The Complexity Space and the Complex Space of Data

We have come out of the time when obedience, the acceptance of discipline, intelligent courage, and resolution were most important, into that more difficult time when it is a man’s duty to understand his world rather than to simply fight for it.  Ernest Hemingway (1899 – 1961), 1946

Information is ubiquitous. As the ability to access, capture, manage, and process data increases, there is a drive to seek knowledge and solutions in both the mining and analysis of massive data sets.

dataThe marketplace is responding to customer demands by offering increasingly cost effective and sophisticated mining and analytic options for dealing with exploding quantities of data. The result is a rush to focus on the power of data as the new strategic imperative for highlighting critical information, suggesting outcomes, and supporting decision making.  Some of the “data” questions getting the most attention in organizations revolve around how “predictive analytics” can provide leverage, solutions, and competitive advantage.

We love good questions. The following is our perspective on potentially emerging opportunities as “data” in all its known and unknown forms can be  more intentionally viewed as a tool for shifting patterns and options for action.

Data analysis (at least when there is a reasonable degree of confidence around its quality and methodology) is thought to provide a reliable set of considerations for action. There are many cases when traditional computational models provide the most efficient way to extract understanding from “big data” sets. However, there is another way to explore the significance and influence of data in the sustainability and growth of an organization. We believe there are additional advantages gained from considering a more intuitive, “sense-making ”approach to the data.

CSLandscape_no diruptionAn example of how we have engaged with clients around this topic is best illustrated by focusing on one component of the Complexity Space (CS).  The Complexity Space Landscape is derived from the original concept of a fitness landscape or adaptive landscape in evolutionary biology and was later further developed by Ralph Stacey  to represent actions in a complex adaptive system based on the degree of certainty and level of agreement on the issue in question.

The Complexity Space Landscape adds two additional distinctions — the amount of “knowledge” and the existence of “patterns” in the system. These offer a visual way to explain how organizations can approach shifting patterns towards desired outcomes with data analysis.

Let’s consider how data informs all the current and desired states of an organization’s practices and options for action. The three states, “status quo”, “innovation”, and “mutation” represent three distinctions in how we look at pattern existence and development throughout the many parts of the organization.  Although the above diagram shows them in a more “linear” progression, these states exist repeatedly and simultaneously across the system. multi_states

The “status quo” state is a system’s “business as usual” space, where everyone involved knows what to do and what results to expect. Data is plentiful, trusted, and employees know how to interpret it to support these patterns.

The “mutation” state of the landscape is where little agreement and certainty exists about what or how things are happening.  We like to explain this state as one of “mutational opportunity.” It is possible for an organization to value and appreciate this space as one where we “recognize the dynamic of insight, potential and breakthrough” but our intuitions are not sufficient for allocation of resources and organizational alignment. It is here that data analytics can provide significant value by enhancing intuition. Data helps answer the questions, “Are we moving in the right direction?” “Is this worth pursuing?” “Are sustainable patterns emerging?” The answers to these questions aid the system as it moves toward greater agreement and certainty.  Negative answers increase confidence that the idea should be scrapped or further mutated. Positive answers provide motivation and confidence to move into the more structured state of “innovation.”

In the “innovation” state an organization engages in a continuum of experimentation, engagement, and self-organization.  In this state, data analytics provides the answers to additional questions. “Do we have confidence that this change should be become the ‘new normal?’” “Is this change sufficiently robust to deal with the emergence and self-organization that takes place in this particular area of the business?” The responses to these and other questions inform the complex decision making needed to stay in the “innovation” state, move towards the greater certainty of the “status quo,” or “go back to the drawing board” state of mutation.

Establishing a healthy coherence between “actions of the human agents in the system” and “indicators for action provided by the data” have  opened a view “into that more difficult time when it is a man’s duty to understand his world rather than to simply fight for it. “

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