Complexity in Organizations: Finding Patterns that Work

As our series on org culture continues, its time to raise the bar in our thinking.

Imagine an overlay of the many cultural dimensions of Edgar Schein onto the four primary cultural forces of Charles Handy. The plot thickens: these are conditions present in virtually all organizations. Large orgs have many, diverse subcultures, making cause and effect of broader organization behaviors elusive. The many variables drive an unpredictable dynamic. Traditional OD interventions often take on issues or interventions one by one, in an effort to simplify. But this simply leaves prevailing forces intact.

It’s a complex but common situation, and for most, it’s hard to imagine how to deal with it. As we said in our framing post, that’s why org and culture change efforts often struggle or fail.

The key focus: complexity, and how it impacts culture change in large organizations.

A modern, more holistic perspective for grappling with complexity in organizations comes from the Human Systems Dynamics Insititute (HSDI). Glenda Eoyang w/ Ed Olson, in “Facilitating Organization Change: Lessons from Complexity Science” (2001) introduce some new thinking. Let’s take look at how their model creates focus on specific group interactions amid a sea of variables:

    What, Where and When (defining: the “containers”). Must focus on the problem scope or domain that bring a group of people together at a point in time. Many contexts are possible and potentially meaningful, but to achieve a result, one must be picked for focus, to produce a tangible result. More simply, if its a “box within a box” world, which box are we working on right now?
    Who (defining: the “differences”). Ideally, members of a group will be diverse in their thinking. This brings strong creative energy via opposing viewpoints. Each member can be a catalyst. Bringing members in contact helps them to see alternatives and challenge the status quo. This is essential, and often impossible in Handy’s ‘role/silo’ culture.
    How (defining: the “exchanges”). Ensuring an efficient means for interaction is key, including face to face conversation and electronic connections. How and why will people in this problem space connect? What is the currency of their interaction? Again, difficult in a ‘role/silo’ world.

I see these as the critical building blocks for framing (and ultimately, teaching) collaborative behaviors. And from this conceptual framework, some useful and practical insights have already emerged.

To advance these ideas, let’s tap the perspectives of additional HSDI thought leaders Royce Halladay, Christine Quade, and Mary Nations:

    Patterns are outcomes that result from adaptive group collaboration. It is important to reinforce (and thus, reproduce) the positive patterns, and stop the negative ones.
    Simple Rules are the basis for guiding behavior, which can be done by selecting valuable patterns and reinforcing them (eg. corporate “guiding principles”). These must be actions, starting with a verb.
    Generative Engagement (aka “productive outcomes”) may be the holy grail in this thought process for OD. It is the way to tap value from the theory, as teams model desired behaviors from the organization, and adopt simple rules.

Over time, the theories go, the organization adopts the best, most valuable behaviors, learning to follow useful patterns. There is more engagement. Good things happen. The organization learns, adapts, and becomes more effective.

If you’re an OD professional, this should be resonating a bit.

As a simple example, cross-functional problem-solving teams can accomplish much using this model. But an even more specific example is a special case: online social communities like #smchat and #ecosys. The diverse thinking of such a group tends to challenge established norms. There is no pre-existing structure to unwind. Innovation can commence as soon as the simple rules are established. The group creates its own situational context, and develops its own specialized, often highly productive method for exchange of ideas.

In complexity terms, we call this “self-organizing”. It is a powerful way for groups to spawn new, emergent results.

That’s a fancy name for innovation.

Seeking to Understand Optimal Conditions. Creating optimized, cross-functional discovery teams is a great way to demonstrate and model effective collaboration. They function best in Handy’s “task/network” model. They are designed to adapt, as members learn from interacting with others, tapping their collective base of experience. And they quickly grow adept at pursuing only patterns that produce desired results. Quite literally, they learn. Without such dynamics, the hardened status quo of the “role/silo” culture prevails, restricting exchange, and providing insufficient diversity of thinking to move beyond the status quo. Thus in traditional structured, top-down groups, innovation can easily be shut down. In groups that understand and build energy from complexity science and the HSDI framework, innovation can flourish.

The “Learning Organization” is a future state imagined by Peter Senge in Fifth Discipline (1990). His systems thinking concepts assumed more structure, but his vision of what is possible is congruent with what I’ve outlined here. We are working in the same direction.

