Coolfarming Ideas through Knowledge Flow Optimization - Boosting Organizational Performance through E-Mail Social Network Analysis
Over the last fifteen years our research group at the MIT Center
for Collective Intelligence, University of Cologne and University of Applied
Sciences Northwestern Switzerland (FHNW) has studied hundreds of organizations
through the lens of their social networks, extracted from the organization’s
e-mail archive.
Among many others we have studied R&D organizations at
car manufacturers, marketing departments at banks, sales teams at high tech
manufacturers, medical researchers and doctors at large hospitals, and service
delivery teams at large consulting and service provider firms. In addition, we
have also looked at collaboration in open source organizations like Eclipse software developers, Wikipedians, and online communities on Facebook
and elsewhere.
We have developed a 4-Step Process which we call “Knowledge
Flow Optimization” to study and increase the performance of organizations, to
“coolfarm ideas” (see figure below).
It consists of the four steps “Analyze – Predict – Mirror –
Optimize”. To illustrate our approach, I describe the analysis of a fortune 500
high-tech company, where we compared e-mail communication of the organization
with sales success of their sales teams in the different geographical regions.
Step 1: Determining Social Network Metrics
and Communication Patterns
In
the first step we analyze and quantify the communication patterns and social
network structure embedded within organizational communication archives such as
email, video conferencing and instant messaging. Quantified communication
patterns include metrics such as average response time to messages, sentiment
and contribution index. Contribution index is a measure of the balance of communication in
terms of the messages sent and received by an individual. These are
complemented by metrics computed using Social Network Analysis that are
measures of social influence (or centrality) and their trends over time.
Step 2: Honest Signals: Comparing
structural attributes with business success
In
the second step we compare communication behavior found in step 1 with communication
patterns that we have identified over a period of 12 years in over a hundred
ONA projects carried out by our team. These patterns, also called “honest
signals”, are indicators of better connectivity, interactivity and sharing
among the individuals in the network. There are 6 honest signals that we look
for, namely: Central Leaders, Rotating Leadership, Balanced Contribution, Rapid
Response, Honest Sentiment and Innovative Language. Having calculated these
honest signals from the data in the communication archives, we then correlate
them with quantified success and failure criteria. The success and failure
criteria vary significantly depending on the type of organization, the industry
and the individuals being measured. In this example we measured sales
performance of the sales teams in different geographic regions and for
different products.
Step 3: Virtual Mirroring
In
the next step we mirror the communication behavior we have identified for the
different parts of the organization back to the teams and individuals. By
showing them how they differ from the best practices we found in past projects,
we help them to improve their behavior for better performance. Just like with a
real mirror, looking at how a team “really” communicates can be an eye-opening
experience for the team members, leading to fundamental changes in their
behavior for the better.
Step 4: Devising a plan to optimize
communication for greater success
Once
we figure out which of the honest signals are correlated with success and
failure, we developed a roadmap for the company to change sales communication
behaviors leading to more successfully closed deals and more satisfied
customers.
Why it is different?
When we do our analysis, we frequently get asked about the
details of our method. There are a few key principles that I would like to
point out that set our approach apart from other e-mail based and SNA approaches
Measuring “True
Creativity” – our framework is based on the notion of COINs (Collaborative Innovation Networks). It has been field-tested in over hundred organizations to identify the communication patterns indicative
of creativity. This includes far more than simply counting the number of e-mails of
individuals and teams, rather, using the six honest signals of creativity
listed above we have identified complex networking patterns of true creativity.
Know Cool People, and
not just Hotspots – our semantic social network analysis tool Condor finds
trends by finding the trendsetters. In the first step it works like Google, to
identify who is using novel words and ideas, but then it finds the cool people,
by measuring who uses novel words first,
and how quickly they are picked up by
others to grow into new COINs.
Anti gaming - We are using social network analysis
metrics such as “betweenness centrality” and time series of e-mail exchange, which
are far more robust towards “gaming” by employees than simply counting e-mails
sent and received.
Measuring
organizational trust and satisfaction - We are not just counting complex words, to
measure complexity in dialogue, and counting positive and negative words such
as “great”, “wonderful”, “horrible”, “awful”, but through machine learning
algorithms track word distribution and model positivity and negativity in
context.
Understanding
communication galaxies – we track the evolution of network positions of
people, measuring how individuals change from being “stars” to becoming
“galaxies”, as the most creative people and most highly functioning teams act
as communication galaxies embedded into clusters of other teams.
Using E-Mail based Social Network Analysis gives an organization an unprecedented view into the nervous system of the organization, and allows it to predict flash points before they happen, leading to greatly improved performance and reduction in risk.
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