From the time of the ancient Greeks, knowledge has been thought about and studied by economists, businessmen, politicians, sociologists and philosophers trying to examine how knowledge drives growth and the creation of wealth and prosperity. Even as early as 1597, Francis Bacon declared ‘knowledge is power’ and acknowledged the central role of knowledge in wealth creation. There have been thousands of books, papers and articles written about knowledge and its meaning, creation, communication, management and use.

What is of interest to the innovator is not to reprise and review this wealth of knowledge about knowledge, but to identify those aspects that are pertinent to the innovation process. This effort can be informed by all of the research into knowledge that has gone before, but it must explore the field from a slightly different perspective.

If innovation is about creating something new that people will want (and value), then the innovation process is a process of knowledge discovery (i.e. learning) and synthesis where the knowledge that is important is not about the past or the present, it is the knowledge about the future or, more accurately, the plausible futures and what can make them come to pass.

In the innovation knowledge process, potential future scenarios are determined by the actions of people, either individually or in organizations (groups of people) having various forms and behaviors. It is these ‘agents’ that determine the adoption of any new thing. Their adoption behaviors are governed by the ways people think and choose, both individually and collectively. It is therefore knowledge of people, or more accurately knowledge of how people will act, that is important to innovation. If one accepts the proposition that innovation is about knowledge of people that results in insights into plausible futures, then it is incumbent upon any theory, model or process of innovation to describe how this knowledge can be obtained and used.

The problem facing everyone in the innovation arena today is that human systems are what are called ‘complex adaptive systems’ or CAS. They are characterized by their non-linear and emergent behaviors over time. As John Holland, one of the preeminent researchers in the field of complex systems has stated ‘To attempt to study cas with [the commonly used linear tools of math] is much like trying to play chess by collecting statistics on the way pieces move in the game.’ This is true of the many methods for ‘knowing’ the future, including trend analysis, expert panels, roadmapping, scenario building, etc. along with market research itself and the newest Voice of the Customer and ethnographic study methods. All of these have as their basis the projection of past and present trends and patterns into some arbitrary future based on either formal or informal linear extrapolations. They do not accommodate the non-linear, emergent properties of complex human systems.

Consider the cases of Google, eBay, the AppleiPod and the Internet itself. Analyzing prior trends makes predicting the inflection points and rate of growth of the curve impossible to predict. It is impossible to apply methods such as market research, futuring, or prediction analystis to complex systems such as future markets. Yet this
is exactly what the innovator, entrepreneur or businessperson wishes to discern – how will a new product be adopted?

An alternative method to answering such questions is to derive insight into plausible futures not from analysis of past trends, but instead from the inner workings of the agents who will create the future – the customers and companies who determine the success or failure of a product, service, business model or process. Knowledge of these agents can lead to insights that can determine whether a new product becomes a true innovation. The critical question for innovators then becomes, how can you gain knowledge of plausible futures that is more than just the projection of past and present trends and patterns or rank speculation?

Knowledge

In order to construct a meaningful knowledge framework, one that is useful in an innovation process, it is necessary to develop a definition and taxonomy of knowledge. This has been done many times by many different people so the effort here is not to create a new definition but to synthesize previous studies and insights into a cohesive and useful structure.

A good starting point is with the Knowledge Pyramid shown in Figure 1.

This pyramid was first hinted at by T.S. Elliot in his 1934 poem “The Rock”

Where is the Life we have lost in living?
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?

These concepts were adapted and extended by Harlan Cleveland in 19821, Milan Zeleny in 19872, and Russell Ackoff in 19893. During these years of conceptual development, the meanings of these terms, while not always agreed upon, tended to converge to the following which are useful for the purposes of the innovation process.

• Data – Measurements & facts. Observations from people & equipment
• Information – Descriptions & patterns. Organizing and assessing into who, what, where, when, how many.
• Knowledge – Instructions & actions. Analyzing and interpreting classes, categories, relationships, that can create future outcomes
• Understanding – Reasons & explanations. How & why – the causes create effects and allow prediction & consequences of actions
• Wisdom – Meaning & insight. What should be done. Why actions are important

Using this understanding of what is meant by data, information, knowledge and understanding (leaving wisdom out for now) it is possible to define a taxonomy and terminology of knowledge that will be useful to the innovator. This is not intended to be a comprehensive epistemological study or even to be compatible with other knowledge taxonomies, nor is it the definitive last word on knowledge. It is intended to be a practical and useful foundation for developing innovation systems and processes and to develop a knowledge framework that informs the innovator.