Where Senge left open the “how”, Eoyang and others at HSDI are applying complexity science to get there.

The rest is up to us. When we’ve connected the dots on the core elements of organization and culture change (as we’re doing here) we can move more swiftly to pilot implementations.

Next, I’ll post on implications of Culture Change in Government and will update Kotter’s “8 interventions” to account for 21st century forces of increased complexity.

Meantime, your insights are, as always, greatly appreciated.

The Trouble w/ Silos: Lessons from Charles Handy

CHARLOTTE, NC. March, 2010, by

We’re continuing to unpack the forces of culture in organizations.

So far, we’ve framed the many challenges, and looked to Edgar Schein to help us understand the interplay among org culture’s multiple, overlapping dimensions.

Now let’s tap the insights of Charles Handy (Understanding Organizations, 4th ed. 1993) who defined four cultural structures that are alive and well today.

I’ve summarized key implications here:

Lot’s to absorb, but Handy is telling an important story. First, let’s update the semantics from Handy’s 4 models, with some 21st century terms.

  • Power (command)
  • Role (control, silo, bureaucracy)
  • Task (network, matrix)
  • Person (individual contributor, talent pool)

To me, its clear: Handy’s structural forces are as deeply ingrained in modern organizations as ever. But what else can we learn from these structural tendencies?

The Trouble with Silos. Humans are a resilient sort, even (especially?) in an organizational context. Perhaps it’s because orgs evolve toward the stability of role-based (or really, standards-based) hierarchy, in a drive to perpetuate past success. We tend to repeat what we know how to do. That sets the stage for trouble, but there’s more to it. The other 3 models, while valuable, also face a limiting factor of organization size. They just don’t work well in large organizations. That has a sobering implication:

Handy’s Role-focused (silo) model is the only one that scales, making it the de facto “end state” culture as our social and commercial infrastructures expand. The result is calcification. We see this in academia, public education, and large-scale commercial enterprises in virtually every industry.

In short, with scale, our silos are hardening. We need to find a way to break this cycle.

Taking On Complexity. In coming posts, I’ll argue that 21st century complexity demands more dynamic organizational frameworks, embracing objectives like discovery and learning. It’s not the first time we’ve posed such ideas. But with the help of Schein, Handy and others, I hope to push toward new, actionable insights.

We’ll want to talk further about the synthesis of models. Most OD veterans (including Schein and Handy) would assert that blending of models is essential.

But too often the organizational practice is “one size fits all”.

Next Up. In my next post, we’ll look at adaptation and simple rules: how patterns emerge within orgs (aka human systems), and how we might use them to harness complexity. This will let us bring 21st century perspectives to Handy’s original analysis.

It’s a complex world. Our organizations and cultures need to be redesigned to contend with it.

Stay tuned.

Problem Solving for Ecosystems with “EcoDNA”

If you’re following ECOSYS, you’ll know we’re moving quickly past the high-level overview stage and on to process details. Our goal: to prove that virtual collaboration can drive social innovation.

We ran our second #ecosys chat last night on Twitter; our transcripts are posted on NING .

To illustrate our approach, let me show you what virtual collaboration looks like: Jay and I (members of the ECOSYS core team) conferenced Friday to brainstorm how to convey “looping” (iteration) and successive stages in our draft 6-step collaboration process. Using SKYPE and YUGMA we did some virtual white boarding as we talked, and came up with the diagram below. To vet our thinking, the ECOSYS core team reviewed the details last night in an online chat, provided feedback, unanimously embraced the new visual, and coined the name “EcoDNA.”

While a small and early win, it is an important one: both the visual and the EcoDNA name convey our trajectory:

To achieve social innovation, we must first understand the building blocks of our social ecosystems; only then can we rearrange them in an optimal way. Consistent with the DNA metaphor, understanding and relating the building blocks of “how things work” is a fundamental first step to learning and to innovation.

Sure, EcoDNA is just a process model. For practical purposes, we’re still at the starting line, revving our engines. But it’s an example of what virtual collaboration can achieve. And for the complexity buffs, EcoDNA demonstrates our fist bit of “emergent” innovation.

