“Only the paranoid survive”

– Former Intel CEO Andy Grove

Things seem so good in the enterprise technology universe right now it is a little scary. The IT transition to cloud/mobile/social has entrepreneurs and investors salivating, with three significant forces at work:

• Real, not PowerPoint, multi-billion dollar opportunities are emerging for new entrants
• IT buyers have new productivity options for workers and better values for their operating and capital spending
•  Incumbents are getting needed wake up calls on their technologies and business models.

So why am I nervous?

The Detroit Syndrome

Forty years ago, the Middle East oil embargo ripped a hole the size of a Buick through the American automotive industry. Over the following decades, the Big 3 automakers suffered huge market share losses, two major bankruptcies and government bailouts. Smaller, cheaper and more fuel-efficient cars from Japan broke the Detroit oligopoly and proved not everyone wanted big, powerful sedans, powered by a Detroit’s V8s.

As David Halberstam documented in The Reckoning, hubris and management blindness led to an enormous transfer of value from American to international car manufacturers. In 2012, the American auto industry had a $140 billion trade deficit. That’s like Apple losing everything.

Tech Drives Today’s Economy

Today, tech is a huge growth driver of the U.S. economy. Millions of jobs – not just in Silicon Valley but across the U.S. – directly depend on it. And that’s just the beginning: Enrico Moretti of UC Berkeley estimates that 1 job in traditional manufacturing generates 1.6 additional jobs, but one job in tech generates closer to 5 incremental jobs.

But the last heyday of enterprise IT crested around 2000, as “CIO as rock star” gave way to “CIO as cost center.” IT spending in the developed world has been basically flat ever since.

5 Challenges For Enterprise Technology

And now, enterprise technology faces an incredibly complex series of challenges and opportunities:

1. Customer Collaboration is Reducing Switching Costs. The IT industry has long relied on vendor-led standards bodies (IETF, IEEE) to ensure interoperability, but the dramatic growth of customer-led collaborative efforts in the open source and cloud movements is reworking the playing field. Rackspace-led OpenStack and Facebook-led Open Compute initiatives are cookbooks for the commoditization of IT infrastructure. If low-cost producers take over the infrastructure business, they will likely come from offshore producers (China, India, etc.) and not U.S. manufacturers. And don’t forget GitHub if you are in the software business.

2. The Cloud and the Rental Economy. The tremendous cost, energy, speed and operational savings presented by Cloud and SaaS technologies are changing how we think about IT. Why buy from HP or Oracle when you can rent from Amazon or Workday? Why let capacity sit idle when you can pay for it as you need it? This is a good thing, as resources will be used more efficiently. But it poses challenges for enterprise tech vendors. “We are one or two amortization cycles away” from the coming drop-off of premise-based IT purchases, warns Lightspeed Ventures’ Tim Danford.

3. BYOD and BYOA. The verdict is in: IT managers are coping, sometimes kicking and screaming, with the influx of customer-purchased devices as corporate computing platforms (Bring Your Own Device). The next wave of mobile challenges will come from BYOA (Bring Your Own Applications), where applications like Evernote and Box replace popular Microsoft programs like Sharepoint or even the Office suite. The classic enterprise software license could be a few apps away from oblivion.

4. The Lean Vendor. User-led IT communities like Spiceworks are replacing how buyers learn about products and services. Tech marketing is becoming a content and education task, not a promotional activity. This is giving the high-touch (read high cost) sales and marketing models of traditional vendors a run for the money. Companies like Ubiquiti Networks are not only taking advantage of this new buying behavior, they are passing on their own lowered selling, general and administrative (SG&A) costs to customers in the form of great technology and user-friendly innovation at lower prices. As a CIO friend of mine once told me: “When you walk into a vendor’s offices and they’re nicer than your own, remember you paid for it.”

5. Distributed Computing Model. Current computing models are built around a central premise: a client (e.g., Microsoft) will talk to a server (x86, IBM) in a single location to process an application. If you own either end of the equation, you can exact enormous value. But the cloud architecture is massively distributed, apps might have to touch dozens of places to process everything, totally disrupting that vendorscape. More significantly, the new in-demand IT skills sets look less like a traditional Fortune 500 corporation and more like Google or Facebook. Distributed computing scientists are this generation’s Einsteins.

3 Ways To Save Enterprise Tech

In Silicon Valley, it’s easy to assume the next generation of giants will grow just down the street. But the rest of the world is working to take advantage of the same trends while eyeing the large and relatively wide-open U.S. market.

I for one do not want to wake up in a decade and buy a book charting the downfall of the American technology industry by the next David Halberstam. Here’s what has to happen for the enterprise technology industry to avoid The Detroit Syndrome:

1. Incumbents need to blow up their own business models before challengers do it for them. The first wave of Detroit’s reaction to the initial oil-shock resulted in half-baked responses like the Ford Pinto, Chevy Vega and AMC Gremlin. And what did the auto industry do when oil prices moderated in the late 1980s and early 1990s? They went back to promoting horsepower instead of fuel efficiency and rolled out fleets of gas-guzzling SUVs that were great for short-term profits but made them even more vulnerable to the next oil shocks.

2. Some of today’s startups must grow into the new giants. While there are many enterprise tech vendors in $500 million to $5 billion range, few new suppliers have cracked the $10 billion run rate as independent companies. Large tech companies play a critical role in the IT economy, but customers must use their wallets to keep their own ecosystems healthy by fostering competition and innovations.

3. Systemic security and privacy solutions must be found. Buyer confidence could be torpedoed by cybercrime and careless data leakage. It will take a range of enabling technologies to give enterprise buyers more purchase confidence to embrace innovation.

Enterprise tech is not the Rust Belt, not by a long shot. There is every possibility that the technology industry will not go the way of the automobile industry. But the seeds of our growth could also be the seeds of our decay. And the ability to thrive requires innovations in our minds as much as our technologies. In the words of Mark Twain: “Circumstances make man, not man circumstances.”

Image courtesy of Shutterstock.

Guest author Derek Brown is a technology executive and analyst who blogs at One Blind Squirrel.

Android, it seems, is the worm that eats away at Apple's core.

According to Gartner, Android-based handsets outsold iOS-based handsets 4-to-1 on a worldwide basis in the first quarter of 2013, up from a ratio of about 2.5-to-1 in the same period of 2012. As such, Android accounted for 74% of global smartphone sales last quarter, up from 57% in the first quarter of 2012, while iOS accounted for just 18%, down from approximately 23% last year.

Apple's Strengths Irrelevant Going Forward

Apple bulls/fans (and even some critics) will likely race to highlight such defenses as:

• Apple didn't have a major new release last quarter.
• Tablet sales should be weighed in this discussion.
• The installed base of iOS devices should be taken into account.
• Developers still generate more revenue through iOS than Android.
• Apple continues to generate the majority of the industry's profit.

Blah. Blah. Blah.

Those points are all very true. Unfortunately for Apple, though, they're also largely irrelevant going forward, given the alarming rate at which consumers worldwide are speaking with their wallets and selecting Android handsets over iOS handsets. With just a few more quarters like this, coupled with the cumulative effect of similar sales data over the past 2-3 years and the likely coming wave of Android-based tablets, it is a given (to me, anyway) that Android will be soon be effectively ubiquitous around the globe.

In the world of technology platforms, ubiquity matters (a lot) when developers, manufacturers, etc., are considering future products/solutions.

The Mobile Battle Is Over - And Google Won

And, so, I will reiterate the view I've held for some time now: The mobile battle that Apple started, first with the launch of iPod in 2001 and then moved into hyperdrive with the introduction of iPhone and iPad in 2007 and 2010, respectively, is over (or, will be over shortly), and Google/Android is the victor.

Make no mistake, Apple will clearly continue to play a prominent role in the industry and maintain leadership in some respects. It will also continue to boast a large installed base and a substantial number of loyalists and devotees. But the company's days of dominance, let alone an effective monopolist, are behind it.

Apple's Success Was A Once-In-A-Generation Event

Pundits, analysts and investors need to wrap their heads around one simple notion: Apple's product cycle and performance between 2001-2012 was a once-in-a-generation event. In my view, no company in history has had (or, likely, will soon have agin) so many successive "grand slams" as did Apple with iPod, iTunes, Mac, iOS, iPhone and, finally, iPad. The company's hardware, software and "it-just-works" approach to integration absolutely annihilated existing competition and ignited massive new markets in which Apple had a multiyear near-monopoly and from which Apple was able to generate once-in-a-generation revenue growth and profitability.

