CI Engine is a rich application, providing
a highly visual and intuitive way to quickly recognize patterns
inside organizations using collective intelligence techniques.
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Categories and indicators. CI Engine
measures collective intelligence against specific
reference points, which we call categories and indicators.
Categories represent the top-level issues about
which we are interested. Indicators represent more
detailed aspects of the categories being studied.
Indicators inside each category are aggregated together
to yield quantitative scores for each category.
These aggregated scores are the measure of collective
intelligence.
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Category comparison. Categories represent the top-level organizational
issues being examined by CI Engine. This screen
shows a category summary ranking, highlighting each
item with colors ranging from dark red to dark blue.
Dark red indicates a problem or vulnerability in
the organization, while dark blue shows a non-risk
aspect of the situation being examined.
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Bubble chart. The bubble chart
ranks indicators visually. This view provides a
project overview, giving an immediate visual representation
of key areas in the organization that require further
examination. Indicators are ranked on three dimensions
in the bubble chart: vulnerability, importance,
and consensus.
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Indicator comparison. CI Engine automatically sorts and ranks indicators,
based on criteria established by the user. The color-coded
ranking system makes it easy and fast to see patterns
in the data. CI Engine helps consultants rapidly
discern important issues and patterns in the organization
being examined.
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Category / indicator detailed analysis.
All data in the system can be seen in a series of
drill-downs, allowing high-level patterns to easily
be recognized; details of all collective intelligence
data in the system can also be viewed quickly. Unlike
anecdotal methods of organizational analysis, all
conclusions can be substantiated by drilling down
into the raw, fully quantified data.
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Participant
analysis. Color-coding makes patterns in
the data immediately apparent. Blue columns suggest
participants with strongly positive views, while
red columns indicate negative perspectives. We can
immediately see discrepancies between how various
participants view the exact same situation. These
differences clearly suggest lack of consensus on
key issues, which could be of concern to the organization.
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