ECOSYS Iterative Problem-Solving Model

ECOSYS Iterative Problem-Solving Model

discovery thread (detail)

discovery thread (detail)

implementation thread (detail)

implementation thread (detail)

[The blow-up of component parts on this diagram should help link it back the process “building blocks” in our prior posts.]

We have more housekeeping to do before moving on to addressing the big issues in healthcare and education. We’re hoping to keep most of our brainstorming sessions online, reaching a broad stakeholder base while documenting inputs in real time. We’re asking core team members to submit guest posts of their takeaways here, so we can establish a consensus (coming from diverse perspectives) of what we’re learning.

Please stop back often. We’ll have much to report. You can follow or search Twitter hashtag #ecosys for daily insights. And check out TweepML to meet and network with the core team.

Thanks for your interest. We’re excited about the potential, more so with every step.

Framework for Ecosystem Change (2): Evolution

Below I introduce a framework for Ecosystem Evolution, a collaboration-based process to achieve innovation in our social ecosystems, which includes complex spaces like Healthcare and Public Education.

Our thought process has been evolving since August 2009, and can be tracked in this stream.

This problem-solving approach is intended to be comprehensive in its objectives and capabilities, yet straightforward in its design. It is made possible by incorporating insights from complexity science, as well as the rapid evolution of the social media platform, which allows cross-disciplinary subject matter experts (“SME”s) to work together in an efficient, virtual manner.

Paradigms: the Way Things Work

At the core of this framework is a realization that there is a current way of doing things, and multiple, new, innovative ways of doing things better.

Using paradigms to frame and analyze developing ideas is important, especially in early stages, when the alternative solutions are still formative [1]. It provides an intuitive frame of reference for discussing ecosystems: boundaries, rules, behaviors, and outcomes, all important elements that describe the complex systems we will be tackling. This “way of doing things” (both current and improved) is often the source of significant debate. Semantic challenges abound. Traditionally, problem/solution scenarios are written down in many ways, ranging from pure text (popular in legislation) and napkin drawings all the way to complex diagrams and flow charts, using a multitude of formats and tools. We will need to keep the process focused on ideas and content, not tools.

Due to the complexities of our social ecosystems, the nature of changes involved must go far beyond any notion of incremental adjustments. Contemplating the “game changing” notion of a paradigm shift precedes any fundamental, structural changes in our current paradigms [2]. To innovate, we’ll need to challenge conventional wisdom in each domain, or subject area. This approach will help us achieve that.

Let’s take a look at my proposed Ecosystem Evolution model, which provides a collaborative overlay to the Current State view that I originated in my last blog post.

Ecosystem Framework pt 2

Ecosystem Framework pt 2

The over-arching characteristics of this new model are:

– All stakeholders will have opportunity for input
– Social media plays a critical role as “open collaboration forum” for idea exchange
– Invested producers with a financial stake will have more limited roles
– Consumers (most impacted by ecosystem outcomes) will have a voice in articulating outcomes
– Consumers will get final validation (via “rating”) of proposed solutions
– Several open-loop cycles ensure iterative improvements toward final innovation
– Multiple iterations or “feedback cycles” ensure consensus

There are a couple key points to take away from this.

(1) Actionable Scope (need to be realistic). A framework like this is a representation of a complex set of relationships, interactions, intermediate steps, and deliverables. The simplicity of the model should by no means imply trivial efforts or shallow treatment of the topics. Rather, considerable work is implied. This model creates the process backbone for a series of connected collaboration teams. Further details on “how” will be forthcoming.

(2) Adaptable, Scalable and Efficient. This approach creates the means by which the rigorous and appropriate discussions might evolve uninterrupted, through a “hub and spoke” model of work group replication. In other words, any number of problem-solving teams may be spun off from the core problem team within the ecosystem, to work on sub-issues, and report back. This makes the Ecosystem Evolution process adaptable, scalable, and via multi-tasking, quite efficient. Given the complexity of our ecosystem issues, this is perhaps the ONLY way problem solving could be meaningfully performed.

(3) Focus and Rigor. We will begin to ask the right questions, and record all viable answers.

