Enterprise systems are inherently complex, often involving many business processes, people, and organizations across a company. Given this built-in complexity, it’s no surprise that failures abound; it’s amazing these systems function at all.

We could make these same comments about any complex, mission critical system. For example, look no further than the space program or health care delivery. In both cases, massive complexity is connected to a need to get things right: failure means potential loss of life.

To say that complicated systems are more prone to break down than simpler systems is obvious. But there are also other, more subtle truths regarding failure and complex systems.

A paper copyrighted in 1998, called How Complex Systems Fail and written by an M.D., Dr. Richard Cook, describes 18 truths about the underlying reasons complicated systems break down. On the surface the list appears surprisingly simple, but deeper meaning is also present. Some of the points are obvious while others may surprise you.

THE EIGHTEEN TRUTHS

The first few items explain that catastrophic failure only occurs when multiple components break down simultaneously:

1. Complex systems are intrinsically hazardous systems. The frequency of hazard exposure can sometimes be changed but the processes involved in the system are themselves intrinsically and irreducibly hazardous. It is the presence of these hazards that drives the creation of defenses against hazard that characterize these systems.

2. Complex systems are heavily and successfully defended against failure. The high consequences of failure lead over time to the construction of multiple layers of defense against failure. The effect of these measures is to provide a series of shields that normally divert operations away from accidents.

3. Catastrophe requires multiple failures - single point failures are not enough. Overt catastrophic failure occurs when small, apparently innocuous failures join to create opportunity for a systemic accident. Each of these small failures is necessary to cause catastrophe but only the combination is sufficient to permit failure.

4. Complex systems contain changing mixtures of failures latent within them. The complexity of these systems makes it impossible for them to run without multiple flaws being present. Because these are individually insufficient to cause failure they are regarded as minor factors during operations.

5. Complex systems run in degraded mode. A corollary to the preceding point is that complex systems run as broken systems. The system continues to function because it contains so many redundancies and because people can make it function, despite the presence of many flaws.

Point six is important because it clearly states that the potential for failure is inherent in complex systems. For large-scale enterprise systems, the profound implications mean that system planners must accept the potential for failure and build in safeguards. Sounds obvious, but too often we ignore this reality:

6. Catastrophe is always just around the corner. The potential for catastrophic outcome is a hallmark of complex systems. It is impossible to eliminate the potential for such catastrophic failure; the potential for such failure is always present by the system’s own nature.

Given the inherent potential for failure, the next point describes the difficulty in assigning simple blame when something goes wrong. For analytic convenience (or laziness), we may prefer to distill narrow causes for failure, but that can lead to incorrect conclusions:

7. Post-accident attribution accident to a ‘root cause’ is fundamentally wrong. Because overt failure requires multiple faults, there is no isolated ‘cause’ of an accident. There are multiple contributors to accidents. Each of these is necessary insufficient in itself to create an accident. Only jointly are these causes sufficient to create an accident.

The next group goes beyond the nature of complex systems and discusses the all-important human element in causing failure:

8. Hindsight biases post-accident assessments of human performance. Knowledge of the outcome makes it seem that events leading to the outcome should have appeared more salient to practitioners at the time than was actually the case. Hindsight bias remains the primary obstacle to accident investigation, especially when expert human performance is involved.

9. Human operators have dual roles: as producers & as defenders against failure. The system practitioners operate the system in order to produce its desired product and also work to forestall accidents. This dynamic quality of system operation, the balancing of demands for production against the possibility of incipient failure is unavoidable.

10. All practitioner actions are gambles. After accidents, the overt failure often appears to have been inevitable and the practitioner’s actions as blunders or deliberate willful disregard of certain impending failure. But all practitioner actions are actually gambles, that is, acts that take place in the face of uncertain outcomes. That practitioner actions are gambles appears clear after accidents; in general, post hoc analysis regards these gambles as poor ones. But the converse: that successful outcomes are also the result of gambles; is not widely appreciated.

11. Actions at the sharp end resolve all ambiguity. Organizations are ambiguous, often intentionally, about the relationship between production targets, efficient use of resources, economy and costs of operations, and acceptable risks of low and high consequence accidents. All ambiguity is resolved by actions of practitioners at the sharp end of the system. After an accident, practitioner actions may be regarded as ‘errors’ or ‘violations’ but these evaluations are heavily biased by hindsight and ignore the other driving forces, especially production pressure.

