In my previous post, I discussed the first two modes of knowledge flow: collection and connection. This post covers the next two modes: boundary spanning and discovery.
Boundary spanning: bridges across organizational boundaries for enabling knowledge to flow between previously isolated groups
In Building Smart Communities through Network Weaving, Valdis Krebs and June Holley define boundary spanners as “nodes that connect two or more clusters – they act as bridges between groups.” They go on to observe: “When left unmanaged, networks follow two simple, yet powerful driving forces:
- Birds of a feather flock together.
- Those close by, form a tie.
This results in many small and dense clusters with little or no diversity. Everyone in the cluster knows what everyone else knows and no one knows what is going on in other clusters. The lack of outside information, and dense cohesion within the network, removes all possibility for new ideas and innovations.”
To overcome this tendency, it is important to make explicit efforts to establish links between different groups. Examples include different regions of the world (e.g., North America, Latin America, Europe/Middle East/Africa, and Asia Pacific), functions (e.g., engineering, manufacturing, marketing, sales, logistics, and service), business units (e.g., paper products, cleaning products, and health products), roles (e.g., interns, retirees, and contractors), and organizations (e.g., employees, customers, and partners).
An example of how boundary spanning can help overcome organizational barriers is product development and introduction. Marketing tells Engineering to develop a new product to meet a customer need. Engineering designs the product, which is produced by Manufacturing. Marketing promotes the product, which is sold to customers by Sales and delivered by Logistics. Service installs the product, and repairs it if the customer experiences problems. A community focused on a specific product that includes members from all of these functions can help them collaborate across boundaries.
One of the following collaboration conditions typically exists in an organization. These are listed in increasing level of connectedness:
- There are no communities. Small work teams collaborate, but there is limited collaboration beyond the teams.
- There are some communities within functions. For example, a community of engineers who help each other out with designs.
- There are some communities that span some functions. For example, a community for engineers and service people for a specific product.
- There are some communities that span all functions. For example, a community with everyone involved in some way on a specific product.
- There are communities for all offerings that span all functions, and include customers and partners. This is true boundary spanning.
In the implementation plan, identify all groups that need to connect, and include boundary spanning as a required knowledge flow. The higher the level of connectedness you can achieve the more knowledge will flow between groups. You can use social network analysis to help determine the current state of social networks and to identify boundary spanning opportunities.
Discovery: processes for learning from existing sources of information, including systems, databases, and libraries
In most organizations there are information systems, transaction processing applications, and databases that are used to run the business. There is data captured in these systems that can be used to distill trends, answer queries, and support decision making. And this can be done without the need to capture data redundantly. For example, if customer purchase information is entered into the order processing system, it can be fed to a data warehouse for use by all departments.
Many organizations have libraries of information obtained through outside sources. These may include competitive intelligence, analyst reports, industry news, and benchmark data. Providing access to this information supports analysis, strategy formulation, and planning. If such libraries do not exist centrally, you should consider providing them to prevent individual departments from purchasing information on their own. If they do exist, then your plan should incorporate them into the resources provided through the user interface.
Many of the specialties needed for knowledge management can be used to support discovery, including:
- Analytics, text analytics, and visualization
- After Action Review
- Ritual dissent
- Appreciative Inquiry
- Positive Deviance
- Most Significant Change
- Business Intelligence
- Databases and repositories
- Big data, data warehouses, and data lakes
- Competitive intelligence, customer intelligence, market intelligence, and research
- Cognitive computing, artificial intelligence, natural language processing, machine learning, and neural networks
The final post in this series will cover the remaining mode and provide examples.
Please read Stan’s additional blog posts offering advice and insights drawn from many years as a KM practitioner. You may also want to download a copy of his book, Proven Practices for Implementing a Knowledge Management Program, from Lucidea Press. And learn about Lucidea’s Inmagic Presto, with KM capabilities to support successful knowledge management programs.
Creating new knowledge is not simple or intuitive, but for knowledge managers it is worth perfecting because the potential benefits are significant.
KM Methodologies are policies, rules, techniques, procedures that prescribe how knowledge work is to be performed and offer ways to do it successfully.
KM incentive and reward programs encourage compliance with goals, improve performance against metrics, and increase participation in KM initiatives.
KM goals and measurements include targets included in employee performance plans and metrics to track performance against those goals and other operational indicators.