(4) Meaningful Social Innovation (“disruptive”, and otherwise). Using this model, we can embark on a journey of discovery and social change that has heretofore been unsuccessful. It will be powered by people, connected using social media, supported (with further discussions) by both government and industry, and ultimately, embraced by all stakeholders. Clayton Christensen has made strong and insightful statements about the need for “disruptive innovation” to achieve change from outside ecosystem walls, and the many mechanisms required [3]. I think his vision is the right one, and this Framework intends to achieve it. However, with participation from producers and consumers alike, the degree of “disruption” can be minimized, and simply acknowledged as a working objective. After all, we won’t score a “win” if we create economic chaos. I believe the collaborative approach is the disruptive innovation that has been needed. The approach itself is an innovation in collaborative techniques imagined by Don Tapscott, but not (as yet) fully implemented [4].

(5) Who benefits? First and foremost, it will be the consumer, as this approach is designed to achieve their objectives. But in the end, all stakeholders will win, because we will have created a viable, optimal, balanced approach for delivering services.

This is clearly ambitious. Why am I so optimistic?

Because there are lots of smart people out there. We simply need to engage them to start solving the tough problems.

It’s time for our second test (and this is a non-rhetorical question): Can we make this work?

Notes:
[1] Kuhn, Thomas, Structure of Scientific Revolutions. (1992).
[2] Meadows, Donella. Leverage Points (web, 2008).
[3] Christensen, Clayton. Disrupting Class (2008): McGraw-Hill, Ch.8, pp. 179-196.
[4] Tapscott, Don. Wikinomics (2006): Penguin, Ch.6, pp. 151-182.

Framework for Ecosystem Change (1): Current State

In my last post, I began to outline a new approach for innovation in complex ecosystems. Efforts to drive reform in Healthcare, Education, and Energy have routinely struggled, and progress has been elusive. My thought process was sparked, in part, by an analysis of complexity science written by Beth Noveck & David Johnson. But much of my energy was fueled by numerous examples where barriers to collaboration and silo-thinking have long served to stifle innovation in large-scale institutions and the ecosystems they serve.

The Challenge of Social Ecosystems

Though a great many provider professionals have, in practice, devoted entire careers to excellence, overall system outcomes can appear inconsistent and, in many cases, undesirable.

Why? As noted by Noveck and Johnson, system complexity itself introduces many dynamics that need to be investigated, among them, conflicting objectives of stakeholder “agents”. Another area for focus is money. While always a powerful motivator, in social ecosystems it serves as a double-edge sword. The same financial capital that’s driven breakthrough innovations can also motivate counter-productive results. To stakeholders in the pipeline, long-term outcomes are not always visible, actionable or prioritized effectively.

A Path Forward

To achieve an efficient system-level problem-solving process, I’ve developed a simple framework for Ecosystem Evolution.

First, let’s introduce Part 1 of this framework for the Current State, to ground our discussion and better define some key concepts like “ecosystems”, their “agents”, and their operating “paradigms”. The status quo is characterized by the following forces:

  • Closed-loop, mature transactions and processes
  • Heavy control exercised by producer and government stakeholders (“agents”)
  • Much investment (financial, emotional) associated with the status quo
  • Insufficient rigor in the definition of problems and possible solutions
  • Insufficient data to effectively prove viability of alternatives
  • Largely untapped sources of insight on complex (adaptive) system behavior

Ecosystem Framework Pt1 (Current State)

How would we move forward with this model?

For each ecosystem targeted, we’d document the current state paradigms (literally, “how things work”, represented above by the black box), creating light-weight process models that demonstrate a solid understanding of core challenges. We’d also break down the paradigms themselves into easily understandable components.

Rigor in developing models is critical. Stating problems fully and accurately is on the critical path to any meaningful change.

Then would come the work of articulating alternative paradigms using the above as a baseline, using a collaborative approach that leverages social media. Resulting ecosystem designs could give us (perhaps, for the first time) a detailed understanding of our fundamental, root cause problems, summarizing the changes that may be necessary to address them.

Next Steps

I’ll introduce Part 2, a collaborative solution framework for Ecosystem Evolution in my next post, building on the Current State model above. It will incorporate new, collaborative open-loop processes and the social media aspect. Comments and inputs are not only welcome, they are critical. We can only be successful if we tackle these problems with a mutual understanding and a resolve to work the issues to completion.

