I’m studying different help sites and applying web analytics. I wanted to write up some of the processes, potential wins, and possible short comings of web analytics for technical communication.

When I spoke with a few Google technical writers at the STC Summit, one of them confirmed that their performance reviews include a web analytics component. This concept got me thinking about help sites I’ve worked on and how well they’d stand that test. Or rather, how well my writing and information architecture would stand up to an investigation with web analytics data. I started looking at what I’d measure. I looked at sites I’ve monitored to find examples. I collected some ideas here.

What are the goals?

I believe tech pubs groups may serve different masters or several masters. Pre-sales or marketing goals are different from support goals, and training or education goals are different still. So you would pay more attention to different measures depending on your goals for the site. This section discusses customer support and preventing costs in the company caused by a support issue being filed. The goal here is preventing support calls.

In the Web Analytics Demystified book, Eric Peterson  points out a distinct difference in goals for a customer support site.

…while the other business models are driven financially by the idea that “more page views are usually better” the customer support model tends to be the opposite, the more quickly the visitor can find the information they are looking for the better.

I’d highly recommend these books for understanding how web analytics tie into business goals. The book has an entire section devoted to Customer Support sites.

For training sites, the goal would be for the person to spend time with the content, digest it, and meet training objectives because they have learned the material fully. Time spent should be higher for training sites.

Another crucial difference between a support site visitor and a training site visitor is that for the support site visitor, you want to observe the behavior of new visitors, but for the training site visitor, you want to ensure retention and repeat visits. To put it simply, customer support deals with acquisition of visitors, training deals with retention of visitors.

Another business goal is conversion – converting site visitors to paying customers. A technical manual can assist with three main goals – acquisition, retention, and conversion.

Support cost deflection – what to measure?

If the site has a question and answer section, compare the page views of the FAQ or Q&A pages to the other pages in the site – are there more views and longer time spent on pages in the FAQ area? That might be a good sign to indicate the help site is deflecting support calls.

Set up a custom segment to look for pages that have troubleshooting or particular error messages in the title or page content. Next, look at the bounce rate for that segment compared to the rest of the site. You want the bounce rate for the troubleshooting topics to be lower than the overall bounce rate. You want the trend for bounce rate for troubleshooting pages to stay the same or go lower over time. In other words, if visitors do not spend any time reading the troubleshooting information you’ve provided, what can you do to improve the content to prevent a visitor from leaving (bouncing)? This screenshot shows an example of a lower bounce rate for the troubleshooting segment of pages compared to all pages, which you want to maintain if your goal is to help users troubleshoot independently.

Pay attention to the pages that have the highest rate of exit, the page that most people leave the site after viewing. But also look at the percentage of exits. I have seen great pages on tech comm sites that have 9% exit rates but the highest number of page views. One explanation is that non-users found them through organic searches and the entire site wasn’t what they expected, not just that page.

What is the responsiveness of people to updates or comments on the site? If it offers comments, how much time elapses between a comment and a corresponding response? Do the questions get answered by a company representative or by another user? Not all web analytics packages will offer this measurement so you may have to do a sampling yourself. You can also try to get a sense of cadence, the rate of comments per day or per week or per month.

Analyzing searches will go a long way towards understanding whether user’s needs are being met by the help site. Look for searches that have zero yield – that is, the user didn’t click through on any hit or no results appeared at all. You can also look at the search results to site exits ratio.

Watch first time visitors‘ data like a hawk. New visitors may struggle at first while they learn their way around the site. As the number of first time visitors goes up, support call volume may also increase if visitors can’t find what they need or if they find the call-in number quickly. A good method for tracking real-world data along with web site data is to use a special phone number displayed only on the online help site, so that you know only those people who found the page with the special phone number can call it.

You can set up a visualization funnel from the home page to the support site to specific information to generate what Eric Peterson calls the “Information Find” conversion rate. For example, consider a flow of visits to the home page, to a product page, to a list of commonly asked questions, to a page containing a specific answer to a question. You can track travel through this series of pages, measure abandonment along the path and track a conversion as a certain amount of time spent on the final answer page.

Potential problems

Limited data will certainly make it harder to provide a convincing, statistically-significant analysis. One of the problems I foresee with applying web analytics on technical documentation sites is the small number of page views per day. I recently analyzed a help site that had about 40-50 page views per day on weekdays, pretty consistently. That was a software-as-a-service product available online. Another site I’ve watched for more than a year consistently gets 200-300 page views on weekdays and just under 100 page views on weekend days. I hear (don’t have an official citation to point to) that the 10k visitors per month is a common benchmark for starting to pay attention to web analytics. Do we get much value or accuracy from analysis if our sites don’t get to that point? I think it’s okay to monitor but to recognize your data may not have the clout you’d like it to.

