Month: February 2014

McKinsey: Weak Signals in Social Media

The latest McKinsey Quarterly has an article by Estelle Métayer & co on detecting weak signals in social media, and how companies can leverage this information to their advantage.  Given the nature of the publication, a broad overview of the subject and accompanying anecdotes is to be expected – that is fine, she gets into greater depth on her blog.  We are happy to see her in MQ again.

What we found ourselves in violent agreement with was contained in the concluding paragraph:

“Regardless of where companies observe weak signals, the authority to act on them should reside as close to the front lines as possible.”

The extent to which social media adds to complexity is debatable – nevertheless, much of business today occurs in complex environments (see the Cynefin series). In such environments cause and effect can only be perceived in retrospect – the onus of strategy becomes pattern management, and continual probing of the environment is necessary to make any sense of it.  Highly centralized organizations cannot effectively compete in such an environment.


Flexecution as a Paradigm for Replanning, Part 1, by Gary Klein.  IEEE Intelligent Systems; Vol. 22, No. 5, September/October 2007.  (PDF)

This is the first of two essays about planning and execution with ill-defined and conflicting goals. The concept of flexecution—flexible execution—goes beyond simply adapting a plan as needed in order to reach our goals.

Rather, flexecution entails changing the goals themselves based on discoveries made during execution. In pursuing ill-defined goals, we must expect to revise and even replace the goals we initially stated during the planning phase.

One of my objectives in writing these essays is to de- scribe the importance of goal discovery to planners and project managers who might not appreciate the difficulties posed by ill-defined goals. A second objective is to offer suggestions to computer scientists who design mixed-initiative systems about how to better support planning and execution with ill-defined goals.

We’ll begin with the notion of planning to set the stage for comparing planning using clear goals versus planning and execution using emergent goals.

Clear goals vs emergent goals

Flexecution, Part 2: Understanding and Supporting Flexible Execution, by Gary Klein. IEEE Intelligent Systems; Vol. 22, No. 6, November/December 2007.  (PDF)

This is the second of a two-part essay about planning and execution with ill-defined and conflicting goals. These essays aim to describe the insights of researchers in AI and cognitive systems engineering to a wider audience and to broaden efforts in supporting flexible execution with ill-defined goals.

The previous essay introduced the term flexecution to bridge the gap between planning and execution.

I suggested that replanning during execution was the fundamental activity to support and that planning was a means of preparing people to adapt and be resilient. “Execution” denotes the activity of carrying out a plan with perhaps some local modifications; however, people often change their plans and goals on the basis of what they learn during execution. They’re making discoveries along the way by diagnosing the reasons they’re failing or having difficulty. They’re trying to achieve goals while simultaneously redefining them. When the initial goals are ill defined, these discoveries become critical. We need to support the learning process, and not just help people execute the initial plan. Hence, “flexecution” describes the flexible execution needed to cope with ill-defined goals. It’s not a technique but rather a reconceptualization about the planning and execution problems that must be addressed.

The flexecution process

Intro to Cynefin and CI: Part II

Note:  This is the second in a series on the application of the Cynefin decision-making framework to the field of competitive intelligence.  Part I is available here.

We last left off with the introduction to Cynefin. To review, Cynefin is a framework based on complexity science and designed to help decision makers understand where their system stands in the external environment. This context allows not only for better decision-making, but also to avoid problems that arise when a management style is used outside of appropriate context.

As Cynefin’s creator – Dave Snowden – described it in the Harvard Business Review:

“The framework sorts the issues facing leaders into five contexts defined by the nature of the relationship between cause and effect. Four of these – simple, complicated, complex, and chaotic – require leaders to diagnose situations and to act in contextually appropriate ways. The fifth – disorder – applies when it is unclear which of the other four contexts is predominant.”

(For the sake of brevity here I encourage you, dear reader, to explore Cynefin yourself with the suggested resources appended to Part I of the series.)