As unfair as it may be, the inevitable comparisons to those days will not look good for Apple for some time. The hard reality is that the company's future — even under the best of circumstances — will likely reflect diminished influence and declining revenue (perhaps substantially so), with the prospect of shrinking margins to boot.

Apple Stuck At Square One In The Cloud

To make matters worse for Apple, I think the company is poorly positioned for the battleground of tomorrow: Web (or cloud) services that function like utilities — seamlessly, across all devices, across all operating systems, all the time — at low or no incremental cost.

As I discussed in a previous post, Welcome to Google’s Playground, Apple, the increasing importance of Web services substantially diminishes the value of Apple’s closed-loop hardware/software core, while simultaneously highlighting the strengths of Google’s business. Web services are Google's lifeblood, and the company prints money, either directly or indirectly, from use of many of these cloud-based services, even if those services are accessed via an Apple device (e.g., Maps or Gmail for iOS).

Apple, on the other hand, is almost at square one and, as a result, may be forced to spend big to acquire services that have proven themselves in the hands of consumers at scale.

Fun days for Apple, I know. But, hey, at least it’s not Dell!

Guest author Jeremy LaTrasse is the CEO and co-founder of Message Bus, and was a co-founder of Twitter.

30 years ago a Digital Equipment Corporation rep sent the first piece of spam. In 2013, the problem of spam has become an epidemic with severe if often unseen consequences. We now live in a world filled with digital messaging abuse; according to security giant Symantec, 65.9% of all email is spam!

These days, the vast majority of that spam is caught and filtered before it reaches end-users' inboxes. But it's still out there, gumming up the works of the Internet and wasting huge amounts of network bandwidth as well as compute power and storage. And still enough gets through to make the practice worthwhile for the spammmers.

The threats faced by everyone who gets email vary wildly from penny stock ads and offshore pharma spam to phishing emails and virus-laden attachments. Socially engineered email content leveraging relevant and timely news are hardest to spot. A classic example is tax-time emails that claim to come from the IRS (despite the IRS stating it will never contact anyone by email).

Malicious content and links are hidden behind innocent URL shortners (such as Bit.ly, Ow.ly etc.) and hyperlinked text make detection of bad links particularly challenging. And compromised social media accounts may be the most effective ways to spread abuse and malware because we trust our friends and family.

A Question Of Trust

Yet trust is required for effective communication, especially when identity is involved. How can you, as an email recipient, trust that you are who you claim you are and that the message you are sending me is not malicious?

The answer comes in the form of email authentication technologies that help establish identity. These technologies present evidence establishing where the message came from and who sent it.

The email industry's leading organizations and thinkers have been working on ways of stopping fraudulent email for years. The most recent innovation, DMARC (Domain-based Message Authentication, Reporting & Conformance) is helping email services like Yahoo, Gmail and Hotmail quickly determine the legitimacy of incoming messages. For DMARC to be successful, though, both senders and receivers need to come to the table; recently Twitter announced that it would sign all of its outbound email with DMARC.

DMARC's rapid adoption by the receiver side of the email world (ISPs and mailbox providers) has resulted in nearly 60% of the world's inboxes secured using DMARC technology in the first year alone. Much of the technologies actively establishing trust and identity are invisible to the end recipient, but Hotmail users might have seen a little green Shield icon in their inboxes - this seal informs recipients that Hotmail has taken an extra step to ascertain the identity of the sender.

Despite the email industry's best efforts, however, fighting spam still requires the cooperation of the people and organizations who send and receive emails.

(Mass) Email Senders Have A Responsibility

Senders of legitimate email must take steps to ensure message security and protect their customers and their brand:

1. Ensure all messages pass SPF (sender policy framework) and DKIM (domain keys identified mail) authentication.
2. Publish a "reject" DMARC policy with reporting enabled.
3. Scan the Internet for "cousin" domains, domains that may be mis-spellings of a legitimate message/corporate domain and have those taken down. (These are often a source of malware and spam aimed at unsuspecting recipients.) Protecting the brand's integrity also protects customers, everything is connected.
4. Respect existing acceptable use policies and terms of service as they're published by ISPs and mailbox providers.
5. Stay familiar with the data privacy laws in the countries where they do business; ensure that all messages and messaging practices follow applicable regulations defining privacy and data security.

5 Ways To Protect Yourself

And regular email users also have to take steps to protect themselves:

1. Use different passwords for different logins.
2. Never share personally identifiable information (passwords, social security numbers, bank accounts, etc.) via email: Your bank will never email you and ask you to confirm your bank account number or the password you use to log into your account.
3. Remember, if it seems too good to be true, it probably is. If you don't know who sent it, delete it. If it was important, they'll send it again.
4. Your operating system will update itself if you allow it to; usually you just have to agree once and it'll happen forever after.
5. Look for email personalization in messages. Marketers leverage first name/last name, and other information you've shared with them when setting up an account to help identify them as legitimate senders.

Image courtesy of Shutterstock.

For years, the software industry has been trending away from so-called 'copyleft' licenses like the GNU General Public License (GPL) and toward permissive, Apache-style licensing. Given the rising importance of developers, this isn't surprising: developers just want to get work done without being bogged down by license requirements. It's perhaps not surprising, therefore, that permissive Apache licensing may simply be a way station on the road to no licensing at all.

That's what GitHub seems to be telling us, anyway.

A Trend Toward Extreme Permissiveness

Early in the life of free and open-source software, copyleft licensing reigned supreme. But for years, permissive licenses like BSD and MIT have been climbing, as Redmonk analyst Donnie Berkholz nicely pictures:

Not content to stop there, however, we seem to be entering a new phase: the no-license model. As free-software advocate Glyn Moody notes, "the logical conclusion of the move to more 'permissive' licences [is] one that permits everything."

While Moody talks about public domain software, the GitHub generation seems to be less fussy about legal mechanics.

The GitHub License Black Hole

As Aaron Williamson, senior staff counsel with the Software Freedom Law Center, presented at this year's Linux Collaboration Summit, the vast majority of projects on GitHub don't appear to carry any license terms at all. (The Register's Neil McAllister offers a great summary.) GitHub has become the gathering point for modern open-source development, so it's hugely significant that a mere 14.9% (219,326) of the 1,692,135 code repositories Williamson scanned had a file in their top-level directories that specified a license. 

In other words, the vast majority of code on GitHub isn't necessarily open source. Or proprietary software. Or, well, anything. It's just code.

Redmonk analyst James Governor nailed this trend in 2012, arguing that "younger devs today are about POSS - Post open source software." For such developers, Governor holds, licensing and governance are an afterthought: the code is all. Both Gartner and Forrester find that open source is booming precisely because developers want flexibility.

Less licensing = more flexibility.

Is Licensing Necessary?

Not that this approach is unproblematic. Outercurve Foundation board member Stephen Walli posits that such "promiscuous" sharing without governance and licensing will lead to "software transmitted diseases." But it's unclear that the GitHub generation cares. Maybe they will. Maybe they'll wake up and smell the need for licensing. 

Or maybe the project/company they create will attract the interest of a would-be buyer, and suddenly source code hygeine will matter. A lot. As a Black Duck study shows, open-source compliance is becoming an increasingly common question in mergers and acquisitions:

But all is not lost. Berkholz analyzed a wide array of projects to determine the interplay between project size and licensing. As he summarizes, "as projects grow, they tend to sort out any licensing issues, likely because they get corporate users, professional developers, etc."

License rebels, in other words, tend to become less rebellious as their projects mature.

Ultimately, then, we almost certainly don't face an industry meltdown stemming from uncertain code provenance. Instead, we have a highly permissive license culture that helps to foster the development of code in the early phases of open-source development, which graduates to Apache-style licensing as projects catch on. Lawyers can rest easy. 

Lead image courtesy of Shutterstock.

Microsoft's much-anticipated updated to Windows 8 will be free, will be called Windows 8.1 and will be out "later this year."

All this news came on Tuesday when Tami Reller, the CMO and CFO of Microsoft's Windows Division, addressed JP Morgan's Technology, Media & Telecom Conference. Reller wouldn't commit to a launch date, but promised a public preview edition when Microsoft's Build 2013 developers conference opens on June 26.  Some reports interpreted Reller's remarks to hint at a full release around the Holidays.