Starting with the nature of complex systems and then discussing the human element, the paper argues that sensitivity to preventing failure must be built in ongoing operations. In my experience, this is true and has substantial implications for the organizational culture of project teams:

12. Human practitioners are the adaptable element of complex systems. Practitioners and first line management actively adapt the system to maximize production and minimize accidents. These adaptations often occur on a moment by moment basis.

13. Human expertise in complex systems is constantly changing. Complex systems require substantial human expertise in their operation and management. Critical issues related to expertise arise from (1) the need to use scarce expertise as a resource for the most difficult or demanding production needs and (2) the need to develop expertise for future use.

14. Change introduces new forms of failure. The low rate of overt accidents in reliable systems may encourage changes, especially the use of new technology, to decrease the number of low consequence but high frequency failures. These changes maybe actually create opportunities for new, low frequency but high consequence failures. Because these new, high consequence accidents occur at a low rate, multiple system changes may occur before an accident, making it hard to see the contribution of technology to the failure.

15. Views of ‘cause’ limit the effectiveness of defenses against future events. Post-accident remedies for “human error” are usually predicated on obstructing activities that can “cause” accidents. These end-of-the-chain measures do little to reduce the likelihood of further accidents.

16. Safety is a characteristic of systems and not of their components. Safety is an emergent property of systems; it does not reside in a person, device or department of an organization or system. Safety cannot be purchased or manufactured; it is not a feature that is separate from the other components of the system. The state of safety in any system is always dynamic; continuous systemic change insures that hazard and its management are constantly changing.

17. People continuously create safety. Failure free operations are the result of activities of people who work to keep the system within the boundaries of tolerable performance. These activities are, for the most part, part of normal operations and superficially straightforward. But because system operations are never trouble free, human practitioner adaptations to changing conditions actually create safety from moment to moment.

The paper concludes with a ray of hope to those have been through the wars:

18. Failure free operations require experience with failure. Recognizing hazard and successfully manipulating system operations to remain inside the tolerable performance boundaries requires intimate contact with failure. More robust system performance is likely to arise in systems where operators can discern the “edge of the envelope”. It also depends on providing calibration about how their actions move system performance towards or away from the edge of the envelope.

THE PROJECT FAILURES ANALYSIS

Rob England, who writes the IT Skeptic blog, brought the paper to my attention. Rob describes its importance on his blog:

It is not often you read something that completely changes the way you look at IT. This paper How Complex Systems Fail rocked me. Reading this made me completely rethink ITSM, especially Root Cause Analysis, Major Incident Reviews, and Change Management.

It dates from 1998!!. Richard Cook is a doctor, an MD. He seemingly knocked this paper off on his own, it is a whole four pages long, and he wrote it with medical systems in mind. But that doesn’t matter: it is deeply profound in its insight into any complex system and it applies head-on to our delivery and support of IT services.

Read this paper. And READ it: none of this 21st Century 10-second-attention-span scanning. READ IT HARD. Blow your service management mind.

My take. This is one of the most insightful and important papers on failure I have read. Although focused on health care delivery, the lessons are equally applicable to large enterprise software systems.

[Photo of the long tail of failure from iStockphoto.]

In addition, the magazine’s Executive Editor, Tony Collins, is one of the top journalists covering IT failures; Tony takes no prisoners but is always fair and impartial. His work has served as an inspiration to me.

SHAMELESS, UNREPENTANT PLUG

Yes, I want you to vote — for my blog! To make it easy, please follow this little guide.

Step 1. Click the voting page

Step 2. Scroll down to number 7, the Project Management category, as shown here:

Step 3. Do the right thing….

In this guest blog post, Miko Matsumura, Vice President and Chief Strategist of Software AG, offers a humorous look at the Enterprise 2.0 movement. In addition to his position at Software AG, Miko is the author of the book, SOA Adoption for Dummies.

Miko’s underlying message is important: to be successful, Enterprise 2.0 activities must remain rooted in the practical realities of real companies, processes, and corporate cultures. I share this perspective. Although the tone and images are funny, I assure you the message is serious.