Credit for the Graphic goes to Goal Systems, Inc.

Credit for the Graphic goes to Goal Systems International.

If we were to match the utility of existing competitive intelligence practice and methodology to the Cynefin domains, we would see that CI is very useful in the ordered domains (simple and complicated), where causation is perceptible. However, in the un-ordered domains (complex and chaotic), where causation is less perceptible – CI would have substantially less utility.

After all, how can you develop KITs and KIQs if you don’t know what you don’t know? Lexis-Nexis does not have a premium subscription for the unknowable.

This lack of utility in the complex and chaotic domains presents a problem for CI.


Because much of the business world over that last 30 years has shifted from complicated systems to complex systems – that is, to the un-ordered side of the spectrum in the Cynefin domains.

As Gokce Sargut and Rita Gunther McGrath wrote in the September 2011 issue of HBR:

complexity has gone from something found mainly in large systems, such as cities, to something that affects almost everything we touch: the products we design, the jobs we do every day, and the organizations we oversee.”

Facilitated by a revolution in information technology, systems that were once separated are now interdependent, and thus by definition – more complex. The result, as Sargut and McGrath write, is that:

“[c]omplex organizations are far more difficult to manage than merely complicated ones. It’s harder to predict what will happen, because complex systems interact in unexpected ways. It’s harder to make sense of things, because the degree of complexity may lie beyond our cognitive limits. And it’s harder to place bets, because the past behavior of a complex system may not predict its future behavior. In a complex system the outlier is often more significant than the average.

Making matters worse, our analytic tools haven’t kept up. Collectively we know a good deal about how to navigate complexity—but that knowledge hasn’t permeated the thinking of most of today’s executives or the business schools that teach tomorrow’s managers.”

Emphasis on “our analytic tools haven’t kept up.


This is not to say that competitive intelligence methods that have come before us should be abandoned; but rather to acknowledge that the methods used in the past had utility, until they were shifted into an inappropriate context. Until CI evolves to be suitable for a complex environment, it will increasingly find itself being used in an inappropriate context.

Managing everything as is if it were simple would be ineffective, and managing everything as if it were complex would be inefficient. Cynefin is not a unified field theory or holy grail of anything – but it does provide a useful framework to put the pieces together in ways that will help to compare, make-sense of, and guide us in the selection and development of CI methodologies.

Coming Up – intelligence and strategy in a complex environment; and the complex adaptive organization.

Intro to Cynefin and CI: Part I

Note: This is the first in a series of posts introducing the Cynefin decision-making framework to the field of competitive intelligence. The Cynefin framework will serve as a thread between subjects this blog intends to explore, including: sense-making, complexity, strategy, and modern intelligence methodologies.

Competitive Intelligence tends to treat all decision making enviroments as the same, regardless of the system it occurs in. This is a mistake because the system or environment the decision making occurs in differs from situation-to-situation and industry-to-industry.

Part of this disconnect is because the CI field was developed on Cold War era intelligence paradigms that were imported, nearly unchanged, from the government and designed for largely static issues and environment. While the US intelligence community broadened its to dynamic topics after the fall of the Berlin Wall, the CI field has not adapted its paradigms to better suit the external environment.

The Cynefin decision-making framework recognizes the causal differences that exist between system types, and proposes new approaches to decision-making in complex environments. Cynefin (Welsh term pronounced Kin-ev-in) is likewise a sense-making model making it conductive to modern intelligence paradigms designed for a dynamic environment.

Into to Cynefin

There are 5 domains or contexts in the Cynefin framework: simple, complicated, complex, chaotic, and disorder and is to be visualized as shown here below:

Cynefin Framework

Cynefin Framework

The first four domains can be described as follows:

Simple – Clear and obvious relationship between cause and effect. Known-knowns. Approach with SenseCategorizeRespond and apply “best practices”.

Complicated – Cause and effect requires analysis, investigation or expert knowledge. Known-unknowns.  Approach with SenseAnalyzeRespond and apply “good practice”.

