Valdis Krebs: Profiles in Knowledge

Originally published November 27, 2018

19 min readNov 28, 2018

This is the 36th article in the Profiles in Knowledge series featuring thought leaders in knowledge management. Valdis Krebs, Founder & Chief Scientist at Orgnet LLC, is an organizational consultant, data scientist, mentor, and the developer of network analysis software.

I first encountered Valdis in the Association of Knowledgework (AOK) online community discussions. I observed that his replies could be quite direct and challenging. So when I hosted the Star Series Dialogue in January, 2006, and I received an initial reply from Valdis, I was a bit nervous. But after I read it, I was greatly relieved:

Jan 24, 2006 — STAR Series with Stan Garfield

From: Valdis Krebs
Subject: re. Connecting Theory to Practice — Stan Garfield

Bring it on Stan! Make it real! Let the ontologists hibernate with the bears — after all, it is winter up here. ;-) I’m all ears for real stories.

I first met Valdis in person at the Ohio KM Cluster later that year. I found his presentation fascinating. He lived in Cleveland, where I frequently visited my mom and my sister. And he has a degree from Michigan State, as do my twin daughters. So we became friends, and he has presented twice at the Midwest KM Symposium in Cleveland, and once on the SIKM Leaders Community call that I lead.

Background

Valdis Krebs is a management consultant and the developer of InFlow, a software based, organization network analysis methodology that maps and measures knowledge exchange, information flow, communities of practice, networks of alliances and other networks within and between organizations. Through eye-opening graphics and revealing measures, this technique allows managers to see what was once invisible.

Valdis has undergraduate degrees in mathematics and computer science, and a graduate degree in organizational behavior/human resources and has studied applied artificial intelligence.

Valdis is a data scientist who specializes in social, relational, and multimodal data analysis and visualization. Krebs, a network scientist, applies artificial intelligence [AI] and social algorithms, data mining, organizational network analysis, and cluster analysis to business, community and organizational projects for a worldwide client base. He developed the popular InFlow software for social and organizational network analysis [SNA / ONA].

Before starting his own business, Valdis held various HRIS/HRMS management positions at The Walt Disney Company, TRW, Toyota, and Ford Motor Company. Valdis worked from his office in Cleveland, Ohio with a network of colleagues around the world. He now lives in Riga, Latvia and has cut back on his consulting work.

Valdis is a social scientist with over 20 years of organizational consulting and coaching experience, specializing in network analysis (maps and metrics that reveal how an organization really works). Valdis has worked with management teams, scientists, marketers, law enforcement, economic development professionals, community activists, universities, healthcare, and hundreds of business and organizational consultants around the world.

Products and services provided:

Management and team consulting for organizational effectiveness and inclusion
Data analysis algorithms and metrics for pattern discovery in relational data
Network analysis software services for organizations and their consultants
Training and mentoring in organizational network analysis with an international client base.
Personal coach for building/growing strategic professional/business networks
Applying network analysis and relationship discovery for business intelligence and innovation.

Education

Michigan State University — Master’s Degree: MLIR — Masters in Labor and Industrial Relations (HR)
Cleveland State University — Bachelor of Science (B.S.): Mathematics and Computer Science

Profiles

Content

Quotes

“Birds of a feather flock together. Those close by, form a tie.”
“Connect on your Similarities, and Benefit from your Differences!”
“Golden Rule of Networks: Location, Location, Location”
“Innovation happens in Intersections, Collaboration happens in Commons”
“Serendipity resides in your network”
“Your Choices reveal who you are, and whom you are like!”