Windows 8.1 will work on both Windows 8 and Windows RT, the version of the operating system that runs on ARM processors. 

See also:

• Microsoft Is Trying To Build - And Sell - A kinder, Gentler Windows 8
• Windows Blue's Goal: You Will Love Metro (Eventually)

Tami Reller image is from an earlier Microsoft event.

The idea that a business needs data analysis to better make business decisions is not in dispute… but there is currently a strong debate on how big a data set a business actually needs and how much they need to spend to get that data.

The lure of big data is a powerful one… your web site is flooded with tracking and logging data, after all, and if you only had the tools to store and analyze that data, you could learn the secrets of making your business successful, discover the Colonel's secret recipe and figure out the Question that goes with the Answer 42.

Well, maybe not that much detail, but with the level of hype around big data, one sometimes wonders.

One standard approach to analyzing this data is the installation and configuration of Hadoop servers that are grouped together in clusters of machines - either physical or virtual. Hadoop clusters use distributed storage that makes it relatively simple to store a lot of data fast with less pain than relational database configuration. They also use Java-based MapReduce software to reach into that data and scoop out what you really want - golden nuggets of pure information.

There are limits to MapReduce, naturally: it doesn't perform analysis in real time, but rather in occasionally time-consuming batches, and setting up MapReduce software to do exactly what you need has been compared to getting a root canal. This is why there is an entire ecosystem around Hadoop dedicated to working around those shortcomings, introducing real-time analysis, structured database tool, and software that can convert existing database queries written in Structured Query Language (SQL) to something MapReduce can handle.

But even though Hadoop is relatively inexpensive and easy to scale out onto many machines that run the Linux operating system, is this approach the equivalent of using a wrecking ball to knock down a dollhouse?

Too Much Data?

Some would argue that is indeed the case. A January 2013 paper from Microsoft Research, for instance, disputes the notion that most data analysis that a business would even need a Hadoop cluster, but instead could use a more powerful single server that is scaled-up.

According to the authors of "Nobody Ever Got Fired For Buying a Cluster," the data set sizes of many given businesses are not typically large enough to warrant scaled-out clusters of multiple computers.

You would expect that to be the case for small- to medium-sized businesses (SMBs), but it's also true for enterprises. Even the mega-companies for which big data tools were practically invented don't need those tool a large majority of the time.

For example, the authors found, an analysis of 174,000 jobs submitted to a production analytics cluster in Microsoft had a median job input data set size of less than 14 GB, and 80% of jobs had an input size of less than 1 TB.

The paper cites another study from K. Elmeleegy that "analyzes the Hadoop jobs run on the production clusters at Yahoo. Unfortunately, the median input data set size is not given but, from the information in the paper we can estimate that the median job input size is less than 12.5 GB."

And Yahoo, by the way, is where much of the core functionality of Hadoop was developed, built on the distributed filesystem research conducted earlier at Google. If they aren't using Hadoop for mega jobs all of the time, how appropriate is Hadoop for a "normal" enterprise's data sets?

Facebook, the Borg-like consumer of all user data, surely needs the big data tools, right?

"Ananthanarayanan et al. show that Facebook jobs follow a power-law distribution with small jobs dominating; from their graphs it appears that at least 90% of the jobs have input sizes under 100 GB," the paper states. "Chen et al. present a detailed study of Hadoop workloads for Facebook as well as 5 Cloudera customers. Their graphs also show that a very small minority of jobs achieves terabyte scale or larger and the paper claims explicitly that 'most jobs have input, shuffle, and output sizes in the MB to GB range.'"

Most Data Is Small

The conclusions of the paper, which analyzes various configurations of Hadoop jobs in clustered computers, both physical and in the cloud, against a single scaled-up Hadoop cluster, found that for a majority of data analysis work, the scaled-up server not only handled the workload well, it actually outperformed the clustered machines in many respects.

Now, like any scientific paper, particularly one from a commercial vendor, some skepticism must be applied. Here, the conclusions would seem to benefit Microsoft's sales model for pushing data analysis tools into the enterprise and even SMBs. Scaled-out Hadoop clusters on Linux, after all, are pretty cheap compared to comparable Windows Server clusters, but even the least expensive Hadoop cluster can't hold a candle to the low price of a single scaled-up server.

Which may be the point of the paper, so take it as you will.

Still, there seems to be compelling evidence from sources other than Microsoft that there is a vast majority of data analysis jobs that do not need much more than a strong server or even a personal computer to crunch the numbers and get those golden nuggets of information.

This is not to say that every data problem can be solved with an Excel spreadsheet and a laptop. The flexibility of non-relational (NoSQL) databases are still a very attractive solution to storing and analyzing data sets. And Hadoop is still a relatively inexpensive way to store a lot of data until such time you need to massage it and discover the secrets of the universe or at least your third-quarter sales.

(See also Hadoop Adoption Accelerates, But Not For Data Analytics.)

Before beginning an exploration into the world of big data, businesses should be careful on separating hype from reality and making sure they don't overkill their data needs with a solution that will be more costly to set up and operate in the long run.

Look at NoSQL databases as a way to hold and analyze data for lower costs than relational SQL databases. Or look at federated data services that can provide key information aggregated within your particular sector. And even look at the data you have and start playing around with it in a spreadsheet sometime and see what you come up with.

Hadoop is one way to work with data, but it is by far not the only way.

Image courtesy of Shutterstock.

The Hadoop market is on a tear, growing at a compound annual growth rate of roughly 60%, according to IDC. But why it's growing, or rather, how it's being used, might surprise you. Given all the media hype around Hadoop and its power to predict everything from the optimal number of raisins in your cereal (23) to the exact date of Armageddon (next Tuesday - call in sick), it's perhaps surprising to learn that comparatively few organizations use Hadoop for analytics. Today most enterprises use Hadoop for the pedestrian uses of storage and ETL (Extract, Transform, Load).

Eventually enterprises get to sexy analytics. But we're not there yet. Not by a long shot.

'Poor Man's ETL', 'Unsupervised Digital Landfill', Or Both?

While commonly billed as an analytics tool, Hadoop remains "a poor man's ETL" for the vast majority of enterprises. Yes, there are enterprises running interesting analytical workloads on Hadoop, but these are the exception, not the rule. Hence, while Cloudera cites three common use cases for Hadoop (data transformation, archiving, and exploration, I'm hearing from analysts that 75% or more of the actual Hadoop adoption resides in those first two use cases.

Which is not to suggest such adoption is valueless. Quite the contrary.

The Common Adoption Path For Hadoop

As 451 Research analyst Matt Aslett highlighted at Hadoop Summit, there is a natural progression from using Hadoop to store large quantities of data (i.e., Hadoop as an "unsupervised landfill"), to processing and transforming that data and ultimately to analyzing that data. The fact that most enterprises have yet to get to analytics in any meaningful way is simply a description of where we are in the Hadoop market's evolution.

Indeed, Aslett notes that "attempting to fast forward to analytics, missing out on the processing/integration stage, creates silos and will result in disillusionment." 

We're still early in Hadoop's technological and market evolution, in part due to the complexity of the technology, with 26% of even the most sophisticated Hadoop users citing how long it takes to get into production as a gating factor to its widespread use. Gartner reveals even lower rates of adoption of Big Data projects, often involving Hadoop, at a mere 6%, as enterprises try to grapple with both appropriate use cases and understanding the relevant technology.

Start With What You Know

Small wonder, then, that enterprises are starting with known use cases like storage or ETL before proceeding to more ambitious analytics projects, as Christos Kotsakis suggests. We're still getting comfortable with Hadoop. Applying an unfamiliar technology to a familiar problem makes a lot of sense.

Some day, we'll get to the point where mainstream adopters commonly use Hadoop for significant analytics. But we're not there. Not yet. Just give it time.

Image courtesy of Shutterstock.

The Open Compute Project (OCP) - started two years ago by Facebook to promote open-source hardware solutions for data centers - announced on Wednesday that it has started a new project to develop a specification and "reference box" for a "open" networking switch.

(See also Facebook's Group Hug Frees The Microprocessor From The Motherboard.)

The project will be led by Najam Ahmad, who runs the network engineering team at Facebook, and high-profile companies including Intel, VMware and Broadcom - among others - have already signed on to participate. 

The new project's goals include helping software-defined networking (SDN) "evolve and flourish" and creating "flexible, scalable, and efficient" data center infrastructures. Just as the Open Compute Projects' simple, generic and open server designs competed with server vendors like HP and Dell, this new initiative challenges networking leaders like Cisco. 

