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July 18, 2005

Collaboration and Analytics: driving production with intelligence

Most organizational managers may not admit it, but the myriad complications of managing costs lower in order to increase net operational gains mean that this approach hits a point of diminishing returns much sooner than anyone prefers. Thus, in order to increase value, the approach of growth-oriented management becomes an immediately critical alternative to cost-orientation. Collaboration and analytics are two of the strongest tools for leveraging existing resources towards value-generating growth.

How? Collaboration and analytics are two modes of discovering and synthesizing intelligence for driving production.

As such, they are both decision-support mechanisms that can make a critical difference to the effectiveness of operations within the time-span of the operation's execution.

In decision support:
- Collaboration, using coordination, especially integrates varied expertise to create competencies from at-large communities.
- Analytics, using formulae, especially distinguishes latent patterns to create information from data collections.

How do coordinating expertise and formulating patterns contribute expertise and information, at a level particularly critical to production value?

Actually, Collaboration and Analytics each provide competencies and information, to recognize and productively incorporate new and unexpected influences -- in less time.

One view of this involves seeing them work together.

Collaboration can leverage analytics by using analytical findings to support decisions that adjust or guide the integration within collaboration. An example of this would be to have, based on forecasts, certain links in a supply chain modify their individual current outputs and/or output schedules to preserve the timeliness, cost limits, volume or quality of promised deliveries.

Conversely, larger data collections can be developed more quickly via collaboration; so, analytics can leverage collaboration to explore a wider range of alternative data sooner. This, within a given time period, can produce more patterns and/or perform more validation of given patterns. An example of the former benefit is identification of viable product alternatives that attract new business or contingent actions that reduce risks; an example of the latter is determining from multiple perspectives whether an idea really meets a quality standard for stakeholders, such as through a peer-review process in academia or in business approvals for complex proposals.

In practicing collaboration or analytics, a basic problem is one of providing an underlying model for organizing the activity. However, in collaboration there must be a model for putting things together, and in analytics, a model for taking things apart. For that and other reasons, another view on their potential delivery of benefits must consider the two techniques separately.

Collaboration is a special form of co-operation, in which all participants are adhering to the same goal for the same reason. The model that is needed in collaboration will, in effect, offer operational objectives to be mutually respected by all participants. More importantly, the model, usually called a "policy", will prioritize activity options that are offered by the various participants or that develop as interim possibilities during operations. This sounds very much like how any process is regulated, but the difference is that in collaboration the operation is constantly open to new inputs that were not anticipated before the operation started. Collaboration therefore requires the ability to effectively re-organize the operational integration based on the new inputs. Distinctively, while the operation is already in progress, a collaboration invents the solution to reaching the goal.

Analytics is a special form of de-composition, in which a pool of data is tested for its key contents -- namely, what it might include that is pertinent to a given fact or assumption. The model needed for analysis, usually called a "hypothesis", describes cause-effect relationships behind "factual" events or conditions (states). However, in an attempt to discover and confirm significant relationships, the analysis must isolate and identify the key factors of the relationships -- thereafter looking for clues or evidence of the factors. Sometimes it turns out that the "suspected" factors are not the real ones, or that the factors presumably needing to be investigated are not relating as suspected. This detective work is usually mandated as auditing; but the difference is that in analytics (for example, with weather reports), it is more important to discover possible futures than the confirmed past. As a result, although analytics are typically used to find proof of something, the primary basis of value in analytics is actually an objectivity that is focused on credible explanations agnostic to the data.

In those various ways, collaboration and analytics should bolster management-based effectiveness. But what does that look like? It's fair to look at progress from the "performance" perspective that a business typically brings to measuring effects; progress means both sustained improvements in operational quality and timely development of desired business advantages. Since there are many management techniques for pursuing those outcomes already, what motivates an embrace of collaboration and analytics?

An important issue clearly suggested by collaboration's inventiveness and analytics's neutrality is innovation -- or the ability to take new approaches to problems, due to having freedom from prior restraints such as resources or points-of-view.

Yet "continuity" is no less important to support. Here, collaboration and analytics matter when a breakthrough is needed for progress that is not, so far, being successfully driven by the capacity of solo efforts. Flexibility and objectivity (as supplied by collaboration and analytics) are key to getting the needed breakthrough.

The challenge is that managing operations towards a repetitive sameness is a technique we have fully embraced in the name of process improvement. Yet all processes are constantly confronting the eventuality of change at many different constituent levels including their resources, controls (management) and beneficiaries (perceived value). The danger of protecting "as is" processes is that they will become inflexible or irrelevant over time -- in short, counter-productive. The shift in operational habits that collaboration and analytics both encourage is straightforward but potentially profound. It is nothing less than a different approach to effectiveness -- requiring us to:
- stop protecting (in the name of efficiency and predictability) the "as is" form of operation; and instead,
- work to grow the operation's effectiveness by continually shaping it to broader opportunities that are more extensively qualified.

For some managers, this may throw their dedication to "optimization" under unexpected pressure. But in that case, it becomes appropriate to give the idea of optimization a revised context, by working on it specifically to support operational growth. An example of this approach is the attention many managers give to pursuing optimization mainly within the guidance of maturity models that are growth-oriented, explicitly sensitive to how the manageability of change (i.e. agility) regenerates and multiplies capacity over time.

Posted by Malcolm Ryder at July 18, 2005 9:07 AM

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