ERIC ÅPPESLÅND: The Enterprise Ambiguity Manager

Facebook recently launched their new profile layout called "timeline". It replaces a user's wall on their profile page with a stream of information surrounded with context such as date, place, photos, "likes", and a host of other details. Although it was always possible to view a person's history, the previous layout primarily focused on the events of the moment, a snapshot of what is happening "right now". The new layout provides a way for viewers to see the "flow" of information over the course of the person's life with the context needed to understand how the individual evolved to the "right now".

This concept of understanding information flow is important for managing risk at a firm. Deloitte Consulting highlights this concept in their "Shift Index" white paper, "[b]ecause of the rapid change, higher unpredictability and volatility...knowledge flows are a particular key to improving performance'.[1]

Yet before diving into the nuances of how this applies to risk, first it's important to understand how measuring information flow is even possible. "Big Data" has become one of the most talked about aspects of business performance analysis in the past year. As W. Brian Arthur argues in a recent McKinsey Quarterly article, the ability to have so much data comes from the exponentially increasing number of sensors being placed all around the world. Individuals are infinitely connected through multiple devices and indeed the entire planet is being wired to track celestial movements and small insects. These connections converge to create an "...unseen, underground conversation [that] is happening among multiple servers talking to other servers, talking to satellites that are talking to computers." [2] Arthur says this is literally creating a kind of "neural network" for the economy and providing a level of intelligence. He says "I’m not talking about human intelligence or anything that would qualify as conscious intelligence. Biologists tell us that an organism is intelligent if it senses something, changes its internal state, and reacts appropriately." [2]

David Weinberger from the Atlantic then builds on this concept of a system and big data to underscore that with the amount of data being collected it no longer makes sense to understand component parts of a system but to look at the system as a whole. In that same article he says, "[a] new science called systems biology studies the ways in which external stimuli send signals across the cell membrane. Some stimuli provoke relatively simple responses, but others cause cascades of reactions. These signals cannot be understood in isolation from one another... The result of having access to all this data is a new science that is able to study not just 'the characteristics of isolated parts of a cell or organism'...but properties that don't show up at the parts level." [3]

Via Rebecca-Lee on Flickr

Now it's possible to see that there is a network of sensors providing an intelligence to our economic system that produces so much data that the whole can reveal more than the parts. It is tempting to measure system reactions from the perspective of it's current context -- what it's doing "right now". However, changes within a system are never static, they continually evolve and eventually turn into yet another reaction leading to yet another state and each state change is related to the ones preceding it. Just as big data forces us to look at the system instead of a single component part, we should also look at the flow of change over time and not just a component instant. The neural networks mentioned earlier are carrying a vast amount of information and businesses that can harness the "flow" of information will be able to understand state changes in the system of our economy far better than others.

Flow is essential to managing risk because it prevents strategies based only on current state factors that are going to rapidly change. "The next decade or two will be defined more by fluidity than by any new, settled paradigm; if there is a pattern to all this, it is that there is no pattern. The most valuable insight is that we are, in a critical sense, in a time of chaos."[4]

Harnessing flow is so important that a new business function needs to be created, traditional enterprise risk management (ERM) is not enough. As Booz & Co. states, "[m]ost ERM groups focus their attention on the risks that businesses most frequently encounter — such as whether the enterprise is complying with regulations, suitably accounting for its activities, and operating in an ethical and legal manner — rather than on black swans."[5]

This new type of manager must not only understand the traditional components of risk, they must be able to thrive in ambiguity, they must be what I call an Enterprise Ambiguity Manager. The EAM must know how to use information flow to do traditional stress testing of component parts such as the supply chain and the customer portfolio, but also be able to build a model that reveals characteristics of a stressed system and then zoom back out to observe the flow again. Surviving the chaos requires plugging into the neurons to collect big data on a massively complex system and then tuning in to the flow in order to ride the waves of change.