A useful way to begin is to organize how we view knowledge into nouns (knowledge of), verbs (knowledge by) and adjectives (knowledge attributes).

The Nouns - Knowledge Of

Knowledge Of refers to what the knowledge is about. Three realms and three subjects define the knowledge space and the focus of all knowledge creation activities.

Knowledge Realms – The domains of knowledge that underlie complex human systems.

• People – individuals, future adopters, …
• Organizations – communities, companies, …
• Ecosystems – partners, competitors, …

Knowledge Subjects – What the knowledge is about

• Material – atoms
• Information – bits
• Behavior – human agency

The Verbs - Knowledge By

The knowledge verbs define the sources of knowledge creation and mechanisms to handle existing knowledge.

Knowledge Creation – How new knowledge is created.

• Engagement/Discovery
• Synthesis/Simulation
• Insight/Decision

Knowledge Operations – What happens to the knowledge

• Capture – Search & find
• Discover – Engage & elicit
• Create – Think and develop
• Manage – Store & organize
• Transform – Analyze & synthesize

The Adjectives – Knowledge Attributes

The knowledge attributes define the form and function of the knowledge and how it is used. These attributes allow one to assess the ‘quality’ of the knowledge that exists.

Knowledge Maturity – The reliability, stability and accuracy of the knowledge

• Nascent – preliminary notions & indications
• Expressed – organized & verified
• Adaptive – durable & predictive

Knowledge Structural Dimensions

• Tacit – Explicit
• Unstructured – Structured
• Hidden – Visible
• Episodic (anecdotal) – Semantic (meaning)
• Exceptional – Normative
• Subjective (opinion) – Objective (science)

Knowledge Functional Dimensions

• Irrelevant – Relevant
• Sketchy – Reliable
• Volatile – Stable
• Declarative (static) – Procedural (dynamic)
• Descriptive – Functional

Knowledge Quality

According to this taxonomy, it is clear that the best knowledge is knowledge that is mature (e.g. adaptive) and exhibits the attributes listed on the right hand side of the structural and functional knowledge dimensions (e.g. explicit, structured, visible etc.). Knowledge of this sort leads to insight and understanding of plausible futures. This high quality knowledge can be called justifiably viable knowledge. It is justified because it has been tested and it is viable because it has been transformed into a form that is useful for creating understanding and insight into future behaviors over time. The difficulty is that virtually all the initial knowledge discovered, especially knowledge of the future actions and behaviors of people, has the attributes listed on the left hand side of these attribute dimensions (e.g. tacit, unstructured, hidden etc.). It is through the use of specific innovation knowledge tools that knowledge gets transformed into justifiably verified knowledge.

True vs. False Knowledge

There exists an alternative definition of knowledge as ‘justified true belief’. The qualifier ‘true’ in this definition creates problems when considering how humans use and act on their knowledge. For humans, knowledge can often be false or uncertain and people act on false and uncertain knowledge all the time.

In any knowledge discovery endeavor, a significant risk is ‘knowing’ what is not true. As Mark Twain famously stated, “It’s not what you know that will get you in trouble, it’s what you know that just ain’t so.” With technology knowledge – knowledge of material and information – the mechanisms of separating true knowledge from false knowledge are well established using the mechanisms and the disciplines of science and engineering.

When it comes to knowledge of people and organizational actions, false knowledge is more the rule than the exception. The domain is dominated by linear analysis, opinion, gut-feel, serendipitous and time-based ‘expertise.’ The tools and methods tend to be ‘soft’ because they are based on qualitative observation, psychological and behavioral approaches such as demographics and ethnography. The tools and methods that are necessary are those that can reliably transform this soft and fuzzy knowledge into knowledge that meets the quality criteria described above.

Ironically, in many situations, what decision and policy makers tend to listen to most are belief (what the Greeks called Pistis) and conjecture (Eikasia), especially when they are delivered by ‘experts.’ A Knowledge Framework It is no secret that we now live in a knowledge economy. Literally thousands of books and scientific papers have been written on this topic. Knowledge has been placed at the center of wealth creation and been acknowledged as the prime engine of growth. What interests the innovator is the acquisition and transformation of knowledge itself and how this relates to innovation. It is through the lens of knowledge that a comprehensive theory and framework of innovation can be constructed. By creating a theory of innovation, a framework or a model that puts all of the various processes, methods, models and approaches to innovation into context allows one to see where they fit and what they do.