Complex – Cause and effect can only be perceived in retrospect, not in advance.  Unknown-unknowns.  Approach with ProbeSenseRespond to detect emergence patterns and practices.

Chaotic – No relationship between cause and effect on the systems level. Unknown-unknowable.  Approach with ActSenseRespond to discover novelty, seek to stabilize environment.

The fifth domain is disorder and abuts all the other domains. This is to signify that any given domain can slip easily into disorder, when not knowing what what type of causality exists and people revert back to their entrained thinking for decision making.

It is important to note that Cynefin is not a categorization model – this is not a simple B-school 2×2 matrix.  Problems can overlap boundaries, and the lines between boundaries blur.

Future posts in this series will further explore Cynefin and its applications to CI.

Suggested Resources:

YouTube: The Cynefin Framework by CognitiveEdge

The New Dynamics of Strategy: Sense-making in a Complex-Complicated World, by Cynthia F. Kurtz and David J. Snowden. IBM Systems Journal, Fall 2003.  (PDF)

A leader’s framework for decision making, by David J. Snowden, and Mary E. Boone. Harvard Business Review, November 2007, Volume: 85 Issue: 11.  (PDF)

Systems Thinking and the Cynefin Framework: A Strategic Approach to Managing Complex Systems, by H. William Dettmer.  Goal Systems International, 2011.  (PDF)

Skybox: The world is one big dataset

Dan Berkenstock is the founder of Skybox Imaging, a Silicon Valley based commercial space imaging startup. Skybox plans to build a constellation of of inexpensive minifidge-size satellites orbiting the earth to provide up-to-the-minute imaging of the planet.

The potential of this idea is significant, as Wired wrote last June:

But over the long term, the company’s real payoff won’t be in the images Skybox sells. Instead, it will derive from the massive trove of unsold images that flow through its system every day—images that, when analyzed by computer vision or by low-paid humans, can be transmogrified into extremely useful, desirable, and valuable data. What kinds of data? One sunny afternoon on the company’s roof, I drank beers with the Skybox employees as they kicked around the following hypotheticals:

  • — The number of cars in the parking lot of every Walmart in America.
  • — The number of fuel tankers on the roads of the three fastest-growing economic zones in China.
  • — The size of the slag heaps outside the largest gold mines in southern Africa.
  • — The rate at which the wattage along key stretches of the Ganges River is growing brighter.

Such bits of information are hardly trivial. They are digital gold dust, containing clues about the economic health of countries, industries, and individual businesses. (One company insider confided to me that they have already brainstormed entirely practical ways to estimate major economic indicators for any country, entirely based on satellite data.) The same process will yield even more direct insight into the revenues of a retail chain or a mining company or an electronics company, once you determine which of the trucks leaving their factories are shipping out goods or key components.

Plenty of people would want real-time access to that data—investors, environmentalists, activists, journalists—and no one currently has it, with the exception of certain nodes of the US government. Given that, the notion that Skybox could become a Google-scale business—or, as one guy on the roof that afternoon suggested to me, an insanely profitable hedge fund—is not at all far-fetched. All they need to do is put enough satellites into orbit, then get the image streams back to Earth and analyze them. Which is exactly what Skybox is planning to do.

Likewise the implications for competitive intelligence are not insignificant, as this has the potential of unleashing data sets previously unavailable. In the past the business world has utilized satellite imaging for intelligence purposes, but its usage has always been constrained by cost and image latency issues. That is about to change.

Skybox released video from its first satellite in orbit at the end of last year:

Note – Skybox is not the only player – or even startup – in this field; they have however gotten the most attention and backing.

About this blog

The field of competitive intelligence is dying; it is dying because it failed to adapt to the 21st century. Worse yet, is that some still fail to see the irony of this.

This blog is an open notebook dedicated to bringing competitive intelligence into the 21st century.

Welcome to Rethinking CI.