(See also Software-Defined Networking: What It Is, How It Works, Why It Matters.)

The project is expected to kick off at the inaugural OCP Engineering Summit to be held at MIT next week.

Image courtesy of Shutterstock

Guest author Dr. Rado Kotorov is chief innovation officer at Information Builders.

The challenge of making business intelligence (BI) easier to use and more pervasive has been widely debated for the last five years. During that time, BI has stalled at an estimated penetration of between 10% and 20% of enterprise users. Every year sees a new analytical technology, a new analytical tool, a new process that promises more analytical power to the business analysts, but none of them have been able to move the needle toward widespread adoption, or "consumerization" of BI.

How Many Business Analysts Do We Really Need?

But is it reasonable to expect more tools for the business analysts to increase Business Intelligence's enterprise penetration? How many business analysts does a business really need?

Instead, we should be thinking about delivering BI to operational employees, suppliers and partners. For every business analyst, there are thousands of other employees who could benefit from the timely information BI can provide. To jump beyond BI's current adoption rate, the needs and skills of those stakeholders must drive BI's technology and the usability considerations.

Apple vs. Microsoft And Apps vs. Tools

When we look at BI through the eyes of end-users as well as business analysts, we can see two different approaches centered on two different philosophies, roughly comparable to the differing philosophies of Apple and Microsoft. While Microsoft has always tailored itself to the business world, Apple aimed its software to the consumer, creating an epic battle between tools and apps.

Microsoft offers a relatively limited set of tools packed into its Office productivity suite. They were designed to satisfy every business need. But of Excel's approximately 30,000 different functions, guess how many the average Excel user utilizes? Most use less than 5%. Only a few know how to use Pivot tables, and IT departments have to build thousands of macros to simplify Excel templates.

Apple, meanwhile, created an app store with 500,000 mostly single-purpose apps designed to meet the broadest possible set of wants and needs, many of which you didn¹t even know you had!

When asked whose paradigm is better, the vast majority of BI stakeholders would likely agree that their end-users would prefer apps over tools.

Fighting Functionality Overload

This is because knowledge workers suffer not only from information overload, but also from functionality overload. End-users are not analysts. When individuals need to check the weather, they do not perform a detailed analysis of the weather patterns. They trust what the weather app says. Similarly, business users want apps that deliver them the trusted information they need to do their jobs.

From this perspective, the consumerization of BI can only be driven by technologies that turn the classic enterprise BI portal into a BI app store, where end users can go and select targeted, specific apps that address their concrete questions.

Two Kinds Of BI Tools

Of course, the simplicity of end-user info apps should be complemented with higher-end tools to help professional analysts learn to perform new and more complex analyses and derive even better business insights.

Rather than striving to turn end-users into analysts, we have to give those users info apps that let them focus on their primary job skills. And vice versa: Rather than making simplistic BI tools for analysts, let's help them learn new methods and methodologies to maximize the insights they can derive. Analysts are coping with new data sources, new types of data and new forms of interaction with consumers, all of which provide plenty of opportunities for analysis, but also requires significant skills development.

How to "consumerize" Business Intelligence may not yet be completely clear, but one thing is certain: It's pretty clear that a one-size-fits-all approach won't do the job. BI-related apps could meet the varying needs of end-users more efficiently than the all-encompassing tools analysts require, and help make BI a core part of enterprise decision making.

Lead image courtesy of Shutterstock.

A new video technology quietly announced late last week could mark a landmark change in how apps are deployed on PCs, tablets and smartphones for years to come - and also have big ramifications on how companies like Apple do business.

You wouldn't think that the technology launched by the Mozilla Foundation and graphic-rendering vendor Otoy on Friday would be all that big a deal. After all, the software, which is known as a codec, was originally designed to allow for the playback of videos on HTML5 pages within a browser without plug-ins.

That alone is pretty cool, from a consumer's point of view. There's are still videos out there, such as those encoded with the H.264 format, that need a special plug-in to be viewed, thanks to the patents tied to the H.264 specification. Live TV and HD video can be viewed with any HTML5 browser that can support WebGL (hold that thought).

But the other thing the new codec, known as ORBX.js, features is much, much more significant: it also enables steaming of desktop applications. An application (say, Microsoft Office) could be hosted on a company's server and then used by any employee who logs in to the application. It would not matter what operating system they were using (Windows, OS X or Linux) or even what platform (phone, tablet or desktop), because the browser would be the only thing that matters.

"This is not just remote desktop tech, or X11 reborn via JavaScript]," [blogged Mozilla Foundation CTO Brendan Eich, "Many local/remote hybrid computation schemes are at hand today, e.g., a game can do near-field computing in the browser on a beefy client while offloading lower [level of detail] work to the [game processing unit] cloud."

When Cloud Becomes The Platform

Using streaming video to deploy remote desktops is not new, of course, this is pretty much the way Logmein does it with their remote desktop technology. But as good as Logmein and other RD vendors are, they still use a dedicated client and the speed of the remote setup can be hampered by the power of the source desktop as well as the limitations of bandwidth.

If the application were to be hosted in the cloud with more resources, as Eich suggests, then only bandwidth would become a limit to application performance. In fact, if ORBX.js performs as promised, you won't even need a "beefy" client, as Eich says we have now - nearly all of the processing work will be done in the cloud and streamed to the waiting browser client.

Streaming apps, if this technology works, would then represent a big change for end users and even a potential cost savings - if the bulk of the processing power is situated in the cloud, then hardware requirements for end-user devices can stay where they are or even be lowered.

Another big change - if all you need is a decent screen and an interface to connect to applications, you could host your entire work/home environment in the cloud and access it from any compatible device at any time. It could be a full version on the desktop or laptop, and perhaps a scaled-down version on your tablet or smartphone, but the apps and your data would always be there, on any of your machines.

Walled Garden? What Walled Garden?

If applications can be delivered effectively through this kind of enhanced video streaming, currently that also puts Apple and Microsoft at a strong disadvantage against competitors like Google and Blackberry, especially in the mobile space.

Recall the requirements for the JavaScript-based ORBX.js: any HTML5 browser that can support WebGL.

As it stands right now, the Safari browser on the iOS mobile platform does not support WebGL at all (except for iAd developers) - and on OS X, Safari only offers partial support for the standard (if the user has up-to-date video drivers). Internet Explorer does not support WebGL at all, either.

Android is a little tricker: neither the native Android browser or Chrome for Android support WebGL, but Firefox for Android does. As of BlackBerry 10, the BlackBerry browser will support WebGL, too.

This would mean that Android and BlackBerry users could run cloud-based apps on their devices right now, while Windows Phone, Windows 8, Windows RT and iOS users would be out of luck.

That's probably no accident, either, since any application that streams in through browser is one the operating system vendor can't monetize. In other words, Apple and Microsoft won't get their app store cut from apps that are streamed.

That this is a deliberate choice on the part of Apple and Microsoft seems likely. Even Google has yet to support WebGL on its mobile-device browsers, possibly for the same reasons.

But given that Google's Chrome browser is all in for WebGL, Google could still reap the benefits of cloud-based applications soon. If that proves a success, or if BlackBerry's WebGL bet pays off, then it won't be a long wait for the Android browsers to come around to WebGL.

At which point, it will be anyone's guess if Microsoft and Apple will jump on board, too. There are already rumors that Internet Explorer 11 will support WebGL, so Microsoft may be on its way to enabling cloud-based streaming apps.

Cloud applications will never supplant native apps - connectivity issues and security concerns will make sure of that - but it's a future that looks pretty cool for users who want to use their applications and data any where, any time.

Image courtesy of Shutterstock.

For years open source and proprietary software camps have fought over which model produces better software. According to Coverity's annual Scan report, released today, both sides are right. And wrong. Depending on how big the code base is.

Coverity's Scan report has long served as the state of the union for open-source software quality, though Coverity analyzes proprietary software, too. In Coverity's 2012 report, which analyzed over 450,000,000 lines of code, both open-source and proprietary software saw an increase in quality, as measured by average defect density (errors found per 1,000 lines of code tested). According to Coverity, this can be attributed in part to an overall increase in organizations that have implemented formal development testing processes for their software code. 

The most interesting part of the report, however, is its analysis of the impact of project size on code quality.  