———–

It’s a cool sunny day in San Francisco. I’m at the Moscone center where there’s some bustle around the Enterprise 2.0 conference. You can tell it’s an Enterprise conference because, unlike the Web 2.0 Conference, there’s no free pass even to the show floor. Also, the full pass is about $2500 bucks.

As I’ve been discussing on Twitter @mikojava and in my blog, here are my top five definitions of Enterprise. Feel free to chime in with your views via Twitter, email or my blog.

One way to define Enterprise is:

en⋅ter⋅prise:
/ˈɛntərˌpraɪz/ [en-ter-prahyz]
-noun

5. Stuff I wouldn’t do unless you paid me.

This definition puts Enterprise squarely in the camp of crime scene janitorial services. It adds a concept of “professional” to the discussion and establishes the Enterprise as the realm of uncomfortable clothing.

I recall reconnecting with Arthur Van Hoff after our adventures in Java and having him laugh at me because I was wearing (in his words) an “IQ Restrictor,” his parlance for a necktie. This definition also puts a dynamic tension between the “Suits” at the Enterprise 2.0 conference and the boho hipsters wearing the Emo Hair.

4. Software that sucks.

This was the definition I evoked in my post “The Human Enterprise.” To be honest, I introduced the idea of “The Human Enterprise” as a direct counter-proposal to “Enterprise 2.0.”

I think the piece that was missing from The Human Enterprise is the extent to which fragmentation plays a role in the essential nature of the Enterprise, which is a theme I’ve been addressing more lately in terms of the effect of sheer size on the Enterprise.

3. A venture requiring industriousness or courage.

This definition deserves some attention because it in some ways captures exactly what’s missing from the current debate around the Enterprise. The extent to which courage has been slowly sapped by the ravages of the Great Recession and “job security” is to some extent disheartening. In particular, efforts to rejuvenate the complex IT System Architecture and to mitigate the effects of Entropy and the “Heat Death of IT” have been met with cries of “SOA is Dead.”

So here’s a call for the restoration of courage in IT, to boldly go. Set phasers on “frappe.”

2. Dead stuff that used to matter.

Rumors of the death of Enterprise Software have been greatly exaggerated (nice post by David Hornik). The thing people find hard to understand about the longevity of most Enterprise IT is that “dead” software actually lives a long time. In fact dead software (nice post by James Governor) runs 90% of the economy.

Another word for “legacy” is IT projects that worked. The word for IT projects that didn’t work is “consolidation.” This should be especially resonant for folks at the Enterprise 2.0 conference, since 99% of the projects spawned by “Enterprise 2.0″ will fall into the latter category. We will have won when there’s “Legacy Enterprise 2.0″ apps out there.

1. An organization whose mission requires significant size, growth and longevity.

I present this as the number one definition in an attempt to extract the most salient feature of the Enterprise to casual observers. The definition is designed to be inclusive of Government organizations. I don’t want to open a can of worms (big government vs. small government), but arguably some “missions” such as the regulation of interstate commerce and providing for the common defense would require a degree of size, scale and longevity.

But what’s more interesting about this definition are the implications:

• At this scale, the organization struggles with whether it’s “too big to fail” or “too big to succeed.”
• The implications of size include fragmentation of organization into tribes.
• The implications of growth include fragmentation of markets into niches.
• The implications of longevity include fragmentation of technology into silos.

These forms of fragmentation are the key challenge of Enterprise, and the points that some E2.0 companies seem to miss. Trying to repackage consumer apps and peddle them to Enterprises misses the unique pain of Enterprise. I’ve spoken and written extensively about the effect of technological and organizational silos, for example in my book SOA Adoption for Dummies. But lately I’ve been thinking about the effects of market fragmentation.

There comes a tipping point in any large commercial sector Enterprise where the market for the flagship product or service becomes saturated. At this juncture, the revenue growth challenge becomes less about attracting and delighting new customers but rather about sucking as much money out of existing customers as possible.

The example I will provide for you is the Apple iPod. At the risk of offending fanboys, the iPod market is saturated. I must own a half dozen iPods. Now I go running with my iPod nano 3g. When my 3g failed, I went to the Apple store to buy a new iPod. The way Apple segmented their products, they had created a low-end model at $59 dollars (the clip) which had no screen; a “medium” range but portable option (the nano) at $150; and then the “platform” model, the iPod Touch at $199.