Articles

Articles by Others

2012 Political Book Buyers Less Polarized Than in 2008 by Micah L. Sifry
What Facebook and Steroid Use Have in Common by Jose Fermoso
Social Network Analysis: What to Map by Dave Pollard
Can Network Theory Thwart Terrorists? by Patrick Radden Keefe
How The NSA Uses Social Network Analysis To Map Terrorist Networks by Greg Satell
Mapping Insights by Heath Row
Valdis Krebs’s analysis of the “social graph” of book sales indicates that the filter bubble isn’t limited to Facebook! by Tim O’Reilly
Making The Invisible Visible With ONA by Jason Lauritsen
Business Week Articles

Interviews

As Quoted by Me

1. Social Network Analysis: SNA, ONA, VNA

Social network analysis: mapping and measuring of relationships and flows between people, groups, organizations, computers or other information/knowledge processing entities; the nodes in the network are the people and groups, while the links show relationships or flows between the nodes — provides both a visual and a mathematical analysis of human relationships

You can use SNA to bridge silos, create awareness of distributed expertise distributed in the network, and identify and draw in peripheral network members. In Building Smart Communities through Network Weaving, Valdis Krebs and June Holley assert that “improved connectivity is created through an iterative process of knowing the network and knitting the network.” SNA enables you to know the network so that you can then proceed to weave new members into it.

In his profile, Valdis advises to “connect on your similarities and profit from your diversities.”

Social network interaction will become the engine for innovation. Master it. Good advice from the Gartner Group at their Symposium/ITxpo: Emerging Trends. They gave 4 core messages for Leading Edge IT Change. Message #1 includes network mapping and network weaving. Gartner said that Social Network Interaction is where leading-edge companies will make their mark and wield their influence. It advised CIOs and IT leaders to:

Expose your trickiest business and technology challenges to open forums and learn how to identify real contributors.
Solicit and respond to customers’ input, feedback and new service ideas through communities of customers.
Use social network analysis software to map out how information and ideas flow among your people across regions, continents and business entities.

Inflow

2. Team Networks → Team Chemistry

Teams are not made of talent alone.

It is how the talents of individual players intersect and interact that distinguishes a good team from a collection of good players. From the New England Patriots, to the Detroit Pistons, to the Chicago White Sox — teams without a superstar at every position win championships.

UPDATE #1: US Basketball Team Lacks “Chemistry” ends up with “Le Bronze” in World Basketball Championships!
UPDATE #2: Superstar NY Yankees lose again!
UPDATE #3: Once again, the Yankees lose!

3. Melissie Rumizen: Profiles in Knowledge

Valdis Krebs believes initial assessments are sooooo important, declaring “My most successful clients dive into the assessment and emerge as the key assessors with the new knowledge/feedback I provide them.” Jack Ring said, “The more foolproof way to determine ‘current state’ is to make ‘know how’ a specific factor in the risk assessments that are done with respect to enterprise objectives and goals. True, the state of the knowledge asset is hard to verify. But the value is not in the asset; the value is in the organization’s ability to apply the asset in pursuit of valuable results.”

4. 5 Modes of Knowledge Flow

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: 1. Birds of a feather flock together. 2. 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.”

Communities

1. SIKM Leaders Community

2. Association of Knowledgework

April, 2005 — Preparing for Conversations with Valdis Krebs
Discussion Topics

How do your networks make you smarter? How do you use your networks daily? This conversation will be from each person’s own experience — no theories or ontologies or beliefs!
Stupid organizations. How do organizations with many smart people with access to all sorts of data, information and knowledge make stupid decisions? What do networks have to do with organizational stupidity?
Is it who you know that determines what you know . . . and what actions you will take?

Managing the Connected Organization

If knowledge is power, what is connected knowledge?

The knowledge economy operates on the complexities of connections. All individuals, communities, systems, and other business assets are massively interconnected in an evolving economic ecosystem. In the connected economy, each network actor (individual, team, or organization) is embedded in a larger economic web that affects each participant and, in return, is influenced by that participant. In such a connected system we can no longer focus on the performance of individual actors — we must manage connected assets.

Efforts at making sense of this new world are beginning to reveal some basic principles at work in the complex adaptive systems we call our organizations.

“There is a central difference between the old and new economies: the old industrial economy was driven by economies of scale; the new information economy is driven by the economics of networks…” Information Rules by Carl Shapiro, Hal R. Varian

Recent research on productivity and effectiveness in the knowledge economy provides insight into what works in the connected workplace. Certain patterns of connections appear around both effective individuals and successful teams when performing knowledge work. We have also discovered where to add ‘missing links’ that change a poor economic network into a better conduit for information, influence, and knowledge.