Both open source and proprietary software had roughly equivalent average defect density rates: .69 for open source and .68 for proprietary software. Open source projects had the highest quality when there were between 500,000 – 1,000,000 lines of code: 70% fewer defects, yielding a .44 average defect density. Proprietary software?  It had the best quality (or, lowest defect density) in projects over one million lines of code, registering a .33 average defect density in larger projects.

For smaller code bases, then, open source shows dramatically better quality. In larger code bases, open source has more defects, but isn't far off from proprietary software: .75 vs. .66.

While there’s no single factor that can explain this phenomenon, it’s likely due at least in part to the fact that open-source projects are often purpose-specific, and maintained by a core group of committed developers. As the projects grow in size and scope, and more developers come on board, there’s a greater hesitancy to make changes to the core kernel for fear of a ripple effect that could adversely impact the larger project.

Conversely, proprietary software projects usually need to get to a certain point of critical mass – somewhere around one million lines of code, if the results of Coverity’s report are to be trusted – before an organization implements formal development testing processes to ensure quality software code. 

Here’s an infographic that encapsulates the main findings of this year’s Scan report:

Lead image courtesy of Shutterstock.

Last September, when Apple debuted its new Lightning connector to replace the company's venerable 30-pin connector, I predicted that the move might cause surprising problems. My post attracted a lot of attention and garnered a whopping 135 comments. Many of those commenters agreed that Apple's move - while perhaps necessary, would have significant complications for the company. But many others said I was crazy to doubt Apple in any way, shape or form.

(See also iPhone 5's New Lightning Connector Is A Bigger Problem Than Apple Thinks.)

Well, according to an article by Nick Wingfield and Brian X. Chen in Sunday's New York Times, the move has indeed given Apple's rivals an edge in the push toward wireless accessories (Accessories No Longer Tethered To Apple).

In my original post, I warned that the peripheral market's commitment to the iPhone's 30-pin connector was a big competitive advantage for Apple, because being the one device that could attach directly to external speakers, clocks, stands and chargers added an extra helping of utility for its devices. I said that the new Lightning connector threatened to eliminate that advantage, and that could hurt Apple:

"The availability of all those peripherals, in turn, has helped make the iPhone even more popular. iPhone buyers know that no other phone comes close to enjoying the choices and support that the iPhone has - in cars, in hotel rooms, at airports and everywhere else. By carrying an iPhone instead of a competing phone, they have a much better chance of being able to buy and use supporting infrastructure - which can make a big difference in the overall experience. The iPhone 5’s new Lightning connector threatens all that, and not just for iPhone 5 users."

Sunday's Times' article seems to confirm that prediction:

"Apple’s iron grip on the digital accessories in hotel rooms, store shelves and living rooms is starting to slip - potentially risking the royalties it earns from accessory makers and, more significant, giving Apple customers more freedom to switch to rival products."

and

"Jeremy Horwitz, editor in chief of iLounge, a Web site devoted to Apple accessories, said Apple’s aggressive control over accessories for its products drove many makers to more open means of connecting devices, which helped feed the success of mobile devices made by other companies."

and 

"Fewer people who buy sound systems that work only with Apple devices, in theory, could mean fewer obstacles for those interested in switching to competing phones and tablets in the future."

To be fair, though, there has been an industry-wide movement toward wireless connections to peripherals, and Apple devices are fully capable of supporting this trend. It's just that the wireless world is pretty much a level playing field, while Apple used to utterly dominate hard-wired connections. You can't blame all of that on the Lightning connector, but as the Times pointed out:

"'Even before Apple shifted from the 30-pin connector to Lightning, the market had started shifting,' said Rory Dooley, senior vice president for music at Logitech. 'Lightning came in and accelerated some of the change.'"

As for me, I couldn't get my speaker docks to work with my iPhone 5, so I ended up using a "spare" Apple TV device to let me control the speakers using Airplay. Works for me, but probably not a cost-effective solution for most people.

If Intel's future chief executives continue rising through its ranks, then the real news isn't that Intel named Brian Krzanich its sixth CEO. It's that Intel software chief Renee James may be in line to succeed him.

It's hard to escape a back-to-the-future feeling with the Krzanich announcement, which basically represents the company's continued focus on making chips smaller, cheaper and, now, less power-hungry — Intel's traditional secret sauce. Like outgoing CEO Paul Otellini, Krzanich served as chief operating officer and previously held several manufacturing positions across the company since he joined Intel in 1989. 

Intel CEOs generally emerge from the president's office or that of the COO — Krzanich's old job, and one that Intel hasn't filled yet. Intel historically has swapped CEOs when they've turned 60; Krzanich, 52, will have eight more years before a successor takes over.

Last November, when Otellini unexpectedly declared his intention to step down later this month, chairman Andy Bryant took the unusual step of naming what some saw as four possibilities to replace him: Krzanich, James, Dadi Perlmutter, the head of Intel's chip business; and Arvind Sodhani, head of the company's internal VC unit, Intel Capital.

The Rise Of Intel Software

Of the four, only James represents something strikingly new for Intel. In a company traditionally dominated by old, balding white engineers, James has been the face of Intel's software business since joining the company via its acquisition of Bell Technologies in 1988.

Although she holds business degrees from the University of Oregon, that's not necessarily a black mark; Otellini himself was an economics major. But James also served as chief of staff for former chief executive Andy Grove, giving her the stamp of legitimacy. James even carries a red pen — a notorious Grove trademark — as a reminder of her roots.

James was instrumental in three major acquisitions: Intel's 2007 acquisition of physics middleware developer Havok for an undisclosed amount, the 2009 acquisition of embedded software company Wind River for about $884 million; and Intel's $7.68 billion purchase of security giant McAfee in 2011. Intel contributes to the Linux kernel, developed its own Hadoop implementation, co-developed the Tizen mobile OS with Samsung, and writes its own software compilers for its microprocessors.

"I can see a day where a future Intel CEO could have extensive software experience," Patrick Moorhead, a former executive of Intel rival AMD and now an independent analyst, said. About 70 percent of a smartphone's research and development costs now derive from software, Moorhead said.

Wait A Second. Software? Intel? Really?

Can software really mean that much to Intel? After all, this is a company that owns over 80 percent of both the PC microprocessor market, about as much in the server market, and is busy trying to make inroads into phones, tablets, and low-power convertible Windows tablets. 

Sure. Moorhead, for instance, argues that Intel's strategy is to combine chips and software in a way that make each indispensable to the other. Rolling your own software and fabricating your own silicon can allow a company to optimize their combined performance in ways that other manufacturers can't match. In a business sense, selling the software and silicon together also allows customers to save money and simplify their own development efforts.

Put that way, Krzanich's appointment as chief executive, with James just below him on the executive ladder, makes sense. Intel's not about to try a RIM/BlackBerry-styled double chief executive, but for a number of years, Intel operated out of a two-in-the-box strategy, where responsibilities for certain divisions were shared not by one, but by two executives.

For Now, Krzanich Minds The Store

Manufacturing prowess remains key to Intel. When the company ships its next-generation "Haswell" processor this June, the chip's smallest features will be just 22 nanometers wide, putting it a full generation ahead of AMD's expected "Piledriver" chip. Smaller chips with finer "linewidths" are traditionally more powerful; now that the focus has turned to mobile, Intel can reduce the power those chips consume instead.

Which isn't to say Intel doesn't have a lot of catching up to do. It has fallen badly behind in producing processors for smartphones and tablets, in an eerie repeat of the way it was initially late to the notebook PC market. 

Krzanich's job will be to turn that around. Ideally, Intel needs to design the best chips it can, shrink them down as small as possible, and then them them with optimized software to maximize their potential.

James' appointment is as much reward as recognition of her strategic role. In 2009, soon after the Wind River acquisition, the notoriously grouchy Grove was asked to characterize Intel's history in software. “The results have been very consistent,” Grove, then 73 and retired, told The Oregonian. “They amounted to nothing.” Now, they're everything.

Contextual search and the Internet of Things are two key factors in how search is evolving from users actively searching for information to users receiving information as they need it.

But there is another key component that must be added to the search equation: the rise of intelligent software agents that will not only anticipate the information you need, but also act on that information to help manage your life.

(See also Forget Searching For Content - Content Is About To Start Searching For You and How The Internet Of Things Will Revolutionize Search.)