The nano costs only 50 bucks less than the Touch, but for users who want to run with an iPod, the Touch is too big. Since they overloaded the nano with features I don’t want (accelerometer, video camera, FM radio) they were able to jack up the price.

This kind of behavior exists in many mature markets, including cell phone plans. The cell phone companies have “package designers” who specifically design packages including SMS and email that rack up a maximum number of overcharges and fees. They design packages that exploit the gap between what users think they will use and what they actually use based on data mining in their demographics.

This type of behavior makes the Enterprise essentially the “enemy” of the consumer. Of course, we want successful companies to have profits so they can fuel the next generation of investment. I certainly want Apple to succeed, and I bought their product even though I found it mildly distasteful (it was still the best player for my purpose).

———–

I wrote this post in the hopes that it would stimulate discussion about how people define the “Enterprise” in “Enterprise 2.0.”

[Thank you to Miko Matsumura, Vice President and Chief Strategist of Software AG, for writing this guest post, which is reprinted from his blog. All images supplied by the author.]

I’m assembling the definitive Twitter list of folks who have demonstrated deep insight and commitment into analyzing the causes and prevention of IT failures. The list is called IT Failure Insights and I need your help to get it right.

If you know someone who should be on that list, please send me a message on Twitter.

Before adding anyone to the list I will ask to see blog posts, Twitter messages, papers the person has written, or other examples demonstrating clear connection between their professional life and this topic.

[Photo from iStockphoto]

Every seasoned executive knows that gaining detailed and accurate information about his or her organization’s activities is a challenging and ongoing struggle. Disconnects between operational data and management decision-making lead to inefficiency, waste, and ultimately to extreme failures of the type described in this blog.

Usually, some members of an organization do possess accurate early warning information regarding potential problems. However, as we have seen in situations ranging from Enron to financial industry practices that kicked off the current recession, surfacing that information can be difficult.

I asked top auditing services analyst and former BearingPoint managing director, Francine McKenna, to place this issue in context. Francine told me:

It’s a classic problem rooted in human nature. Information in large, complex, and geographically dispersed organizations tends to become diluted and distorted as it flows up the chain. Even worse, some individuals redesign information flowing through their hands based on personal goals and objectives.

The best organizations recognize this state of affairs and create standardized policies, procedures, and governance monitoring activities to overcome it. Despite these efforts, however, the problem remains a very real challenge.

Detecting and amplifying “weak signals.” Techniques that reveal hidden vulnerabilities are a valuable weapon in the fight against project failure.

My recent post, Learning from the weak signals of failure, discussed the importance of methods that detect and amplify these weak signals:

Many so-called “victims” of failed projects claim they were blindsided by problems that arose suddenly out of nowhere. In reality, the entire notion that failures spontaneously arise without warning is nonsense.

Most troubled projects suffer from poor communication across information silos, which is a variant of the information disconnects discussed above. For example, communication problems are common between management and workers; IT and the lines of business; internal groups and external vendors; and so on. Successful methods for revealing hidden sources of potential failure are therefore particularly valuable when they operate across traditional organizational boundaries and silos.

• Related: Learning from the weak signals of failure

Traditional methods to uncover and amplify weak signals include interviewing project participants, applying business intelligence techniques to data associated with a project, and using portfolio management techniques such as resource analysis.

Recently, several companies have developed new approaches for detecting weak signals. These techniques are applicable to preventing waste and inefficiency associated with business initiatives of all sorts.

Dachis Group. Social business consultancy, Dachis Group, uses a concept called dynamic signals to explain the undercurrent of information flows running through an organization. As this schematic diagram illustrates, Dachis intends for this approach to measure points in a workflow or process, separating important information from background noise:

In an email, Dachis Group principal, Jevon MacDonald, explained that dynamic signal techniques allow one to recognize the “heartbeat” of an organization:

Dynamic signal helps users see their work in the context of other parts of the organization, for example, the activities in which other people or machines are engaged or the progress of a workflow. Automated techniques help separate important information from background noise and allow us to maintain an ongoing “ambient awareness” of activity status across an organization or network.

