In my latest two posts, I've been exploring what I call disjoint awareness — a state of a collaboration in which its members labor under differing views of what each other is doing or intending to do. Their different understandings about each other's work and intentions can be problematic because it can lead to their interfering with each other's attempts to reach their joint or individual objectives. In last week's post, I noted three factors related to the work itself that tend to enhance the likelihood of disjoint awareness. They are the complexity of the work, the specialization of the collaborators, and the pace of change within the collaboration or the context in which it takes place.
Also contributing to the formation and persistence of disjoint awareness is a widely used approach to problem solving known as analysis. Analysis is a standard way of attacking problems by which we break the problem into supposedly independent pieces (I'll call them work streams here). We than allocate responsibility for each work stream to an individual, team, department, division, subsidiary, or some other appropriate subunit of the entire collaboration. That is, in analysis, we reduce the problem to smaller, more manageable parts, and tackle the parts in parallel.
The method of analysis is very flexible, admitting of various ways of segmenting the work. For example, in a project, we decompose the total work into a set of tasks. The tasks have well-defined initiation criteria, objectives, initial inputs, deliverables, resources, schedules, budgets, and completion criteria. As a second example, in the operational context, we decompose the total work into (usually) functional units such as Administration, Development, Testing, Marketing, Sales, and so on. In some organizations, in the operational context, product, brand, customer, or channel define how we apply analysis to the entire operation.
To most people in modern organizations, analysis seems sensible and workable. And for many problems, it is. But there are difficulties. Typically, when these difficulties arise, we hold responsible the people who are doing the work. In this post, I argue that many difficulties arise not because of the behavior of the people doing the work, but instead because those difficulties are inherent in the choice to use the analytic approach to the work. It is analysis itself that leads to many of these difficulties. And often, analysis "accomplishes" this feat by creating conditions that lead to disjoint awareness.
At least four kinds of difficulties arise when we use analysis to address large, complex packages of work: boundary ambiguity, incomplete coverage, unanticipated work stream interactions, and what I'll call illusory factorability.
- Boundary ambiguity
- Boundary Problems arise not because of the
behavior of the people doing the
work, but instead because of the
choice to use the analytic approachambiguity arises when those responsible for different work streams disagree about who has responsibility for what. Using the concept of the Johari window, we can classify these boundary ambiguities in a way that's useful for resolving them.
- Suppose two teams, A and B, harbor disagreements about which team is responsible for a given piece of the work I'll call W. From the perspective of Team A, and using the Johari Widow, the disagreement can be Open, Blind, Hidden, or Unknown. An Open ambiguity is one in which the teams both know that they're taking differing positions about which team is responsible for W. A Blind ambiguity is one in which Team A is unaware that Team B has a differing view of which team is responsible for W. A Hidden ambiguity is one in which Team A conceals the fact that Team A disagrees with Team B about which team is responsible. And an Unknown ambiguity is one in which neither Team A nor Team B realizes that they disagree about which team is responsible.
- All four states exhibit disjoint awareness in that there is disagreement about responsibility allocation. But to complicate matters, Blind, Hidden, and Unknown ambiguities also exhibit disjoint awareness with respect to the disagreement itself.
- Open ambiguities are subject to resolution politically. But the others cannot be resolved until they're converted to Open ambiguities. Either A or B or some other party must initiate a conversation that can lead to clarifying the situation.
- Because toxic conflict is possible with Blind, Hidden, or Unknown ambiguities, working to bring them into the open offers the best hope for beneficial resolution.
- Incomplete coverage
- Incomplete coverage arises when a piece of necessary work is omitted from all defined work streams. When this happens inadvertently, resolution is relatively straightforward, although budget and schedule adjustments might be necessary.
- But omissions are sometimes intentional or the result of a bias, especially when the omitted work is undesirable or risky. In these cases, omissions can be indicators of organizational dysfunction. Dealing with such omissions is likely to be ineffective until the dysfunction is addressed.
- Unanticipated interactions
- Unanticipated interactions between work streams can occur at the level of the actual work. They can also arise in other dimensions, such as resource contention or schedule conflicts. Political resolution of these interactions is usually straightforward when all parties are components of one political unit. More problematic are virtual collaborations that extend across multiple political units and work streams. Interactions in the virtual context must be resolved at the lowest level of the collaboration that's responsible for all contenders.
- Unanticipated interactions can also appear in "lock up" configurations in which A depends on B, B on C, and C on A. Dependency loops of any length are possible, but the longer loops are more likely to be overlooked until they lock up. They're more difficult to detect in advance when the dependency loop spans multiple work streams, where disjoint awareness can play a more significant role in concealing the loops. Intentional search for such loops early in the effort of a project, or periodically in the case of operations, is a worthwhile endeavor.
- Illusory factorability
- An extreme form of difficulty arising from the analytic approach occurs when the collaboration tries to segment into smaller tasks a problem (or portion of a problem) that cannot be segmented. These problems are susceptible to solution only by teams that can work as one or nearly so.
- One possible structure of such problems is what can be called braided work streams. Suppose we segment a problem into work streams, and assign a team to each stream. If the nature of the work of each stream is well understood by those performing the work, the teams can coordinate their plans and work so as to achieve their respective objectives as they would for any problem. But if the streams aren't understood well enough, disjoint awareness sets in, and the teams have, respectively, different understandings of each other's efforts. The respective stream plans will therefore need corrective action repeatedly and possibly frequently. Replanning might require nearly continuous coordination across multiple streams. The streams become, in effect, braided. In a very real sense, the problem wasn't actually factorable. Such problems might be better addressed as wholes.
- In this example, factorability is compromised because of disjoint awareness of the work streams. But factorability can be compromised whenever the team's ability to coordinate is inadequate to the needs of the problem. For example, geographical dispersion of the team can reduce its ability to coordinate work across the team's various work sites. Other possible barriers to coordination include language, professional specialty, and in the case of strategic partnership, enterprise loyalty. Any barrier that contributes to disjoint awareness sufficiently to compromise the ability of the teams to adequately coordinate can compromise the factorability of the problem.
Analysis can work well when its risks are well managed. Examining the work streams for boundary clarity, coverage completeness, potential stream-stream interactions, and factorability can help in managing those risks. First in this series Next in this series Top Next Issue
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