Our first test: looking at the model above, can we start to see the challenges more clearly?

Unraveling Complexity (the Missing Link): A new approach for solving problems in Social Ecosystems

For months I’ve been reaching out to colleagues to explore barriers to collaboration, a key tool in the social innovator’s toolbox. Among those queried (and in spite of diverse backgrounds), virtually all had experienced significant barriers to collaboration over the years including silo-thinking, dated and inefficient problem solving models, cultures of control, and a strong, prevailing lack of trust.

Consensus? The barriers to innovation seem to be as universal as they are frustrating.

So something is broken. What is the root cause?

Beth Noveck and David Johnson have published important research on how new Social Media collaboration technologies can change the game. Their perspective on a New Science of Complexity is summarized in this People & Place blog post and explained further in an excerpt from their research. Their focus was the U.S. EPA (including the Federal process for environmental research and legislation) but their conclusion, which I agree with strongly, is that the principles are applicable in business (#e20) and broader social venues (#gov20) as well.

My primary takeaway?  I now believe that INNOVATION IN COMPLEX ECOSYSTEMS will depend on an improved collaboration process – a new middle ground for problem solving – that balances large-scale central organizational approach with grass-roots contributions by individuals. It is about finding the “sweet spot” between rigid structure and adaptive, organic sourcing of ideas. In a new and somewhat uncharted public collaboration space, it means that the forces of organizational scale and leverage can be networked – connected – with discrete centers (or hubs) for contribution to produce more rigorous solutions.

At the core of this thinking? A realization that traditional large-scale organizations (with their central thinking, hierarchical layers, and silos of functional experts) are generally ineffective when dealing with complex situations. Quite literally, they are too rigid. Without the ability to adapt to new variables or to coordinate across silos, grid-lock ensues. And complex social ecosystems are impacted, since “sending in experts” is how we tend to attack these issues. On the list? The well known structural challenges in energy, sustainable food and water sources, public education and healthcare.

What’s needed is an outright paradigm shift in problem solving models that are fundamentally more interactive and cross-functional. And focusing on complexity theory is key, because it begins to unlock some new doors. For one, there must be an organic aspect that allows solution teams to learn, self-correct and grow. And to meet the requirement of connecting people more dynamically, Social Media is the ideal technology. Some examples? Think about experts engaged in live chat. Acceleration of thought synergies. Tools to merge and re-mix knowledge. Ability to leverage and extend dynamic repositories.

With focus and coordination, we can work to find the elusive “sweet spot”.

In terms of naming and framing the problem, the above research makes significant strides. The next step is critical as well, and is just as exciting: in pockets across the internet, the new collaboration is already starting to appear.

Are you seeing it too? Let’s talk, I’ll show you where and how.

Organization Change: the ‘Organic’ POV

In times of dramatic change and crisis, executives (perhaps in line with human nature) revert to known formulas .. tapping structured, controlled and seemingly “safe “solutions. In Stephen Billing’s blog “Organizational Change is Not a Relay Race” he warns against relying on formal & rigid decision processes in times of crisis. Most dangerous: hand-offs between executives, consultants and HR .. passing the baton of responsibility from runner to runner.

We’ve all seen this happen. If you’re leading an organization in crisis, it is no time for hand-offs. It introduces delay, dilutes both message content and ‘signal strength’ .. and in the end, serves to diminish trust. But to really get at the core of the disconnect, we need to understand how change works.

I frame the issue as two contrasting views: the organization as machine vs. the organization as living organism.

In the latter view, change brings in an organic element. Transformations take place in every cell.  Granted, there are communication and control processes present in the organism too; each cell plays a vital role, as in a machine.  But in a living organism, just as within an organization, success (survival and adaptation) depend on symbiotic adjustments from every minute part of the system.

No doubt the industrial age has influenced the thinking of executive management.  But we must now choose our relational paradigms more carefully, especially at times of crisis when time is short and emotions are running high.

Machines excel at repetition. Living organisms excel at change.

Take a good look, there’s plenty of change and crisis to go around.  What kind of organization do you need to be?