Improving Individual Effectiveness

Is it who you know (social capital) or what you know (human capital) that leads to success? This has been often debated with good arguments on both sides. Most managers today side with the “what you know” crowd.

In the late 1980s management researchers were starting to notice that some managers were better, than other managers, at accomplishing objectives through relationships. John Kotter, of the Harvard business school, discovered that effective general managers spend more than 80% of their time interacting with others. Other management scholars were also starting to see the importance of conversations and relationships in managerial work. Individual mastery was no longer the key — it was human capital and social capital working together to create productivity and innovation. Ron Burt, of the University of Chicago, a leading researcher on the social capital of managers has found, through numerous studies, that certain patterns of connections that individuals build with others brings them higher pay, earlier promotions, greater influence, better ideas and overall greater career success. Burt believes that good social capital provides a much higher return on investment in human capital — the two work together.

Arent Greve, a researcher at the Norwegian School of Economics, is also interested in the contribution of human and social capital on organizational outcomes and individual productivity. He studied project managers in a knowledge-based services company in Europe. He viewed human capital as the knowledge and skills attained by the individual over his/her career. Social capital was defined as a property of personal networks — the ability to reach others, inside and outside the organization, for information, advice and problem-solving. He found something very interesting. As expected, better human capital and better social capital both had a positive effect on productivity, but unexpected was the effect of better social capital was noticeably stronger! Project managers with better personal networks were more productive — they were better able to coordinate tasks and find the knowledge necessary to accomplish the goals of their projects.

Improving Team Effectiveness

Meanwhile in a high-tech firm in Silicon Valley, Morten Hansen, also of Harvard, had a similar research agenda. The key difference was that Hansen was interested in the productivity and effectiveness of teams. Hansen found that teams who could easily reach other teams and access the knowledge they needed were more successful than teams with poor network connections. Both Greve and Hansen found that the ability to reach a diverse set of others in the network through very few links was the key to success.

Hansen took his research one step further. He examined the difference between those teams that had many direct connections to other project teams and those that used both direct and indirect ties to reach the resources they needed. Hansen found that those teams that used only direct ties to seek and find information were soon overwhelmed with too many connections. The teams that used the power of the indirect tie, while at the same time limiting their direct ties, were more successful — they did not spend as much time interacting with the network to get what they needed. A sparse radial network in which your direct ties are connected to others that you are not connected to, has been shown, by Burt and others, to provide many benefits and opportunities.

Hansen discovered one other insight that is key for knowledge management. A diverse radial network with many unique indirect ties is good for monitoring what is happening in the organization and for discovering pockets of knowledge and expertise. Yet, this type of network may not be useful for transferring knowledge. Although indirect ties help you cast a wide net and see far into the organization (and beyond it), these ties are not always efficient for transferring and utilizing knowledge once it is discovered. It depends on what type of knowledge needs to be transferred. Explicit knowledge, which can be easily codified, can be transferred indirectly through various technologies such as email, FTP, WWW or documents through interoffice mail. For example, sharing a presentation done previously for the same customer. Complex tacit knowledge knowledge requires direct interaction and sharing of experiences between two or more individuals. To transfer tacit knowledge a direct tie with the knowledge source(s) must be established. Trust and understanding must be built — this is similar to apprenticeship. Explicit knowledge travels over computer networks, but tacit knowledge is shared and learned via human networks.

Improving Information Flow

Network ties are distributed unevenly in organizations. People that work together form networks together — clusters emerge around established work relationships. Engineers working on Project X form a cluster, those working on Project Y form a cluster, and those working on Project Z form a cluster. Everyone knows everyone else within the local cluster, and yet only a few individuals have boundary spanning ties to other clusters. Strong, frequent, ties are usually found within clusters, while weaker, less frequent ties are found between clusters.