The Dawn Of The Bot Age

Call it artificial intelligence, software agents or even bots, the technology for search-related automated prediction and action has been in development for a long time. (In fact, I used to cover this topic when I was the managing editor for BotSpot.com at the turn of the century.)

(See also Futurist's Cheat Sheet: Artificial Intelligence.)

In those days, bot development was focused on creating automated software to handle the routine tasks that were proliferating on the then-fledgling commercial Internet. Web crawlers, software that actively seeks out and indexes websites for search engines, were one very popular use of software bots. But there was always a goal beyond the mundane world of Web crawlers and software trying to reasonably fake a Turing test to appear human: developers wanted the software to take specific tasks completely off the hands of humans.

Over a decade later, we may finally be getting to the point where bots can actually do that.

It is not that the development of intelligent agents stalled during the first decade of the 21st Century. Instead, we may not have been quite ready to implement them. Automating routine tasks just didn't seem like the top priority during the beginnings of the mobile revolution.

Now things have changed. First, and most obviously, mobile devices are everywhere. Second, there are now legions of interesting Web services to automate. The final ingredient is the most important: With the rise of Big Data, there is now enough information available for a software agent to actually use to perform anticipatory actions. In that context, the challenges of applying software agents and artificial intelligence to business solutions is nothing compared to the potential payoff to users.

(See also How The Internet Will Tell You What To Eat, Where To Go And Even Who To Date.)

Dr. Moshe BenBassat, CEO of ClickSoftware, it is fair to say, lives and breathes this stuff every day. BenBassat envisions a world where personal agents, which he calls “butlers,” manage the day-to-day planning and implementing of workflows.

The "Butler" Did It

BenBassat offers an example to illustrate: Imagine a service technician who logs into his smartphone’s service app and pulls up today’s schedule. His first appointment: the Acme Bank downtown. A few more swipes pulls the address from the calendar app and then brings up a map in the navigation app, and off the technician goes. When he arrives at Acme, he finds and calls the customer contact, who has to come down to the lobby and admit him into the building. Once arriving at the customer site on the 17th floor, the technician discovers he has left the replacement part in his vehicle, so he goes back down to get it.

In a scenario like this, BenBassat estimates the technician would spend 7-12 minutes just swiping and typing on the phone to find and use the data he needs. Over the course of the day, that adds up to a lot of lost productivity.

In a world staffed by BenBassat’s butlers, the scenario might unfold like this: The service technician logs into his phone’s service and is immediately informed about the first appointment: the Acme Bank downtown. The butler asks if the technician if he would like a map to the appointment, and after agreeing, off the technician goes, using the map to reach his destination. Just before he arrives at Acme, the butler autodials the customer contact and informs her the technician is about to arrive, so she can come down to the lobby and let him into the building. When the technician leaves his vehicle, the butler senses that the replacement part is not in toolkit the technician is carrying, and prompts the technician to grab it from the truck, saving the trip back down to the vehicle.

"Butlers" like the ones BenBassat describes promise to play a huge role in changing search - and by extension the way we work. Proactive software agents will reduce the need to waste time looking for information. Instead, information will be delivered right when we need it. As software agents get better at figuring out what we want, that information will become more useful and actionable.

We are almost there now: Contextual search tools like Google Now, which takes into account where you are and what you are doing to provide useful information, are the first big step towards anticipatory and responsive software agents.

(See also Google Now Knows More About You Than Your Family Does - Are You OK With That?)

The Interaction Issue

There is still a ways to go. Social interaction is seen as the biggest obstacle to effective software agents. Agents are only as good as what they are programmed to do, while humans have internalized a lot of common-sense tricks for interpreting reality. We know what we mean when we say, "find me some pizza," but the software agent might give you a map of nearby pizza places - or just call up pictures of pizza.

In the consumer world right now, Apple's Siri is the most well-known example thus far of how a software agent will interact with humans, though it has its limitations, both in speech recognition and plain common sense. As that interaction is smoothed out, though, it is not hard to imaging giving agents like Siri or Google Now's voice search more permissions to act on the information at hand, instead of just reporting it. Once that hurdle is overcome, all of that predictive and contextual information that the Internet is starting to finding for us will have a smooth, human-like interface and better able to help us manage our days.

(See also Who Has The Advantage: Siri Or Google Now?)

Why Search Anymore?

Searching for anything - be it on the Internet, your inbox or on your personal devices and services - will be far less necessary, both in business and personal contexts. Search is not just firing up Google, after all - it also includes combing through your own data for relevant information. When your spouse has a last-minute meeting and can’t pick up the kids from after-school sports, for example, you won't have to go though a complicated dance of multiple phone calls, texts and emails as you re-arrange both your schedules and stress out over making sure someone gets there on time. Instead, your search agents could analyze and coordinate both your schedules and create a single suggestion to line everything up. All you'd have to do is agree to the changes.  

The combination of automated agents, contextual search and a sea of data from our devices, services and the Internet of Things, search is poised to become vastly more useful and efficient than it already is. The pieces are getting there with agents like Siri and contextual search like Google Now. If it all works as promised, information we need will be delivered to us just when we need it, without our having to invest time and effort looking for it.

Image courtesy of Shutterstock.

Intel has named a new chief executive officer. The chipmaker has dubbed former chief operating officer Brian Krzanich as its new leader, replacing Paul Otellini who announced that he would retire from the company last November. 

Krzanich has been at Intel for more than 30 years, starting in 1982. He was promoted to COO in January 2012 and has been operating as executive vice president since Nov. 2012. He started at Intel as an engineer and worked his way up through the ranks before becoming head of Intel's worldwide manufacturing in 2010. In that position Krzanich was responsible for all of Intel's factories and chip manufacturing worldwide.

The move to promote Krzanich mirrors that of Otellini himself. Otellini had worked at Intel for 32 years before becoming CEO in 2005, replacing Craig Barret. At the time was seen as an odd choice for Intel as the company had historically promoted senior executives who had been trained as engineers. Otellini was trained as a businessman with a degree in economics and a Master's in Business Administration. Krzanich comes from an engineering background and has served as an Intel plant manager before his ascension to the executive ranks.

Guest author Ryan Murphey is VP of Facilities and DC Operations for PEER 1 Hosting.

Data center computing demand grew 63% in 2012, requiring enterprises and data center operators to build new facilities to accommodate the market.

Although some data centers may seem to be placed at random, selecting a data center location is a little more strategic than throwing darts at a map. In fact, there are many different factors that affect this decision-making process. The goal is  to ensure that new facilities address local service demand efficiently and sustainably while still making a profit (or meeting corporate needs). Whether the plan is to build a new facility or retrofit an existing building, a number of factors must be considered to ensure that the potential $1 billion investment will deliver strong returns, including type of service, proximity to end users, potential for disaster and climate.

(See also Facebook To Build Huge New Data Center In Iowa - Here's Why)

While all of these factors come into play for data services and hosting providers, many of them are also concerns for enterprises building their own data centers for corporate use.

Is There Enough Demand?

Typically, the first step in hosting companies choosing where to put a data center is talking to customers, prospects and partners to determine where companies are looking for hosting support. This may seem obvious, but for providers to ensure the success of a new data center, they must fill a substantial portion of the facility before the doors even open to guarantee profits during the first month.

If a provider can’t fill enough of a new facility, one way to reduce the financial risk is to use a modular approach. Many companies, including Dell and IBM, use this approach to accommodate growing data center demand quickly, as it allows for the gradual buildout of infrastructure. Additionally, going modular means that providers don’t have to dedicate resources to power and cool unused aisles and racks – they just pay for what’s being used.

Who Needs These Services?

In addition to demand, the location must also match the services to be offered. For example, if a provider is receiving numerous requests for co-located trading equipment on Wall Street, then a co-location facility as close to Wall Street as possible – ideally on the same block – will best serve the demand.

Alternatively, if a provider is predominantly seeing non-latency-sensitive demand across the greater New York area, it can build a hosting facility anywhere nearby. Building a data center on Long Island would cost much less in rent and utilities, and would still be able to meet this market’s hosting needs.

How Likely Are Natural Disasters?

Another element that plays a role in the decision-making process is the number and severity of natural disasters common to the region. For instance, areas prone to tornados, flooding or hurricanes raise a red flag because they could knock out power and damage the facilities. Similarly, operators may shy away from building new data centers along turbulent coastlines and instead look at real estate further inland to avoid water or salt damage.

