly cloud self-service options help enable business managers to create analytic development “laboratories” where they can carve out system resources to work on special projects, test out new theories, or collaborate with product, marketing and R&D teams on a global basis.

In short, cost savings are a very important aspect for CIOs, but not always the primary driver for cloud adoption. CIOs then, fundamentally seek cloud computing options to help them align more closely to business user demands and better meet customer needs.  Indeed, with shrinking product lifecycles, the ability to source immediate compute, storage and analytics—for faster business results—could mean the difference between an extremely profitable or disastrous quarterly result.

Better Capacity Management ABCs: “Always Be (Thinking) Cloud”

Sales personnel have a mantra, “ABC” or “Always Be Closing,” as a reminder to continually drive conversations to selling conclusions or move on. In a world where business conditions remain helter-skelter, traditional IT capacity management techniques are proving insufficient. It’s time to think different – or “ABC”: Always Be (Thinking) Cloud.

Getting more for your IT dollar is a smart strategy, but running your IT assets at the upper limits of utilization—without a plan to get extra and immediate capacity at a moment’s notice—isn’t so brainy. Let me explain why.

Courtesy of Flickr. By M Hooper

Author Nassim Taleb writes in his latest tome, “Anti-Fragility,” about how humans are often unprepared for randomness and thus fooled into believing that tomorrow will be much like today. He says we often expect linear outcomes in a complex and chaotic world, where responses and events are frequently not dished out in a straight line.

What exactly does this mean? Dr. Taleb often bemoans our pre-occupation with efficiency and optimization at the expense of reserving some “slack” in systems.

For example, he cites London’s Heathrow as one of the world’s most “over-optimized” airports. At Heathrow, when everything runs according to plan, planes depart on time and passengers are satisfied with airline travel. However, Dr. Taleb says that because of over-optimization, “the smallest disruption in Heathrow causes 10-15 hour delays.”

Bringing this back to the topic at hand, when a business runs its IT assets at continually high utilization rates it’s perceived as a beneficial and positive outcome. However, running systems at near 100% utilization offers little spare capacity or “slack” to respond to changing market conditions without affecting expectations (i.e. service levels) of existing users.

For example, in the analytics space, running data warehouse and BI servers at high utilization rates makes great business sense, until you realize that business needs constantly change: new users and new applications come online (often as mid-year requests), and data volumes continue to explode at an exponential pace. And we haven’t even yet mentioned corporate M&A activities, special projects from the C-suite, or unexpected bursts of product and sales activity. In a complex and evolving world, solely relying on statistical forecasts (i.e. linear or multiple linear regression analysis) isn’t going to cut it for capacity planning purposes.

On premises “capacity on demand” pricing models and/or cloud computing are possible panaceas for better reacting to business needs by bursting into extra compute, storage and analytic processing when needed. Access to cloud computing can definitely help “reduce the need to forecast” for traffic.

However, many businesses won’t have a plan in place, much less the capability or designed processes—at the ready—to access extra computing power or storage at a moment’s notice. In other words, many IT shops know “the cloud” is out there, but they have no idea how they’d access what they need without a whole lot of research and planning first. By then, the market opportunity may have passed.

Businesses must be ready to scale (where possible) to more capacity in minutes or hours—not days, weeks or months. This likely means having a cloud strategy in place, completion of vendor negotiation (if necessary), adaptable and agile business processes, identifying and architecting workloads for the cloud, and a tested “battle plan” so that when demands for extra resources filter in, you’re ready to respond to whatever the volatile marketplace requires.

Fifteen Articles on Cloud Computing

Let’s talk cloud computing. Some argue it’s nothing more than a re-dressed virtualization trend from the 1960s. Others say cloud is the same concept as mainframe “Timesharing” and that analysts and pundits should move along as there’s nothing new here. That’s pure hogwash.

Think of cloud as a service of computational power, storage and more, much like the service you’d get from a utility company. The cloud allows you to plug into a required capability—whether it’s for print servers or analytics. The cloud is typically available on a metered basis when demanded, and can be accessed via self-service methods–simply plug in via a portal and access what you need. And it’s delivered via a host of technologies, software, processes, devices and physical locations that power this “service”.

This collection includes articles on cloud computing concepts, trends, delivery models, risks and challenges with a special focus on cloud for analytics. Free download

Will Pay-Per-Use Pricing Become the Norm?

CIOs across the globe have embraced cloud computing for myriad reasons; however a key argument is cost savings. If a typical corporate server is utilized anywhere from 5-10% over the life of the asset, then it’s fair to argue the CIO paid ~10x too much for that asset (assuming full utilization). Thus to get better value,  a CIO then has two choices – embark on a server consolidation project—or use cloud computing models to access processing power and/or storage, when needed, on a metered basis.

Cloud computing isn’t the only place where utility based pricing is taking off. An article in the Financial Times shows how the use of “Big Data” in terms of volume, variability and velocity, is stoking a revolution in real-time, pay-per-use pricing models.

The FT article cites Progressive Insurance as an example. With the simple installation of a device that can measure driver speed, braking, location and other data points, Progressive can gather multiple data streams and compute a usage based pricing model for drivers that want to reduce premiums. For example, rates may vary depending on how hard a customer brakes, how “heavy they are on the accelerator”, or how many miles they drive.

The installed device works wirelessly to stream automobile data back to Progressive’s corporate headquarters, where billing computations take place in near real time.  Of course, the driver must be willing to embark upon such a pricing endeavor, and possibly lose some privacy freedoms, however this is often a small price to pay for the benefit of a pricing model that correlates safer driving habits with a lower insurance premium.

And this is just the tip of the iceberg. Going a step further to true utility based pricing, captured automobile data points also make it possible to create innovative pricing models based on other risk factors.

For example, if an insurance company decides it is riskier to drive to certain locales, or from 2am-5am, they can attach a “premium price” to those decisions, thus letting a driver choose their insurance rate.  Even more futuristic, it might be possible to be charged more or less based on discovery of how many passengers are driving with you!

Whether it is utility based pricing of electricity based on time of day, cloud computing, or even pay as you go insurance, with the explosion of “big data” and other technologies, it’s already possible to stream and collect various data, calculate a price and then bill a customer in a matter of minutes.  The key consideration will be consumer acceptance of such pricing models (considering various privacy tradeoffs) and adoption rates.

If the million “data collection” devices Progressive has installed are any indication, much less the general acceptance of utility priced cloud computing models, it appears we’ve embarked upon a journey in which it’s far too late to go back home.