An innovation framework based on knowledge encompasses both the processes of knowledge acquisition and discovery. Included are the types of knowledge acquired and the ways this knowledge is used to gain insight and make decisions. All valid innovation processes, methods, and tools can be viewed through the lens of knowledge acquisition and use and, from this common foundation, can be compared, contrasted, related and organized.

A knowledge framework allows one to organize and measure the types of knowledge discovery activities necessary for the innovation process. The framework presented here uses the taxonomy and definitions described in the preceding section to organize and direct knowledge activities.

The framework is shown in the following figure. This framework positions each knowledge element and activity in a structure that defines a) the realm and type of knowledge being sought and b) its maturity level. The means of knowledge discovery and maturation are not important for understanding the framework, only the types and state of the knowledge being sought and that exist inside the innovation process.


Starting with the rows in the diagram, one of the most compelling patterns, repeated over and over again in virtually every innovation oriented thought or activity, is that there are three realms (descirbed below) within which innovation can (and some would argue must) take place. These three realms are the sources of innovation. Each realm contributes to a domain of knowledge and knowledge discovery that is critical to innovation:

• Business Ecosystem – Knowledge of how the new innovation will ‘change the game’ in the way partners, competitors, regulators and other pertinent organizations will adapt over time. This is often where business model innovations emerge.
• Mind of the Customer – Knowledge of how individuals or, more accurately, types of individuals will respond to the new innovation and influence each other over time.Knowledge discovery focuses on the unmet needs and desires of current and future customers that are the source of product and service innovations.
• Organizational Capabilities – How the organization that is creating and launching the innovation will adapt and change over time. Understanding knowledge, capabilities, and intentions that drive growth and the source of operational innovations.

Each of these realms represents a complex adaptive system with non-linear and emergent behavior. The knowledge within these three realms starts with specific data and ends up with a dynamic ‘behavior over time’ of the pertinent and relevant aspects. It is this behavior over time that is the ultimate determinant of insight and understanding. The columns of this framework represent the transformation of knowledge from the initial data gathered as the result of knowledge discovery efforts into the means of understanding plausible futures derived from likely behaviors over time and the causes of these dynamic system behaviors.

This knowledge framework is useful because it can be used to direct knowledge discovery activities and develop methods and tools directed at the specific types of knowledge that are critical to the innovator. This type of framework is much more useful that a business plan template or a market analysis spreadsheet because it can point to where knowledge exists and doesn’t exist and what state it is in. In the ideal circumstance, having a justifiably viable simulation or scenario for the ecosystem, the customer, and the organization will provide the understanding and insight needed to make good decisions with respect to the innovation of interest. Knowledge Yield The concept of knowledge yield is valuable because it gets at the fundamental essence of what an innovation process needs to do – create justifiably viable knowledge about plausible futures. The concept of yield also provides a mechanism for developing innovation metrics and measurements.

A yield is defined as the quality (i.e. non-defective) working output for a given input in a set time period. For knowledge processes, the working output is knowledge that is justifiably viable, that is accurate, useable, useful and predictive. Based on this definition, the following figure shows the estimated yields at each stage of the knowledge transformation process.


What is clear is that our ability to gather data, organize, store, analyze it to classify, and discover patterns is top notch. When it comes to turning this information into instructions and actions that have predictable outcomes, the yield is much poorer and we have fewer tools to make this transformation. At the ultimate end of the knowledge transformation process, where causal mechanisms are understood and we have insight into the how and why and can accurately explain what will happen, the knowledge yields are extremely poor and tools are virtually non-existent. We, as humans, have characteristics that work against increasing knowledge yields. Among other behaviors, we tend to rely too much on anecdotal evidence, mistake correlation for causality, rely on our imperfect memories, and oversimplify complexity. As we move up the levels from data to understanding, we use human experts, mentors, and teachers who guide us through experience and practice, but there are precious few mechanisms for making this human experience explicit in new tools. In today’s world of exponentially increasing amounts of data and information, increasing knowledge yield will depend on the development of new tools that handle the volume and the complexity of information and that overcome the reliance on the human brain to transform it into something useful.

Models – Synthesized Knowledge

The innovation knowledge framework and discussions of knowledge taxonomies, yield, and quality are all based on the concept of ‘behavior over time’ or BoT. This is the gold standard for creating understanding and insight because it is only by seeing these dynamic behaviors that one can think reliably about plausible futures and make meaningful decisions.

