IT-backed business initiatives generated Waste when the initiatives:
* Don?t delivery on their promise
* Cost way more than expected or than the benefit they deliver
* Become operational long after their target date
These initiative miss their mark not because of the operational data is weak or missing -- the summarized view of scads of transactional data that is produced by the operations of a company or project -- but because of organizational, political, and cultural issues. These issues don?t fit the input-output model (i.e. the 100 pennies equals one dollar, or x hours of work equals one unit produced).
Information that reveals such issues tends to be tacit, and reflects what people perceive and believe. Sometimes, it represents just plain common-sense wisdom and gets ignored or suppressed during the decision-making process because it?s not operational data.
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.
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.]