Benchmarks to compare your site to would be valuable, but there aren’t categories as specific as “help site for consumer gadget” or “help site for enterprise software” yet.

Site search analytics, while most valuable to us, may be harder to enable unless you use specific tools. Site search analysis shows you what users look for, whether they find anything, and the path they take after clicking on a result link. Search analytics focused only on your tech comm or online help site require you to use a Google Custom Search Engine or the MindTouch 2010 platform which has site search analytics built into their reporting system. It appears that Adobe RoboHelp Server Analytics offers the ability to see what users search for but I don’t know the depth of analysis beyond keywords.

Connecting to the greater web analytics group at your company may be a challenge. Google Analytics is the free offering, so I expect it would have the highest uptake in tech comm in the beginning. Also, tech pubs departments aren’t usually tied in to the web content management systems so Omniture or Coremetrics (now owned by IBM) which are two other web metrics tools may not gather data on a tech comm site.

One takeaway from the Web Analytics Demystified book that makes a lot of sense is to always ask, “Is the information actionable?” In other words, when deciding which metrics to watch, make sure you can do something about the resulting metrics, whether you make changes to content or dive more deeply into the metrics. In certain environments, actionable items could be problematic if politics or tools get in the way.

Definitions to Understand

It’s important to get a handle on the basics of web analytics. I appreciate Avanish Kaushik’s blog, Occam’s Razor, for learning about web analytics and the definitions that are so important to understand. What are visitors and what are visits, and what are clicks and what are views? What’s the difference between clicks, visits, visitors, pageviews, and unique pageviews? is a Google Analytics help topic that explains these well. I also pursued and attained a Google Analytics Individual Certification which was extremely valuable for understanding definitions and the mechanics of web analytics.

Sources

I refer to a great article by Rachel Potts titled, What can web analytics do for technical communications? that she wrote for the ISTC’s Communicator magazine last year.

I downloaded and devoured two web analytics books from John Lovett and Eric T. Peterson on their site at www.webanalyticsdemystified.com. The titles are available for a free (give them an email address) download, Web Analytics Demystified and The Big Book of Key Performance Indicators.

Last spring I wrote up a blog entry pointing out some additional technical documentation wikis to add to a list I had in my “Wiki-fy Your Doc Set” presentation. A recent Twitter request asking for technical documentation wiki examples brings me back to both lists to try to compile an even longer, more updated list. These are in no particular order and the links were tested in August 2010. Other wikis are behind support logins but this list offers wikis that can be viewed without a login.

• All of the manuals on FLOSS Manuals site are authored and displayed in a customized Twiki wiki.
• Sun Microsystems (now Oracle) has many documentation wikis. The OpenDS Wiki offers a nice example. Also all the OpenOffice documentation is available on a wiki.
• Mozilla Developer Network
• Adobe Labs wiki
• Splunk product documentation
• Atlassian product documentation, specifically the Confluence documentation
• OpenCloud
• GigaSpaces
• Firefox Support Knowledgebase
• Second Life In fact, they single-source their embedded online help with the wiki as source.
• Ubuntu
• RightScale
• ExactTarget
• IBM developerWorks Wiki
• The MSDN Library from Microsoft offers many wiki-like features.
• Embarcadero RAD Studio wiki is actually 9 wikis, read the manager’s blog entry about it.
• WebWorks Documentation Wiki
• Tideway (now BMC Atrium Discovery) documentation

It’s no wonder I have to keep creating new lists. The examples are constantly changing. For example, the Facebook Developer wiki is being moved to another site.

Finally, if you are considering a wiki for technical documentation, I recommend reading my post, Hurdles and Hardships using Wikis for Documentation, reading Sarah Maddox’s blog, buying my book, and sharing your experiences with others. Here’s to enjoying the wiki journey.

I’m pondering the 1998 article, Pages Must Live Forever (from Jakob Nielson’s Alertbox) while documenting the content aging report in MindTouch 2010 (Read the spec here, read the user guide here).

With redirects helping stave off link rot, it seems that we can fulfill the wish behind Kristina Halvorson’s plea not to allow the web become like the junk-filled planet in Wall-E. Instead of piling up old versions of pages, the links stay fresh while the content might age a bit, like a fine wine.