Clusters of concentrated connections appear throughout an organization and throughout industries. Some clusters have many ties outside the group, while other clusters have only a few. Poor connections between clusters result in very long path lengths throughout the organization. In such a network it is easy to access those in your cluster but not those in other clusters. This often results in distant clusters not knowing what information and knowledge is available elsewhere in the organization.

Often the knowledge you need is in clusters other than your own. Networks have a horizon beyond which it is difficult to see what is happening. Research by Noah Friedkin, at the University of California at Santa Barbara, has shown that this horizon of observability is usually two steps in a human network — your direct contacts and their direct contacts. Around three steps out, things are real fuzzy — you do not have a good idea of what is happening in that part of the network. Beyond three steps, you are blind to what is happening in the rest of the network — except for obvious ‘public’ information known by everyone. So the popular idea of it being a ‘small world’ because we are all separated by an average of 6 degrees is misleading. Six degrees is actually a very large world — one, two and three degrees is a small world! It is usually those separated by two degrees where the ‘small world’ discoveries happen — it is here where you discover the person next to you on the plane is related to a friend from your university days.

In a network of very long path lengths between clusters, your ability to find the knowledge or information you need is very constrained. If the knowledge that you seek is not within your network horizon[1 or 2 steps], then you assume it is not available in your organization and you reinvent it, or pay for it on the outside. Exasperated with this network horizon in his organization, a former CEO of HP once lamented, “If we only knew what we know”.

The natural response in many organizations is to throw technology at the problem. A very poor, yet quite common, solution is to mine knowledge from employees, codify it, and store it in a knowledge database. Many large consulting firms tried this approach in the 1990s with usually poor results. They found that people were not always willing to make public their best knowledge and that codifying tacit knowledge was like trying to nail jelly to the wall.

Why not use the power of the network itself to create a solution? Improve the organizational network and then use technology to help people communicate across wide spans of the human network. At first blush, improving an organization-wide network may seem an overwhelming task. Where do we start? First, look at the networks and communities of practice/interest/knowledge that have organized around a specific topic, product, service or customer. Usually the whole organization does not have to be included in the problem space. Second, map out the network nodes and their connections (who goes to whom for expertise/knowledge/advice on X?). From this network map, you can see the various clusters and how they are connected. Figure 1 below is a network map of project teams. A line connecting two teams indicates a two-way information flow or exchange of knowledge.

Figure 1

This network of 17 project teams all work on subassemblies to a larger product. The teams are composed of mostly engineers, technicians, and project managers. All teams have less than ten members. Three clusters are evident in the network of project teams.

Before we look at how to improve the overall connectivity of the network, let’s digress back to social capital. Which team has the best social capital in this network? Which team can access all of the knowledge and resources in the network quicker than the others? (Hint: this network is drawn to reveal the answer.)

Common wisdom in networks is “the more connections, the better.” This is not always true. What is always true is “the better connections, the better.” Better connections are those that provide you access to nodes that you currently do not have access to. Although Team F and Team Q have many connections each and have excellent local access (to the nodes near them), they have only fair access to the rest of the network. Team O has the best social capital (aka network benefits) in this network of project teams. Team O achieves this with only three direct ties — it is connected to others who are well connected. Team O’s indirect contacts bring access to information and knowledge not available locally.

The average path length in this network is 3.45 with many paths longer than the network horizon. Even in this small network there are nodes[teams] that are nearly blind to what is happening in other parts of the network.

In the summer of 1998, writing in the scientific journal Nature, a stir of excitement was generated by two mathematicians from Cornell, Steven Strogatz and Duncan Watts. While investigating small-world networks (those with many clusters), they discovered that a few randomly added crosscuts between unconnected clusters would improve[i.e. lower] a network’s characteristic path length significantly. The benefits were not just local, but spread throughout the network and this improvement could be achieved with just a few added ties in the network. Very small adjustments could cause large positive changes — a common dynamic in complex adaptive systems.

Looking back on our project team network in Figure 1, how can we improve the connectivity with just one added link? Which two nodes would you connect to bring everyone in the network closer together?