The Dachis website states that the company:

[H]elps its clients utilize a measurement framework to capture value in social interaction and conversation. We recognize that clients are not at a loss for data, but actionable interpretation.

The concepts appear interesting and promising, but additional detailed information is required before we can understand how Dachis applies this approach to practical business situations.

Asuret. IT failures consultancy, Asuret, uses an approach based on concepts of collective intelligence to reach inside an organization and discover what’s really going on. Asuret applies sentiment-analysis techniques to uncover perception gaps and measure the extent of mismatched expectations among organizational silos and even among individuals.

Although Asuret’s techniques are generalizable to a range of business situations and problems, a core focus is reducing waste associated with troubled IT projects. To accomplish this goal, the company measures projects against a “profile” that describes common reasons IT projects get into difficulty. The profile addresses issues such as business planning, change management, executive sponsorship, stakeholder engagement, and similar fundamental aspects of running a successful project or initiative.

To measure the degree to which vulnerability drivers are present on a project, for example, Asuret asks stakeholders to evaluate simple indicators that describe aspects of the organizational environment. The following illustration shows one of these indicators:

Asuret aggregates these indicators to create a large dataset, from which it derives inferences about the business initiative under consideration. By decomposing this data differently from the way in which it was collected, Asuret reports expectation- and alignment-mismatches across information silos in areas such as IT vs. finance; system integrator vs. internal departments; and so on.

This screen summarizes perception differences between executive management and IT with respect to a particular project’s business case. Quantifying this information allows project stakeholders to use data-driven metrics as a reference for achieving consensus during team discussions:

In addition to identifying gaps among organizational departments and silos, Asuret’s measurement techniques also reveal areas where decision makers should apply training and other support resources, as this illustration shows:

My take. Organizations can improve decision-making and reduce IT failures by applying tools, techniques, and processes that increase information transparency. Quantitative techniques are beneficial by helping create data points against which organizations can prioritize issues, achieve consensus, and therefore make decisions more rapidly.

Discussing these issues, MIT Sloan Management Review says:

Our own research suggest that fewer than 20% of global companies have sufficient capacity to spot, interpret and act on the weak signals of forthcoming threats and opportunities.

And that, folks, is precisely why these techniques are important.

[Disclosures: As CEO of Asuret, I am working with fellow ZDNet blogger, Dion Hinchcliffe to apply these concepts to the Pragamatic Enterprise 2.0 initiative, which Paul Greenberg reviewed on ZDNet. Photo from Wikipedia Commons and colorized by Michael Krigsman.]

In a post today. ZDNet open source blogger, Dana Blankenhorn, says the primary value of open source software is transparency rather than low cost. He then argues that open source software offers at least a partial solution to the problem of IT failures. Let’s examine that view.

Dana argues that open source code transparency aligns the interests of customers and vendors, which can have a positive effect on IT project outcomes:

With code visibility, you and your vendors become partners in trying to make something work. The vendor can’t over-promise, but you can’t over-assume either. This may be one of main hidden reasons for IT failure, the two sides of the transaction not being on the same page.

From an IT failures perspective, this logic consists of two primary components:

1. Shared visibility into open source code reduces hidden assumptions and makes explicit what the vendor is actually selling to the customer.
2. Such transparency can reduce failure by forcing alignment between vendor and customer goals.

Although Dana raises an interesting and important question, I do not share his confidence that implementation projects based on open source software should more successful than those based on commercial software.

In my experience, most failures associated with packaged software arise from expectation mismatches in the business, rather than technical, domain. Custom software development projects are even more complicated, since these situations include creating something that does not yet exist.

This diagram summarizes my view regarding why many IT projects that are late, over-budget, or don’t deliver planned results:

As you can see, the primary problems are organizational in nature; failure is rarely due primarily to code or technology. In addition, we cannot ignore basic differences in the vested interests of vendors and customers. The Devil’s Triangle concept explains the large role of conflicts of interest in driving failed projects.

To uncover the true reasons project fail, I suggest looking at poor judgment, politics, and personal agendas. Yes, it’s ugly, but sometimes the truth is just not pretty. I wish we could rely on open source to help improve project success rates, but I just don’t see it.