For help content, I can list reasons that older content might be just fine, no need to send off alarms.

• Software that has classic features that were well documented in the first place, those pages can be static.
• Pages that haven’t been updated but are still oft-visited I would consider to be fresh, not stale. As long as the comments don’t indicate a problem with the content, it can be considered fresh.
• Depending on how well it’s resourced or energetic it is, your writing staff and community can only add a finite amount of content per week (or month). So the percentage of old content may be higher than the percentage of new content. That ratio is probably okay as your site ages. The mark the report sets is two years (24 months), then the content might be “old.”
• Depending on the scope of the aging report, an older product would have older help pages. Filtering helps you tune in the grouping of pages where you might be concerned about stale pages.

Two years would be a long time in a web application’s life, but perhaps not so long for an enterprise application. As usual, the answer to “Must Help Pages Live Forever?” is “It depends.” The real question that I’m trying to answer is “When are Help Pages Stale?” I believe two years is a valid and reasonable line to draw. What do you think?

Today’s Technology Tools and Social Media for Growing Business was the title, and this session was the last one for Tech Fair, and the Panelists and their company names form an impressive lineup:

Doug Whatley – Human Capital Consultant, moderator

• Scott Ingram – Sales Director, Bazaarvoice / Founder, Network-In-Austin
• Julie Niehoff – ConstantContact
• Dave Evans – Principal, Social Web Strategies
• David Armistead – Principal, Social Web Strategies
• Britton Manasco – Manasco Marketing Partners

After a development manager from Paypal gave a great inspirational talk about how the inflection point for electricity’s usefulness came after an electrical washing machine was invented, bringing the outlet from the ceiling to the wall, Doug Whatley introduced the panelists. Each panelist talked briefly about their perspectives on social business. Here are some notes from the valuable session with interesting insights. I mostly wrote down quotes that I found insightful or that offered a perspective I hadn’t heard.

Britton sums up the social technologies that enable businesses to get work done with a simple phrase: Guidance imperative – the ability to provide expertise, advice, coaching, to be a trusted authority and advisor in the marketplace. He says, social media outlets amplify our ability as authorities – helps us personally brand ourselves. Helps us target and reach prospects.

David Armistead says his perspective is that the C-suite is grid locked on what social media is and what it can do. He typically explains it as, social tech does 2 things well – lower cost of communication and lower cost of coordination. There’s a lot of work to do in comprehending the change and effecting the change – transforming the way we work. No piece of the org will not be affected. We are not “messaging’ any longer, taking a known message through the structures in place – we’re talking.

Dave Evans has been working with groups in Argentina, Netherlands, US and Canada. He observes that 15-45-year olds are doing the SAME things with social tech across all four countries. The other countries just need time to catch up. For example, in India, the 3G spectrum bidding has started, and will enable much more mobile technology. Dave also sees opportunities for businesses monitoring the Social Graph – using TweetDeck and BuzzStream in combination he can monitor microscopic conversations.

One example of business tie-ins with people’s social graph – Social Web Strategies uses the LinkedIn API to build a specific landing page on the 2020 social site that shows C-level people who visits their site a page that shows anyone in their 1st tier who has used their social site. He said later that this is their highest converting landing page. This is B2B lead generation, folks.

Dave also says, this shift affects the whole organization – not just marketing. His example – marketing can’t possibly respond directly to comparison of carbon footprint of products. Basically, consumers can take out their smart phone and scan the barcodes of products while shopping to find out carbon foot print and make your purchasing decision based on the data. That type of decision isn’t made due to marketing or sales team’s efforts.

And next, Julie Niehoff, a development manager at Constant Contact, spoke on her perspective as an email marketing and list segmentation provider. They also acquired Nutshell mail this week, which caused a bit of excitement in the room. She reminds us all that you need a strategy first. Know what your objective is, try and test, then stop doing that when it doesn’t pay off. Loved hearing this as it validates my thoughts in this area. She said, always test just 10-20% of people on your list to try something new – don’t make them all dislike a new method or approach. Later she asked, “Can you segment your lists based on ‘raving fan/brand ambassador’ vs ‘kinda maybe’ vs ‘never gonna’? Do it. Treat brand ambassadors well.”

Another speaker noted that the gaming generation has gained a lot from the mentality that it’s always okay to hit reset and restart.

Where technology turnover is higher, adoption rates are higher. The pay offs are doing business faster, easier, or cheaper.