Although many combinations will increase the access of everyone to everyone else, the greatest measurable effect is when we add a crosscut between Team Q and Team F. The average path length drops a whole step! The longest path in the network is reduced from 7 steps to 4 steps. In human networks, the fewer steps in the network path, the quicker information arrives with less distortion.

Figure 2

The connection between Teams Q and F may be the optimal connection in network efficiency, but it may not be a practical connection. Both of these teams already have many ties and may not have the time and energy to support another one (remember what Hansen discovered about too many direct ties?). What is an alternative connection? If you cannot connect the highly connected nodes, how about connecting their respective network neighbors? Instead of connecting Q and F, how about connecting D and Z? This connection will not reduce the path length as much, but it is between nodes that are not overburdened with connections.

Leading Edge Management

One of the benefits of consulting with organizational network analysis is having leading edge clients. Not only are they open to new methods to improve their organizations, they usually end up teaching me quite a bit. One such client is Vancho Cirovski, Vice President of Human Resources at Cardinal Health. Vancho, an expert soccer player and coach, first noticed an interesting phenomenon on the playing field. Teams that were more integrated and communicated well among themselves on the field, more often than not, beat a collection of individually superior players who were not interacting well on the field. I saw a similar phenomenon on my son’s soccer team. They had good players, but were divided up into several cliques which did not get along with each other.

Vancho saw the same effect in project teams inside organizations. He has summarized these concepts of managing connected organizations using Einstein’s famous formula:

E = MC2
M is the Mastery of each individual (human capital)
C are the Connections that join individuals into a community (social capital)
C is the Communication that flows through those Connections
E is the resulting Effectiveness of the team or organization

The effectiveness of a team or organization is based on personal know-how, enhanced by communication, information flow and knowledge exchange through both direct and indirect connections.

A common reason for the failure of many mergers and acquisitions is the failure to properly integrate the two combining organizations and their cultures. Although a formal hierarchy combining the two organizations may be in place, the right work relationships are never formed and the merging organizations remain disconnected. Ralph Polumbo, Vice President of Integration for Rubbermaid’s 1998 acquisition of its European competitor, Curver, wanted to make sure the two organizations were combining effectively. He decided to map and measure the melding of information flows, work relationships and knowledge exchanges — connections that help cultures combine. His vision was one of a boundaryless organization with no fragmentation along former constituencies. He wanted to track where integration was happening and where it was not occurring. By examining his human and social capital concurrently, he was able to visually monitor the successful integration of both organizations.

How can managers improve the connectivity within their organization? Here are a few places to get started:

Look beyond the individual — uncover their interconnections and multiple group memberships.
Know the difference between tacit and explicit knowledge and how it is shared and transferred.
Reward people for directly sharing their know-how, for including others in their knowledge-sharing networks.
Design computer systems that facilitate conversations and sharing of knowledge — think communication, not storage/retrieval.
Help women and people of color connect to key knowledge flows and communities in the organization. This may help eliminate the glass ceiling.
Recruit new hires through the networks of current employees — they will be happier, adjust quicker, and stay longer.
When transferring employees keep in mind their connections. Exchanging employees with a diverse network of ties can create shortcuts between departments or teams and greatly improve the overall information flow.
Ensure better coordination of behavior between departments or projects by adding crosscuts to minimize the path length of their information exchange networks. To reduce delays you want some redundancy in the paths — if one is blocked then alternative communication paths are available.

For the HR department it is no longer sufficient to just ‘hire the best.’ You must hire and wire! Start new networks, help employees and teams connect — connect the unconnected!

What is connected knowledge? A competitive advantage! Your competition may duplicate the nodes in your organization, but not the pattern of connections that have emerged through sense-making, feedback and learning within your business network. And if you get Vancho’s take on Einstein’s formula correct, then connected knowledge is pure energy!

In the 1992 U.S. presidential race, one simple phrase refocused and re-ignited a jumbled campaign effort by Bill Clinton — “It’s the economy, stupid.” Adaptive businesses see the benefits in managing connected organizations. We can adapt the old campaign slogan to reflect the new network reality — “It’s the connections, stupid!”