For complex adaptive systems such as business ecosystems, organizations, and customer communities, behavior over time can only be understood by using a model. A model is a description of cause and effect and can be as simple as a single statement – “If we advertise more, sales will go up” – to a large scale computer simulation of thousands of interacting agents or entities.

The human brain is actually a modeling machine, using internal models all the time to deduce what is going to happen. The problem for the innovator is that the human brain is a limited and idiosyncratic modeling machine at best, suffering from severe capacity limitations and illogical behavior. If the innovator is ever going to be able to gain insight into plausible futures, then new models, and the tools to create them, need to be developed.

The models we are interested in are therefore those that manifest the justifiably viable knowledge we have gathered. The model is the knowledge that has been synthesized and transformed into an integrated and interlinked system that represents what we have discovered about how the world will work with respect to our innovation of interest. The best and most useful models are those that capture the dynamic nature of the complex systems we are interested in and that can be run to create simulations of different scenarios.

The modeling approach is being taken by more and more people. Since it is knowledge of human systems – people, organizations, and ecosystems – that matter in innovation, then, if we are to determine plausible futures, we need a model of individuals and organizations that can be used to simulate possible actions and behaviors. This model must meet several criteria:

• It must be sufficiently abstract as to be usable
• It must be sufficiently detailed as to be interesting
• It must be flexible enough to represent the many different types of agents & entities
• It must exhibit known cognitive behaviors and be consistent with what is known about how people think and behave
• It must be able to be used in practical applications to give meaningful results and insights that are actionable

The knowledge of the individual and human systems that is discovered and created is transformed into a working model that is ‘run’ to create simulated scenarios of the future. To the extent that models can be run many times with different inputs and parameters, many different plausible futures can be explored resulting in greater insight into what can affect these futures. The model thus becomes the ultimate representation of knowledge. Whether it is contained inside someone’s head, or it is explicitly represented in a document, spreadsheet, diagram or simulation, the model represents what is known about the way the world will work and it is the final basis upon which decisions are made. The better the model, the better the decisions. It is with models that we can make sense of the future.

The New Era of Agency

In the Industrial Revolution, it was knowledge of ‘stuff’ that created wealth - how to transform materials into useful items. In the Information Age, it was knowledge of information that created wealth – how to transform information into items of value. The value of material knowledge didn’t go away, it continued to have value and, in many respects, advanced as rapidly as information knowledge.

The new millennium is seeing an emerging era that is just as significant as the industrial or the information revolutions and will be the source of just as much, if not more wealth creation as these previous two revolutions. This is the era where knowledge of agency will hold sway. Knowledge of Agency (KoA) is that of agent behavior – what agents do and why, when and how they do it. Agents are people or groups of people in organizations, communities and societies. They act and behave in ways that affect every new innovation.

We are seeing the signs of this agency in many recent endeavors:

• Web 2.0 and the collaborative creation movements
• New venues such as MySpace, YouTube, Flickr, etc.
• Open source creations such as Linux and Wikipedia with disruptive business models

The wealth that is being generated by these efforts and others like them, are powered by the knowledge of agency – how people think, choose, interact, experience and adopt new things. It is precisely this knowledge that will be critical for companies to develop if they are to thrive.

What does KoA mean and why now? It means knowledge of how and why independent, cognitive agents act, be they individuals, groups or organizations. KoA is all about the relationships, influences and actions of people and what causes these. It is all about the underlying human cognitive processes and the similarities and differences between people. It is all about the human experience and what is shared and what is private. It is all about how to form relationships and use these relationships to accomplish things. In other words, KoA is about the technology of people and their relationships.

Over the next decade(s) it is this knowledge that will create the most exciting and interesting opportunities. This new knowledge, which has a long history of research in psychology, sociology, economics, complex systems, etc., is what innovation technology and the new processes of innovation are all about. The new technology of agency is just now being ‘actualized’ in domains as diverse as Web 2.0 and design engineering. Why it is happening now is a complex question whose answers are not entirely clear, but it probably has something to do with the combining influences of advancements in human cognitive understanding with the inherent interactivity, social community, and collaborative environments made possible by the low cost communication enabler, the internet. Just like the past two revolutions, the knowledge of agency will not supplant the value of knowledge of material or information. These will still be necessary, but they will not be sufficient to excel. Knowledge of agency will become the necessary source of wealth creation in the coming decade. This new knowledge, and how it is discovered, transformed and used, will be the determining factor of the successful innovator of the future.

1 Information as Resource; 1982
2 Management Support Systems – 1987
3 From Data to Wisdom – 1989