Scott Ingram spoke from BazaarVoice’s viewpoint, where one of their clients, Sephora, garners lots of reviews, such as 17,000 reviews on a base foundation. Goodness. He noted that people are starting to “live’ in Facebook. One of their customers has 20% of traffic from Facebook. Wow.

I think it was Scott who also said that Twitter is a relationship accelerant. It allows you to stage when you personally and physically interact with others. For example, sales people don’t get on planes until they know what people are looking for. Julie noted that there are also tools that actually mask the relationship – ratings and reviews set the rhythm of the relationship, but you can screw it up with the wrong copy.

The panel session had a question about privacy as the last question, though I think discussion continued after I departed. I liked David Armistead’s assertion that we mostly format questions around privacy when what we need to discuss is security. Julie had good points that there are laws around data collection and privacy policies and companies need to be governed accordingly. She also noted that as individuals we personally need to draw our own points of privacy in what we share online, read the policies before sharing info, and prevent certain connections in order to prevent correlation of data for those people under 13, for example.

What a great session – I could tell many people in the room were learning, taking notes, and nodding in agreement. Thanks to Matt Genovese for starting Door64, to Paypal for sponsoring the session, and to the presenters for sharing their valuable insights.

I did it! I passed and received my individual qualification (IQ) for Google Analytics. Hurray! And Whew!

The site offers a way to look up people who have passed their IQ test so you can verify if someone has it (here’s mine). I wouldn’t call myself an expert yet, since I think expertise comes with more and more experience. The test itself had well-worded questions, and you need 80% correct out of 70 questions.

I got 81% correct (hence, the whew). I double-checked all my answers, and if I didn’t know an answer for certain, I looked up information either in the Conversion University site or the Google Analytics help site.

With 90 minutes to take the test, my look-up-to-verify method would not have worked for all the questions, and I had to be quite familiar with the University lessons in order to verify what I needed quickly. I wish I could find out more about my incorrect answers. Apparently I need to work more on ecommerce, which makes sense since I’ve never run an ecommerce site so I don’t have hands-on experience with one.

Why pay for an individual qualification? Avinish Kaushik has an excellent post where he says for every $100 you invest in web analytics, you should spend $10 on tools and $90 on people with the brain power to think about the results from the tools. So for me, it made sense to test my brain power on a tool, but I realize that each site needs its own analyst behind it to choose the measurements and connect the site to the business.

If there ever could be a Cliff’s Notes for the wiki chapter of my book, I think I’m writing it now. I’ve been working on a great project with MindTouch. I visited them for a focus group with other technical communicators and technical support pros back in February in San Diego. We had open source community documentation represented, we had the health information industry represented, cloud computing, and high tech software writers, Agile writers, and document collaborators. It was a great time, discussing tips, tricks, and the trials of managing lots of content with specific purposes in mind such as learning, education, customer support, technical support, and internal collaboration. The write-up for how to run a focus group of this type is quite good – see Seek Omega: How to hold a Professional Focus Group that Produces Quantifiable Results.

After such a great session, we all continue to talk online thanks to one of the members setting up a LinkedIn Group, and MindTouch also invited us to work on a project to write up specifications for using wikis for technical documentation. We’re basically creating best practices using wikis for documentation, such as:

• templates, such as DITA’s concept/task/reference as well as FAQ and solution guidance through multiple tasks
• tags for workflow, assigning tasks, editing, and categorizing pages
• content collection and curation
• reports that assist with content curation and community documentation

I’ve been circling back with the members of the focus group while I write these specs, and working with Steve Bjorg, the CTO for MindTouch. His attitude towards development is,  create something with a sense of openess and collaborate with users as early as you can. It’s a refreshing way to make software. He describes these first go-rounds as the “Cliff’s Notes” for creating technical documentation with wikis. It’s not as robust as other solutions yet, but it sure does have features that are exciting glimpses at the future of documentation. I’ll post more in the coming weeks and months as we round out the features.

After a Q&A on the Facebook discussion page for my book, Sarah Maddox and I had an additional email exchange talking about the difficulties people face when using wikis for documentation.

I believe that many wikis are in the range of “content management systems” or moving in that direction. But there are many difficulties in general with content management. Here are some areas I’ve heard from fellow technical writers:

Access control: Without being able to say who can view or edit what, some wikis are impossible to apply to tech doc due to serving specific business reasons with the content. A customer support article should not be subjected to multiple edits from wiki spammers.

Hierarchy: Without at least 2 levels of hierarchy, tech writers are stymied as to how to use a wiki without hierarchy. Of course. We have complex documentation sets to maintain and hierarchy is a natural way to organize topics.