Hosting providers haven’t always considered volatile weather a determining factor, though. For instance, despite the likelihood of hurricanes and tornados in areas like the Southeast and Midwest, both these areas have a high concentration of data center facilities. But as data center infrastructure and functionality rise in importance, this factor can no longer be ignored – especially since data center outages can cost an average of $5,600 per minute – that’s $336,000 per hour!

What About Free Cooling?

Average temperature is also very important to keep in mind when choosing where to construct a new data center, as it can greatly influence utility costs. Power accounts for an estimaed 50% of data center operation costs, which is why many operators choose temperate environments that won’t add to the heat generated by servers. In a milder climate, operators can also take advantage of “free cooling,” such as open-air cooling, to further cut cooling costs. Large companies like Facebook, Amazon and Apple have been opening data centers to the Northwest region of the U.S. to take advantage of the area’s cool climate and potential for free cooling.

(See also Say Goodbye To The Traditional Data Center.)

Selecting a new data center location follows complex formulas that may not result in the same outcome for every every hosting operator or enterprise.

Every company building a data center will prioritize different goals and concerns. For example, data centers located in Los Angeles face big bills when it comes to climate control, while New York City data centers must grapple with extremely high rent. It’s all a matter of finding an location that offers the greatest number of benefits - without costing a fortune.

Image courtesy of Shutterstock.

While Microsoft is obviously having trouble convincing consumers to adopt Windows 8, its message is that enterprises have been far more accepting. It turns out that might not be true, either.

Last week, Forrester Research released a report claiming that Microsoft's Windows 7 is used in about 50% of all enterprise installationss, based both on its own surveys as well as a sampling of the Web traffic across it own servers. That's not surprising, given that Windows 7 was released to enterprises years ago, in mid-2009.

But what's more shocking - and more worrisome to Microsoft - is a survey of IT professionals polled by Dell's KACE systems management unit last week. It seems that even now, companies who are finally upgrading from Windows XP are turning away from Windows 8 in droves, selecting instead the older Windows 7 operating system. Of the 273 IT professionals who said that they're upgrading from Windows XP, just 2% said they're choosing Windows 8. The vast majority - 69% - said that they're choosing Windows 7 instead.

2013: A Key Transition Year From Windows XP

For Microsoft and many of its customers, 2013 represents a key transitional year. Many of its enterprise customers will be forced to move away from Windows XP,  which Microsoft plans to cease supporting on April 8, 2014. Microsoft is eager to sell those customers an upgrade to Windows 8, Office 2013, and other services, while PC makers hope they'll buy all new PCs, too.

In September of 2012, though, research firm Gartner warned enterprises that they should upgrade to Windows 7, not Windows 8. Gartner vice president Richard Kleynhans said then that he was aware of many enterprises doing just that. "Get Windows 7 done, and then you can start to experiment and dabble with Windows 8, but don't let Windows 8 derail your Windows 7 upgrade project," Kleynhans said.

That's a lesson Dell customers apparently have apparently taken to heart. The reason, explained Lisa Richardson, a senior product manager for Dell KACE, is simple: complexity equals cost.

Transition Fatigue

"For a lot of them, it's fatigue. It's OS fatigue," Richardson said. "It's, 'OK, we're making this huge shift to Windows 7, we know it's been tested, it's been around, we have to move onto it.' What we're hearing from IT administrators is that there's a challenge from moving to Windows 7 and its ribbon interface. But Windows 8 is an ever bigger shift in terms of user experience. And what I'm finding out is that because it's such a big shift in user experience, such a huge jump from Windows XP to Windows 8, support calls are going to go up. That drives up support costs, and that turns off many IT administrators."

The other big transition concern is applications compatibility, especially with line-of-business programs developed in house, Richardson reported. Compatibility issues, however, can crop in both Windows 7 and Windows 8.

In case you're wondering, operating system upgrades are what Dell KACE does. The Dell KACE Deployment Appliance manages OS upgrades across enterprises (including apps, files, and operating systems), so IT admins participating in the survey have skin in the game. The survey participants represented a mix of existing KACE customers as well as prospective clients, Dell said.

Richardson added that 15% of the survey participants said they plan to deploy both Windows 8 and Windows 7, and 10% said they wouldn't install either one. A second survey question indicated that 17% of participants had completed their upgrade, 18% were three-quarters done, and that an additional 13% said they were at least halfway done. But almost half (49%) said either that they were either less than halfway done or hadn't even begun.

Forrester's data, meanwhile, also gives an edge to Windows 7 over WIndows 8. The firm found that its Web traffic was about 50% Windows 7, with 47.5% of IT managers saying they've installed it. Windows XP still accounted for about 22.3% of traffic, or 38.2% of systems; Macs are about 14.6% of traffic, and 14.3% of self-reported employee PC ownership. Windows 8 was too new to make the IT survey, but represented just 1% of Forrester's traffic from May 2012 through January 2013.

Windows 8 Or Windows 7: It's Still Good News For Hardware Makers

No matter OS enterprises are upgrading to, Dell found, the time seems ripe for a hardware refresh. A lot of IT customers reported that PCs were being asked to last far longer than the previously standard three-year refresh cycle because of the effects of the recession - often five to six years. "Those systems couldn't support either Windows 7 or Windows 8," Richardson said, in part because they don't have big enough hard drives.

And hardware still matters, even as the trend toward mobile devices implies that the cloud is shouldering more of the workload. "A stylized view suggests that computing is moving to the cloud and that platforms don’t matter anymore," Forrester's report concluded. "This stylized view couldn’t be more wrong — today and for the next five years or longer. The mobile revolution continues afoot, as users shift computing minutes from traditional PCs and Macs to tablets, smartphones, and new classes of devices like hybrids and convertibles."

And that's where the good news for Microsoft may be found. Richardson reported strong IT demand for Windows tablets - as supplemental devices, not as laptop replacements - to the point where Kace plans to add support for Windows 8 deployments on tablets.

The War For Windows? Or For PCs?

Forrester's message is that platforms still matter. But listen closely to what Microsoft's saying these days, and the interpretation changes.

"Businesses continue to value the Windows platform," Chris Suh, general manager of Microsoft's investor relations, said during the company's recent conference call. "Volume licensing of Windows is on track to deliver almost $4 billion in revenue this year, and nearly three quarters of enterprise agreements that we’ve signed this year include Windows. Additionally, this quarter we saw continued progress in the transition of Windows XP to Windows 7, and now two thirds of enterprise desktops are running Windows 7."

It's all Windows, Windows, Windows. But notice the careful phrasing. Microsoft's message is that businesses value Windows, not necessarily Windows 8.

For a company reacting to the alarm bells analysts are sounding on the future of the PC, Microsoft's statements signify an important strategic retrenching: for years, Microsoft fought to establish its latest operating system to spearhead continued growth. As this data from Forrester and Dell shows, though, Microsoft may be forced to acknowledge that Windows 8 is a lost cause within the enterprise. The new, lesser goal may be simply trying to hold on to the Windows PC - any flavor of Windows PC.

Lead image of Tami Reller discussing Windows 8 and images of Windows XP, 7, 8 via Microsoft.

You can't be too rich, too thin or have too much computing power, but you can spend too much money. Since the first server went into a rack, enterprise IT departments have been balancing cost and performance in the data center. HP's ProLiant BL660c Gen8 blade server and DL560 Gen8 server give them new ways to stack the odds. 

The single largest operating cost in most data centers is power, and while estimates vary, as much as 50% of that power goes for cooling. Big data, social media and the proliferation of cloud-based applications and mobile clients have spiked demand for server-side processing and throughput. As the installed base grows, so does the cost of energy, further straining IT budgets.

In response, HP is leading the server industry's focus on server efficiency. By optimizing power consumption and increasing server density, data centers can significantly reduce their energy needs. Density coupled with increased processing capabilities provides a more compact management environment for administrators, while producing a much greater compute output for a given floor size.

HP's latest additions to the ProLiant Scale-up (four or more sockets) portfolio have taken the next steps toward addressing power, performance and floor space congestion. Featuring Intel® Xeon® E5-4600 processors, the BL660c Gen8 and DL560 Gen8 were built to deliver industry-leading performance for compute-intensive applications, while reducing the overall infrastructure and management burden. The results are impressive.

Savings

The most noticeable features of both the BL660c and the DL560 are embedded intelligence and size. While they offer the computing power of many 4s/4U servers, the DL560 is just 2U high. The BL660c is a full-height, single-slot blade. By increasing server density and enhancing energy efficiency, data centers can reduce their overall rack space profile, cooling requirements and power consumption. As a result, HP estimates break-even times of as fast as three months.