Sound off with your view — can open source prevent failed IT projects?

[Image from iStockphoto]

Many so-called “victims” of failed projects claim they were blindsided by problems that arose suddenly out of nowhere. In reality, the entire notion that failures spontaneously arise without warning is nonsense.

Still, this experience is sufficiently widespread that it appears in a Dilbert cartoon. Wally asks Dilbert, “How’s your project coming along?” Dilbert replies, “It’s a steaming pile of failure. It’s like fifteen drunken monkeys with a jigsaw puzzle.” When the boss asks, “How’s your project coming along?” Dilbert responds sardonically, “Fine.”

This Dilbert interchange expresses a fundamental truth for understanding failed projects: in most cases, someone associated with the project knew in advance about impending difficulty. For example, an engineering manager might realize early in the project that his team will not be able to achieve certain milestones on time.

Similarly, consider the game of project failure chicken. Management sets an unrealistic product ship date, which the Engineering and Design groups, for example, each know is impossible to meet. Since neither group wants to appear weak, they both tell management the schedule is workable, each hoping the other will admit defeat first. In project failure chicken, neither group wants to yield to the other, leading to the worst possible outcome for management.

In these examples, accurate knowledge about the true state of the project is present inside the organization, yet remains hidden from management because it is diffuse and unfocused.

If denial is the handmaiden of failure, then acknowledgment can be a strong harbinger of success. But what, precisely, should we acknowledge?

• Related: Denial: The secret IT killer

Respected CIO leadership expert, Chris Curran, addresses this question in a blog post about the concept of finding weak signals:

[M]aybe we are ignoring some fundamental, but less obvious signs that our projects are not positioned for success. These signs, or weak signals, require different mindsets and toolsets to gather, track and act upon.

Chris’ comments responded to an article in the MIT Sloan Management Review titled, How to Make Sense of Weak Signals, which offers this definition of weak signals:

A seemingly random or disconnected piece of information that at first appears to be background noise but can be recognized as part of a significant pattern by viewing it through a different frame or connecting it with other pieces of information.

The article goes on to summarize the key challenge:

There is a major difference between taking in signals and realizing what they mean. Managers as well as organizations tend to see the world in a certain way and confuse their mental maps with he territory. Weak signals that don’t fit are often ignored, distorted or dismissed, leaving the company exposed.

An upcoming blog post will explore this issue further and describe the efforts of several firms to address the problem.

[Photo from Wikipedia Commons.]

Industry analyst firm, Gartner, is the target of a lawsuit from software vendor, ZL Technologies, challenging the “legitimacy” of Gartner’s Magic Quadrant rating system. The suit has brought forth an array of divergent opinions.

Background. As one of the top analyst firms, with revenue in excess of a billion dollars, Gartner’s opinions and recommendations carry substantial weight with technology buyers and influencers.

On a special website page devoted to the lawsuit, ZL Technologies claims that Gartner’s Magic Quadrant does not present a fair and accurate portrayal of the software market. The company says:

Gartner’s use of their proprietary “Magic Quadrant” is misleading and favors large vendors with large sales and marketing budgets over smaller innovators such as ZL that have developed higher performing products.

Here is the original legal filing:

ZL v Gartner Complaint

Gartner’s legal response is highly technical, but states that Magic Quadrant reports are “clearly opinion” and therefore protected by the first amendment right to free speech. See page ten of Gartner’s Motion to Dismiss the ZL complaint:
Gartner Motion to Dismiss

The four legal documents made available to date are available by clicking here.

Divergent opinions. Lawsuits often involve strong emotions and polemic, and observers in this case have divided opinions.

In the anti-Gartner camp, ZDNet blogger, Dennis Howlett, quotes a third party questioning Gartner’s objectivity and integrity:

‘And you know what, it doesn’t stop at tin cupping. They double dip. They say they’ll help you with strategy, charge you for the pleasure and then go on to sell analysis based on that to say exactly the opposite.’

Howlett adds:

[T]here is no denying that Gartner opinion stated as fact has become a powerful tool for vendors to wave in front of customers.

