In our just-published Cutter Benchmark Review article (see "Linking IT Budgeting, Governance, and Value," Vol., 8, No. 7), we report that only 27% of managers of large companies believe their IT is superior to that of their competition. (By "large," we mean companies with more than US $50 million annual spend.) For all companies, regardless of size, only 39% of managers believe that their company's IT is superior.

Wow.

I once wrote a project plan for a global team with a business challenge of developing a model for the formation and management of worldwide strategic alliances. The seven team members were physically situated in different cities and time zones; we were part of an international executive education program to study key management issues of global enterprise.

Increasingly, we're hearing about cloud computing, with a host of companies -- including Amazon, Google, Hewlett-Packard (HP), IBM, Microsoft, Oracle, SAP AG, and Sun -- all promoting it to varying degrees.

Is the role of your change management specialist really that of a trainer? While training is essential to a successful change management program, it is only one piece of the puzzle. As with any change program, there is considerable change happening with many potential points of failure. So how do you mitigate them all?

Advanced data visualization tools have been around for some time. They first gained a following among scientists and engineers, who used them to build models for fluid-flow analysis, aerodynamic simulation, and other complex applications involving large data sets with many cause-and-effect variables.

On the face of it, we all can agree that how a company does its work matters a great deal. The continual interest in reorganization, the business process reengineering explosion of the 1980s, and the now nearly universal acceptance of the business process and organizational structure as fundamental to good performance give proof to this truth.

Many agile teams do their estimations in abstract measures, such as story points or complexity points, rather than using effort or time. Though the difference is subtle, abstract measures allow a self-adapting estimation process, decrease wish-based planning, and increase realism.

In far too many organizations, bureaucracy is an inherent impediment to success.

I was reading an article today that claimed that parallel computing is the next big thing [1]. I heard a similar refrain in an interview with someone from Intel. The premise behind this idea is that the first age of Moore's Law is ending and the hardware guys are beginning to panic.

Necessity is not only the mother of invention but apparently the father of invention offshore. A gaping need for domestic technical talent is causing US technology-driven organizations to site product development and R&D functions where there exists an abundance of engineers.1 Not surprisingly, this flavor of strategic sourcing has little to do with offshoring's historical allure -- saving money.

How many times have you left your office at the end of the day reflecting, "I spent the entire day in meetings"? And then wondered to yourself, "When do I find time to get some work done?" You also possibly didn't remember to think, "I wonder what's happening elsewhere in the organization." Does this scenario sound familiar?

Practically everyone has started down the path toward software-oriented architecture (SOA). Industry surveys show that 80% or more of enterprises have already adopted, or are in the process of adopting, some kind of SOA. We're told we need to for a variety of reasons, although the ones we use are often not the best reasons for us to consider it.

Second Life [1] and virtual worlds like it are Internet-based platforms for collaboration and socializing, which provide users with a 3D experience and a community. Second Life is a platform on top of which users, called "residents," can create their own world with 3D content and associated software, realize their ideas, and even earn money.

BI and business performance management vendors have offered facilities that give mobile users the ability to view and interact with performance-related information -- managed by their BI tools and analytic applications -- via such wireless devices as smartphones and PDAs for years.

We think of agile methods as a family of approaches aimed at dealing with uncertainty. If there is a high level of uncertainty about what exactly you will end up building, then the agile approaches (all of which call for multiple short iterations with ongoing customer feedback) make enormous sense. With the agile methods, we incrementally steer our way to a useful system.

Last month, I had the privilege of being one of four keynote speakers at the Better Software Conference in Las Vegas. I'm not a gambler, so I didn't partake at the card tables or roulette wheels, but I do watch software project managers gamble all the time, so it seemed to be a fitting place for a technology conference.

Ever since the private sector's recent discovery that meaningful technological innovations didn't necessarily include a price tag, we've had open source innovation. While "free" overstates and misstates the value of open source innovation (OSI), the characterization does not miss by much. So far.

As I write this Advisor in the spring of 2008, the need for enterprise agility is once again a topic on the minds of executives everywhere. Stock market gyrations push prices down 3% one day, up 4% the next. Firms that months before had record profits struggle to stay alive.

A client asks about methods to increase the trust between IT and business managers and staff. It seems the relationship is currently broken: business managers don't trust IT, and the feeling is mutual.

All of a sudden, Microsoft seems to be moving aggressively -- at least, aggressively for Microsoft -- into the area of high-level architecture. I have read about this recently with some hope.

What we often call process improvement is actually change management. The fact that most process improvement initiatives fail or disappoint is primarily due to the lack of appreciation for what matters when attempting to drive change in an organization. It has nothing to do with suggesting new practices or telling people what to do.

Data integration tools provider Talend has introduced an open source data profiling toolset. Talend Open Profiler, as the new software is called, rounds out Talend's open source data integration platform by providing tools to help developers evaluate and document the quality of their data.

In the IT world, most things happen in 10-year cycles. However, the evolution of project management (PM) seems to be on a 20-year cycle. The 1960s and 1970s used a consolidated mainframe approach to project management. The 1980s and 1990s saw a more distributed, desktop-oriented PC approach. Today, in the new millennium, we return to the consolidated approach -- but with a difference.

This article is a continuation of last month's Advisor on quality; specifically, intrinsic quality (see "Investigating Agile: Inside and Out," 19 June 2008). Agile development focuses on delivering customer value as its highest priority.

There was a story not too long ago in the Wall Street Journal on the lessons CEOs are trying to learn from the subprime mess and how these lessons might be applied in their own markets [1]. One of the critical lessons the article highlighted was the importance -- and extreme difficulty -- of being able to deliver bad news to senior executives quickly.