Knowledge is dynamic and constantly evolving, and knowledge grows through sharing. Engelbart’s vision embodies both principles effectively, providing a superior framework to other theories of intellectual capital management or organizational collective intelligence. I had the rare honor of meeting and working with Doug Engelbart in 2006 and this meeting reinforced these principles and gave me an appreciation of the potential to scale them.
I will attempt to describe my understanding of this vision through practical examples of current day strategy and process applications, in organizations and in national policy.
Let me begin by comparing Doug’s theory of collective intelligence with a management theory that has gained acceptance in both the corporate environment and in national policy. The 1990’s and the early 21st century saw a movement towards understanding how an organization can identify, measure, manage, leverage and act upon its collective intelligence towards the pursuit of sustainable innovation. This school is widely referred to as the Intellectual Capital Management (ICM). I was influenced by the intellectual capital movement and the effort to measure intangible assets. Intellectual Capital Management is the third big idea in management thought in the late 20th century (after Business Process Re-engineering (BPR) and Total Quality Management (TQM)), according to Thomas Stewart, editor, Harvard Business Review.
The ICM framework defines an organization’s intellectual capital as being a sum of its market capital (relationship with suppliers, customers, and brand value), structural capital (internal structure, computer systems, patents, etc.) and its human capital (educational experience of people in organizations).
Many ICM measurements have been successfully adapted in organizations.
Yet, none of them has been successful in improving collective innovation or utilizing collective intelligence to solve complex problems. The Engelbart theory manages to do that because it recognizes knowledge as being dynamic and constantly evolving.
The traditional ICM frameworks are static. They assign a value to education, to the years of experience of the employees. They fail to measure: What kind of knowledge is being transferred? Is it tacit to explicit? Or tacit to tacit? How much is being learned? How quickly? By whom? And how is it being applied? Are there new innovations in the applications of that knowledge?
The traditional ICM frameworks fail to factor the dynamic nature of knowledge and its growth due to constant interaction between individuals. The Dynamic Knowledge Repository (DKR), as envisioned by Engelbart, is a more accurate measure of collective intelligence.
The traditional ICM school of thought does not account for the formal and informal networks. Networks are another component of Engelbart’s model. Networks can help overcome the barriers to change. Social networks exist in every organization and are used to find information and solve problems. Organizations have been formalizing these groups and providing the web-based connectivity in order to leverage collective knowledge since the 1980’s. Doug Engelbart envisioned the Network Improvement Communities (NICs) in the 1960’s. NICs are networked communities of practice (CoPs) that maintain protocols and practices with the goal of sharing information in order to improve processes. Studies show that these trial NICs rapidly reduce the learning curve of new employees and generate new ideas.
The ICM framework does not account for the organization’s adaptive capacity and response time. How an organization responds to threats and opportunities is another element of the Engelbart model. Absorptive capacity is the external capital, and is probably a far more accurate measure of its success in responding to complex and urgent problems than its brand equity.
I was able to create a robust strategy and measurement model by incorporating Engelbart’s system with frameworks for managing intangible assets. My first implementation was with India’s National Knowledge Commission (NKC). The National Knowledge Commission was a high-level advisory body to the Prime Minister of India, with the objective of transforming India into a knowledge society. It included a broad spectrum of partners from education to e-governance. The commission’s objectives included:
• easy access to knowledge
• creation and preservation of knowledge systems
• dissemination of knowledge and better knowledge services
The NKC worked with Doug Engelbart on understanding the value of high speed research and education networks. Based upon the recommendations of the NKC, the government of India has created a National Knowledge Network. The network has a capacity covering 1000 nodes with gigabit capacity scalable to 10,000 nodes/institutions.
Other key aspects of Engelbart’s vision include the dynamic knowledge repository and networked improvement communities. The DKR is essentially an easily accessed decision support system. It is a continuously growing repository of collective knowledge that provides information to solve complex and urgent problems. An example of this would be at Daimler AG, where it was used to correct a materials error problem almost instantaneously. Daimler AG was one of the early adopters of communities of practice and used a web-based DKR-like tool. When the materials problem occurred, one of the engineers in a plant in Germany came up with a “quick-check” solution and shared it with his peer group through the EBoK. This method was applied in all plants globally and resulted in large savings for the firm. The adopted method also became standard practice on future projects.
Another example of applications of the DKR and NICs is a project to improve the requirements elicitation process in a client organization. Fifty percent of defects can be traced back to errors in requirements. Thus, getting the requirements elicitation process right is critical for project success. Requirements are constantly evolving and a single business analyst rarely understands all the impacts of a proposed change. Hence, it was important to make the collective knowledge of the analyst, operational and systems teams available to the all members.
This knowledge had to be codified and stored in a central repository. It also needed to be easily accessible and to be dynamic. Every change or modification to the system, every new learning and innovation, could change the current state of the system and impact the requirements accuracy. My team created a dynamic knowledge repository that is supported and maintained by a NIC that meets regularly to discuss and review requirements. Members of the NIC take stock of changes and re-evaluate a requirement.
We incorporated the principle of bootstrapping and acknowledged that each time a change is made or a problem is solved, it leads to a completely new situation. A good strategy requires continual reevaluation of the problem. The result was an integrated knowledge-based dynamic process similar to Engelbart’s CoDIAK.
I have used knowledge mapping as a tool for post-merger integration strategy in organizations for over nine years. I’ve realized that most of my methods are akin to Doug’s theory of future mapping and facilitated co-evolution.
Knowledge-based integration is a very effective method, especially in the context of highly diverse cultural differences between the merging organizations. A three-company merger can be especially challenging when the three organizations represent three nationalities and three varied approaches to knowledge management.
The British view knowledge as being managed by rules, the French share through a social network (who knows whom) and the Germans like to convert all tacit knowledge to explicit form. The best way of getting these three teams to work together is to map and understand the various components of the knowledge required to meet the new organization’s goals and the direct benefits that they provide to each other.
These form the motivators for sharing and reevaluating the old processes to develop new ways to share knowledge and improve the organization’s collective IQ. We build our processes around knowledge needs and flows, and develop tools and technology to facilitate these processes.
Collaboration is a key to overcoming any complexity or solving problems effectively, whether it is in a firm or in society. There is now a lot of interest in collaboration and collective intelligence. Engelbart’s vision and methods give us a tool to start collaborating both technologically and strategically. However, there is still a need for measurement, to demonstrate the value of collaboration and knowledge sharing. This is especially true in a globally competitive world, where knowledge is power and people may be motivated to hold on to their knowledge in order to hold on to their jobs. The very same technology that facilitates collaboration and globalization could also impede it. It is now time to implement the strategic aspects of Engelbart’s vision in order to ensure successful collaboration.
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Gopika Kannan is a consultant, researcher and author specializing in knowledge and collaboration-based post merger integration strategy. She has worked with large aerospace, automotive, research and development, information technology, and insurance corporations in Europe, North America and South East Asia, developing and implementing her own unique methodologies and strategies.
She also worked with the India knowledge commission on national knowledge strategy and valuation of research and education networks. She has designed innovation and knowledge reuse tools that have been widely adopted in corporations. Gopika has published in several peer reviewed journals, conference proceedings and edited volumes on knowledge management. She is a reviewer on the editorial board of the Management Decision Journal.
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