Taking a different view, Mark Logic’s CEO, Dave Kellogg, blogs:

“Sour grapes” spring to mind as an immediate reaction. In fact, ZL concedes that they’ve been ranked in the “niche” segment of every email archiving quadrant since 2005. (Ouch!) But they nevertheless argue that bigger stakes are in play and that this is not only about ZL, but Gartner itself, technological innovation, and very nearly preservation of the American way of life.

THE PROJECT FAILURES ANALYSIS

The lawsuit raises important issues about the role of industry analysts in the enterprise software ecosystem.

Analysts help technology buyers and sellers understand the market environment in which both groups operate. This intelligence helps technology vendors determine the strategies and features that are important to buyers. Likewise, analysts guide enterprise buyers through the challenging process of procuring large, expensive systems. When this system works properly, analysts provide a valuable service.

Carter Lusher, strategist at analyst relations consulting firm SageCircle, believes reputable analyst firms strive for neutrality, but don’t always communicate limitations of their research. He told me:

Savvy research consumers use a variety of sources for information and advice. Unfortunately, consumers of analyst research don’t always know how to interpret reports such as Gartner’s Magic Quadrant. Analyst firms should do a better job educating readers.

Other observers, such as longtime industry analyst Jonathan Yarmis, question the relationship between commercial contracts and favorable analyst coverage. When Gartner analyst, Thomas Bittman, wrote a blog post staunchly defending the analyst business model, Jonathan responded with a strongly worded comment:

If you think all that money vendors are spending is purely because of your insights, again you’re being naive. There’s an influence component and an insight component. Why do you think they spend so much more with Gartner? Are your insights really that much keener? Or are the vendors more concerned with your market reach and therefore the need to more strongly influence you compared with everyone else?

I spoke with Jonathan and asked him to elaborate:

Most analysts have integrity and I do not believe that a commercial relationship with vendors overtly influences analyst opinions. However, it is also true that money buys access, which, deftly used, can lead to improved favorability.

My take. Analyst research and reporting is not an exact science, which does lead to real or perceived conflicts of interest. The analyst industry can reduce potential conflicts by improving transparency around how it forms opinions and makes recommendations.

Analyst clients should ask firms for spreadsheets and other supporting documentation that explain how vendor and product rankings are determined. Although differences of opinion will always exist, transparency helps ensure impartiality.

To increase transparency, analyst firms should also disclose their revenue relationships with vendors. How much do they get from whom, and what is the break down between retainer and consulting revenue? Consulting is much more influential than subscription research or retainer arrangements, because it represents one-on-one relationships with an individual analyst.

Regardless of who wins or loses the lawsuit, it is beneficial to shine the light of day into this important segment of the technology industry.

CRM failure remains a significant problem in many organizations. For that reason, it’s important to explore why so many of these projects do not achieve their potential.

• Related: CRM failure rates: 2001-2009

For this reason, I asked Jill Dyché, a Partner with IT services and management consulting firm, Baseline Consulting to write a guest post for this blog. Jill is the author of three books on the business value of technology, including her latest, Customer Data Integration: Reaching a Single Version of the Truth. You can reach Jill on Twitter (@jilldyche) or email (jilldyche@baseline-consulting.com).

Most folks involved in CRM don’t quickly recognize the warning signs of failure, causing bad projects that die a slow, agonizing death. These folks need Arnold Schwarzeneggers’ Commando to stop the madness and put their project out of its misery.

Arnold’s role of “exit champion,” in which he kills for the greater good, reminds us that not all CRM projects are fit to survive. However, most corporate cultures discourage naysayers, so even well-informed people who sense a CRM effort going sideways avoid making waves and don’t call out obvious warning signs.

This cultural condition creates groupthink and denial, a type of organizational neglect that researcher Isabelle Royer calls “the seductive appeal of collective belief.” We’ve all seen or heard this kind of logic:

Our project must be going fine, since no one is complaining, even though we’re already over-budget and the development team is still being trained on the software. After all, CRM is a hot topic with analysts and our executives are all on board. Someone must know what they’re doing. Right?