Version control: The difficulty in maintaining or tracking several versions of a bunch of topics (or an entire namespace/space) to correlate with a software release version is frustrating to many – I’ve heard this is a basic problem for WordPress’s Codex.

Global search and replace – and don’t forget spell check: Writers are used to maintaining giant Framemaker docs where they could spell check and search and replace across large amounts of content. CMSes and wikis don’t make that so easy as before.

Search on the site itself: We’ve all become so spoiled by Google’s search algorithms that any local search engine usually comes up short.

Workflow: Wikis can be weak in workflow, even as simple as “approve or reject” a particular article.

Creating collections: More than just outputting to PDF, people want to single source from a wiki to create collections of articles based on tags, categories, labels, and so on.

Offline access: Many wikis think they’re the end destination for readers, but the classic scenario is “what do my readers do if they need to get on a plane?” One clever solution to this problem would be to offer the wiki on a USB stick – call it a wikiscicle.

Round tripping: Writers are always talking about roundtripping content. I’ve usually dismissed it as not worth the trouble – there wouldn’t be enough contributions that a team of writers couldn’t keep up with. I’ve finally heard a decent business case for doing so – from structured XML (DITA) contained in a CMS to wiki and back again. Translation (to 22 languages) and volume of edits or contributions are the key to this scenario.

One-click publishing (batch processing): On release day, you want to set all topics to released at once, but with many wikis, you have to go to each page one-at-a-time to click them over to the right state for release.

With plugins and advanced wiki engines, these hurdles are easily overcome. But Mediawiki, a popular wiki engine, flunks the first two tests that many technical writers would apply. These are the examples I’ve seen and some of what Sarah has experienced. How does it match up with your viewpoint?

I finally did it, I wrote my first article for the Demand Studios content farm site, eHow. I wasn’t playing the part of a content farmer, though, but rather a farm worker, writing an article for little pay (compared to other rates I have earned as a professional writer).

I signed up for Demand Studios a few months back. There is a company called Pluck here in Austin that was acquired by Demand Media in the spring of 2008. What drew me to them in particular was not only the local connection, but also a fascination with turning search engine optimization on its ear. I first learned of these methods for content creation from this Wired article, The Answer Factory: Demand Media and the Fast, Disposable, and Profitable as Hell Media Model. Basically Demand Studios analyzes what phrases are searched for and then enter an article request in their database. There are currently 15,000 articles waiting to be written in their system. The pay for those articles is from $15 to $7.50 or less, and there are some assignments that offer profit sharing based on the numbers of views, apparently.

As a pro writer, I was dead set on following the style guide, knowing that attentiveness to the guidance given is part of the battle in producing good content. In their system, when I “Claimed” the article, it wasn’t immediately apparent which template I would be writing to, which made me a little nervous about attempting it in the first place. After clicking the article to claim it, though, I found that it was the About template. The guidelines were very clear – the About type required five sections with one-word section headers and the first section had to be titled Overview and contain about 75 words. The rest of the sections could contain more than 75 words but at least 50 words were necessary, and overall the article was targeted for 400-500 words. Quite structured.

The web-based authoring forms were easy to use, though it did not include a word count. I found it easier to get word counts in Textpad and then copy/paste the text into each section.

They’ve also very recently introduced an image library that you can search for images, the use of which is encouraged. You could add an image to each of the sections if you wanted. To my poorly trained eye, they seemed adequate but not too glossy, and none of my searches found quite the perfect image, but I included two anyway. They intend to allow people to upload their own photos, which I would have done in a heartbeat as I had one or two that would have been just right.

To my relief, the article I submitted by noon on a week day was approved by early morning the next week day.

It took me about 2.5 hours to write a 500 word article, I’m not proud to admit (or perhaps I should be proud of the quality that comes at that speed?) So my hourly rate for the article was right around $6.00 per hour. At least I didn’t have rewrites (she says sheepishly.)

To reflect back, I did the article because I wanted to see what the authoring system was like, and experience for myself the process of writing in such a system. To be sure, it’s easy to demonize such a system when you’re accustomed to higher pay for content creation. There’s a great interview on ReadWriteWeb by Jay Rosen, who talked with Demand Media founder and CEO Richard Rosenblatt, and it offers both sides of the issues surrounding content collection and the future of the web. I don’t want to take sides by sharing my experience. I just wanted to collect information based on the writer’s experience.

What do you think? Are content farms cluttering the web and driving down writer’s pay? Or is there an entrepreneurial opportunity here that offers a low barrier to entry for content creators any where to earn pay for  populating the web with content that’s already being searched for?