HP has built in a number of embedded technologies to automate these savings. For example, Automated Energy Optimization builds a heat map of the entire server, boosting cooling resources where they're needed and slowing them where they aren't.

Manageability

Human resources have a cost, too. Optimizing administrative tasks can reduce the amount of time IT spends on routine management - freeing staff to create more value through innovation.

The new ProLiant servers score here as well. HP's ProActive Insight Architecture provides monitoring and automation tools that can reduce the administration workload by up to 69% by assisting provisioning, monitoring, diagnostics and support, thus allowing IT staff to better support corporate business strategies. 

From the hardware up, the BL660c and DL560 were built for serviceability. All four CPU sockets and memory modules are placed on the system board for easy access, and the FlexibleLOM card slot replaces the embedded LAN on the motherboard to give administrators a wide variety of options for fabric, port quantity and chipset. Additionally, the DL560 provides up to six full-height card slots (two of them full-length) for maximum flexibility. There's even support for 150 Watt cards.

HP's Integrated Lights Out (iLO)

Performance

Savings and efficiency don't matter much if the server can't perform. The ProLiant BL660c and DL560's balanced architecture enable them to command top spots in all cases. Leading SPECjbb2013 and SAP SD 2-tier benchmarks demonstrate the servers' suitability for business processing implementations - including databases, Enterprise Resource Planning (ERP) and batch processing - and the combination of the industry's top floating-point calculations and super-fast PCIe Gen3 I/O provide more than enough horsepower and bandwidth to handle High Performance Computing (HPC) jobs – particularly as control nodes. Finally, in the omnipresent virtualization space, the ProLiant BL660c and DL560 hold the top spots for dense 4-socket servers. High scores across-the-board show that the ProLiants are solid, well-built machines - not just generic servers "cooked" to score well on a specific test.

The Optimum Workloads

Dense 4-socket (and greater) servers like the BL660c and DL560 really show their value in situations that require fast I/O, heavy threading and large amounts of memory. Traditional IT infrastructure operations are often fairly low-demand, so unless you're operating at an incredibly large volume or plan to grow quickly, you may be able to get by with lower-cost, 2-socket servers, like the HP ProLiant BL460c Gen8 server blade or DL360p Gen8 rack server.

Four-socket servers are best-suited to applications demanding large numbers of cores, database threading, or high numbers of VMs.

As you step up your processing load and number of connections, though, the BL660c and DL560 come into their own. Virtualization, business intelligence and business processing are full of potential bottlenecks that won't slow them down. These Gen8 servers are ideal for replacing aging RISC/Unix systems as well as demanding database workloads - either bare-metal or virtualized.

Much more than some Frankenstein's monster artificially packing in resources to make the form factor more dense, the BL660c and DL560 Gen8 servers deliver all this in a well-architected, superbly built package without compromising on performance, scalability or efficiency.

Intel, the Intel logo, Xeon, and Xeon Inside are trademarks or registered trademarks of Intel Corporation in the U.S. and/or other countries.

Taking the stage at the Open Business Conference, Red Hat CEO Jim Whitehurst opened the event with a provocative question: is the industry's choice to go open... or die? Whitehurst clearly has a dog in this fight, given that his company is the first open source vendor to reach $1 billion in annual revenue. But he laid out a compelling argument that enterprises no longer choose between competing technology products, but instead must decide between competing innovation models.

And in Whitehurst's mind, there's only one real choice to make: for open data, open source, open APIs.

Credit: ReadWrite

From Vendor-Led Innovation To User-Led Innovation

As Whitehurst argues, commoditization of technology changes the way innovation occurs, moving from vendor-led to user-led. This is the natural progression for any industry, as he highlighted by referencing the automobile industry. While the companies that make engines and steering wheels are unquestionably important, we don't really think about them much. Instead we focus on Toyota, Ford, Volkswagen, etc. as the companies really driving innovation in the automobile industry.

We have seen this play out in technology, With the Web giants driving innovation in mobile, cloud and Big Data. Each of the major Web companies not only uses open source, but depends fundamentally upon it:

Credit: Red Hat

This isn't where open source started, of course. Open source started with existing categories, commodifying well-known categories like operating systems (Unix->Linux), databases (Oracle->MySQL), application servers (WebLogic->JBoss). But a funny thing happens on the way to the market: as more users get involved, they started to move at a faster cadence than any vendor can. Indeed, as Whitehurst noted, open source got to a point in a number of categories where it wasn't merely replicating the state of the art, but advancing the state of the art. 

As such, enterprises no longer pick products. They pick innovation models.

Picking Innovation Models

By choosing an open model, enterprises necessarily make the choice to participate in the future, rather than simply accepting the technologies their preferred vendors hand them. In such a model, even vendors must collaborate with communities, rather than dictate to them. For example, Red Hat can't provide a long-term roadmap because it works collaboratively with the wider community of open source technology users, and can't impose its will on that group.

Nor is open source merely a matter of ones and zeroes. 

Whitehurst said he had asked the CTO of a major Web company why the company insisted on contributing its internally-developed software to the broader open-source community. The response: "If there is anything we can do to make a data center run more efficiently and hence save energy, for example, we have a moral obligation to the world to contribute it back."

While this moral element of open source isn't universally shared, it remains a strong motivator for a significant segment of the developer population.

Who Is Ready For An Open Future?

Not everyone wants to assume such moral obligations, and not everyone wants to give up control to a community. While we're unlikely to see the death of proprietary software anytime soon, we certainly seem to be witnessing a move toward open innovation. Is the future as cut and dried as Whitehurst hypothesized?

Please share your thoughts in the comments.

The Big Data boom has largely been fueled by a simple calculation: Data + Technology = Actionable Insights, Magic Ponies, and Superpowers. The reality, of course, is far more pedestrian, because while Big Data technology has indeed increased our ability to store and process lots of disparate data in real-time, the technology is only as useful the people managing it. As Bill Wise, CEO of Mediaocean, highlights, the costs of getting it wrong increase as our reliance on data grows.

To be clear, we've long been able to query so-called "Big Data." We've had expensive data warehousing and Business Intelligence tools for many years. The great innovation of tools like Hadoop is that they've made such capabilities available as free, open-source tools that run on commodity hardware, essentially paving the way for anyone and everyone to become a data scientist.

Therein lies the problem.

Taking an influential paper on economics and intelligence efforts around the Boston bombing suspects as background, wherein a few missing rows in Excel and a misspelling of Boston Marathon bombing suspect Tamerlan Tsarnaev's name, Wise points out that "data management tools (i.e., the FBI’s systems and Excel) were undone by fairly simple errors," with terrible results. In other words, as much as we may believe Big Data is as simple as "Input data into Hadoop, out come insights!", the reality depends heavily on the people querying that data.

And the bigger the data, the bigger the likelihood we'll read it wrong, as Wise posits:

[M]ore human/data interaction means a lot more room for error (and inefficiency) around increasingly critical data sets - which... can have very serious results... If Big Data can’t fit hand-in-glove with usability and workflow, a lot of the promise of big data will be empty data crunching. That’s not just a problem for getting where we want to be in the evolution of computing. It’s a situation that can lead to bad data management - which translates into bad economics and, sometimes, far worse.

This confirms renowned statistician Nate Silver's arguments that data doesn't speak for itself, but is instead corrupted by our biases. Worse, the bigger the data set, the more noise to sift through: "the noise is increasing faster than the signal. There are so many hypotheses to test, so many data sets to mine - but a relatively constant amount of objective truth."

Often, misunderstanding our data simply means our businesses will run more inefficiently or, at least, no more efficiently than before. But if Wise is correct, getting our data wrong can have disastrous consequences.

Which means, as I've argued before, that we really need to look inside our organizations for "data scientists," because context is critical to effectively querying our data, as well as knowing which data to collect in the first place. It also means, as Kate Crawford argues in Harvard Business Review, "data scientists should take a page from social scientists, who have a long history of asking where the data they're working with comes from, what methods were used to gather and analyze it, and what cognitive biases they might bring to its interpretation." 

In other words, the more data has the potential to impact our organizations, the more humble and circumspect we should become in using it. The consequences of reading our data wrong scale with the volume and velocity of that data.

Image courtesy of Shutterstock.