Although the warning signs of CRM-gone-wrong may appear vague, they are actually quite consistent across companies, vendor solutions, and development plans. Here are four to watch for:

1. Choosing a bad executive sponsor. Few organizations take time to qualify their CRM sponsor properly. Great sponsors are skilled at articulating business requirements for customer information, funding the acquisition of software products, and managing up effectively. Bad sponsors fail to communicate sufficiently and may be unwilling to intercede when project participants disagree on critical matters. If things go wrong, a poor sponsor may relinquish responsibility for solving the problem or, even worse, disavow the entire project and walk away.
2. Suspending status reports. CRM projects usually start with a bang. When executives push a new focus on the customer, the organization allocates budget, plans the project, purchases software, convenes the development team, and circulates regular status reports to stakeholder. Until one day those reports stop. Be wary anytime progress is suddenly no longer forthcoming. When the project team clams up, it’s not good news.
3. Revisiting past decisions. Something is wrong when stakeholders second-guess decisions they’ve already approved. You’ll see this expressed as accusatory questions such as, “Why did the selection team choose that lousy software.” That kind of language means confidence has eroded, regarding basic aspects of the project. Historian Barbara Tuchman calls this tendency to reexamine old decisions “disaffection of constituents.” It’s a warning sign you shouldn’t ignore.
4. Focusing on the wrong issues. Some teams spend more time studying competitors’ Facebook page, or debating merits of on-line communities, and less time integrating customer data and ensuring the project meets business requirements. Failure is coming when folks succumb to bright shiny object syndrome, focusing on so-called “social CRM” rather than improving customer-facing processes or enhancing customer data quality.

CRM projects that display these warning signs are in real danger.

Should you become the exit champion who calls out warning signs of a CRM effort in jeopardy? It’s a tough question because in many organizations, the risks of speaking unpopular truths can outweigh the rewards.

Schwarzenegger’s hero in The Running Man insisted, “I’m not into politics. I’m into survival.” That’s not a bad lesson for those of us trying to run successful CRM projects.

[Thanks to Jill Dyché for writing this guest post. Image via iStockphoto.]

Catz opened her part of the keynote by explaining what she called the “back story” behind Oracle’s acquisition strategy. She did this with a humorous look at what would happen if we bought cars the way we buy enterprise technology.

In such world, Catz said we would go online to buy thousands of disconnected parts from many vendors, which our children would assemble into a completed car because the parts would not come with instructions. Just as we finished assembling the car, she continued, a light would go on indicating that an upgrade or patch is required. Catz said, “We would then do it all again.”

Catz used this car assembly story as a metaphor for product complexity in the enterprise. According to Catz and Phillips, Oracle reduces this complexity by bringing together under one roof infrastructure, hardware, and database products that are “engineered to work together.”

This diagram expresses Oracle’s end-to-end vision:

Regarding the “open” tag line on the slide, Catz said, “We are slavishly devoted to open standards.” Wow, that’s a pretty strong statement.

THE PROJECT FAILURES ANALYSIS

From a project failures perspective, important truths lie beneath the cute story about assembling cars at home from parts purchased online. As Catz correctly points out, many organizations purchase enterprise technology in pieces from multiple vendors, which can make the selection and implementation process time-consuming and expensive for the customer, relative to buying from a single vendor.

I discussed these points during a follow-on conversation with Paco Aubrejuan, Oracle’s Vice President of PeopleSoft Enterprise, who explained the benefits of single-vendor integration:

Integrating transactions at a technical level on starts to address user needs. From a business standpoint, however, users want to correlate data from multiple applications into a unified view, to gain actionable information that helps make decisions. We bring that benefit.

Another perspective. Even though integration can reduce implementation complexity on customer projects, large vendors may introduce another set of risks.

While a large end-to-end vendor can offer greater simplicity, the trade-off involves the customer transferring power to that vendor. In a single-vendor world, customers who invest in large systems can become beholden to the large vendor, which gains greater control over pricing, product features, maintenance costs, and future development. This is a major concern since replacing enterprise vendors is expensive, difficult, and time-consuming.

Whether you view Oracle’s strategy as positive or negative may depend on whether you trust the company to act in customers’ long-term best interests. It’s a complicated equation that involves not just pricing, but also Oracle’s product road map and ongoing vision.

My take. There’s no magic bullet for reducing enterprise complexity, and Oracle’s vision offers one particular path forward. Without doubt, observers with strong opinions will line up behind both camps.

What do you think about Oracle’s plan to dominate the enterprise?