I’ve published a long-ish article on the WritersUA site where I describe techniques for user assistance that let the user participate. It starts with simple techniques such as comments and moves towards community documentation efforts. Please read and share Putting the User in User Assistance. I’d love to hear what you think about these techniques.

One of my readers asked for a post about cloud computing. I went straight to my in-Austin expert, Ynema Mangum, and she exceeded my expectations by writing the post! This is a guest post by Ynema Mangum, architect at Hewlett-Packard. She contributed information about web metrics to my book, Conversation and Community: The Social Web for Documentation. She’s working on a chapter for the upcoming book Cloud Computing: Principles and Paradigms. I’ll post a second guest post from Ynema next week.

Cloud computing represents a paradigm shift from traditional IT rooted in heavy process and technology-centric management to agile processes and service-centric management. This shift converges with Web 2.0 and distributed application design, resulting in democratized computing and an economic revolution — where the developer can deploy enterprise grade applications and user services without having to pay the capital expense for the underlying IT infrastructure. It represents a radical change and requires a culture shift for IT when building a private cloud.

Today, confusion exists about exactly what cloud is as well as how it compares to current IT methods and technologies. Clearing the air is the first order of business.

Public Cloud vs. Private Cloud

The public cloud model has is vastly different from the private cloud, creating a chasm in their connection. The current expectation for public cloud infrastructure and platform services is the ability to provision compute, storage, database and networking resources in a few minutes, completely online without establishing an agreement or talking to a person.

Private cloud computing has different challenges for the service provider, but often is faced with the same expectations. Regulatory compliance, security, and privacy are just the icing on the cake. The concern that seems most often forgotten in comparing public and private cloud models is quality and compliance of data.

Public cloud providers, in general, do not care what type of application or data you throw on the cloud. Compare that with an enterprise private cloud, where IT not only owns the performance and availability of the organizational assets, but also has responsibility for ensuring that business assets are used in the proper manner. Applications that are developed and deployed on a private cloud need to go through a series of quick checks before they can be cleared in order to prevent misuse of company assets or the risk of retrofit and ground-up redesign of applications developed outside of IT.

There is an ongoing challenge in enterprises today to segment cloud service offerings, architectures and buyer types into useful, focused categories for strategic planning, according to Frank Gillett of Forrester. For public cloud service providers, two IaaS market categories have emerged, the software Platform as a Service (PaaS) and virtual Infrastructure as a Service (IaaS) offerings that differ by level of infrastructure service and abstraction offered.

For private clouds, there are two types of compute clouds, server clouds and scale-out clouds.

• Server clouds are built for the traditional needs of the business applications, catalyzed by x86 server virtualization and adding self service provisioning.
• Scale-out clouds are designed for massive, highly distributed applications.

Virtualization vs. Cloud

Virtualization and cloud computing have much in common, including phrase overuse and hype, resulting in a lack of understanding of both. Cloud computing does not equal virtualization, but does use abstraction as a common element in each layer of the cloud. In fact, the most distinct differences between the two terms seem to be in the areas of abstraction and IT maturity.

Virtualization is datacenter-centric and technology-centric, while cloud computing is service and user-centric. Memory, desktops, applications, storage, applications, platforms, and servers can be virtualized, or abstracted from the underlying technology. Cloud computing can use or not use virtualization in its architecture.

Typically, the virtualization referred to for use in cloud computing is operating-system virtualization, where multiple virtualized machines can run on a physical server, secure and isolated from one another. These VMs provide benefits in that they can be provisioned without requesting physical hardware, changed, moved, controlled, terminated, and configured more easily than a physical machine. This results in greater efficiencies and productivity in IT, and also increases agility for the services developed and deployed on these VMs.

Beyond this layer of virtualization, cloud computing adds platforms, agile processes, and services for developers, providing value far beyond virtualization.

Utility Computing vs. Cloud

Utility computing is a business or economic model, whereas cloud computing is about technology and process architecture. Utility computing allows users to receive computing resources and “pay by consumption”. Cloud computing is a much broader concept, taking into consideration the underlying architecture and actual services delivered.

Consumer users have been reaping the benefits of the utility model in cloud computing for years — at the application as a service level. It is developers and IT who are using cloud computing in a transformative way now. IaaS and PaaS allows them to develop, test, deploy and run apps that can scale on enterprise grade technology, all without having to pay the capital expense for the underlying infrastructure. This is creating a new cloud economy and truly represents the democratization of computing.