Description
Collaborative Business Intelligence Socializing Team-Based Decision Making
9 BUSINESS INTELLIGENCE Journal • vol. 17, no. 3
CollAborATIve bI
Collaborative
Business Intelligence:
Socializing
Team-Based
Decision Making
Barry Devlin
Abstract
For many of us, making decisions is a challenge; for others, it
can be torture. Despite nearly half a century of work in deci-
sion support and business intelligence (BI), many businesses’
decisions look vaguely dysfunctional.
If we examine how most organizations really make important
and innovative decisions, we see that most are made by
teams (permanent or transitory) of people rather than by
individuals. It’s high time we designed an effective approach
to true decision-making support—what we might call
innovative team-based decision making.
This article presents a new model we call iSight that maps the
path from the information cues that signal change is required,
through the team interactions and implementation, and on to
measurable and repeatable innovation. The key to this pro-
gression lies in informal information—the conversations and
meetings, messages, and e-mails that record the actual path
of decision making but which are largely lost today. Capturing
and using this informal information is increasingly possible as
we move to a world where nearly all information is digital.
Our iSight model provides the frst comprehensive framework
in which team-based decision making can be understood,
designed into software solutions, and implemented in highly
innovative and forward-looking organizations.
Barry Devlin, Ph.D., is a founder of the data
warehousing industry and among the foremost
worldwide authorities on business intelligence
and the emerging feld of business insight. He is a
widely respected consultant, lecturer, and author of
the seminal book Data Warehouse: From Architecture
to Implementation. He is founder and principal of
9sight Consulting (www.9sight.com).
[email protected]
10 BUSINESS INTELLIGENCE Journal • vol. 17, no. 3
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Is Business Intelligence Working?
In the mid-1990s, Gartner analyst Howard Dresner
popularized the term business intelligence (BI), words that
suggest deep thought and extensive, rational decision
making. However, what we get from vendors and IT is
closer to what we used to call a decision support system
(DSS). Dan Power (2007) identifes fve classes of DSS,
within which BI fts mainly as a data-driven and, to a
lesser extent, model-driven DSS. Te actual focus of BI
tools is on the collection, analysis, and presentation of
largely numerical, mostly internal information to indi-
vidual decision makers. Te assumption is that having
provided enough “good” information, IT can stand back
and watch the business make “better” decisions.
Tis is only occasionally true, at best, as shown as far back
as 1999 (Gigerenzer et al, 2000). In recent years, much
of the focus has turned to operational BI and big data—
making ever-faster, smaller decisions based on ever-larger
data sets. Yes, the world of business is spinning ever faster
and daily decision making has to keep pace, but the type
of change we’re experiencing now is revolutionary. Te
social and economic fabric of our world is being torn
apart and remade in constant and repeated seismic events.
Sovereign debt ratings of formerly unassailable AAA
countries are being downgraded. Previously blue-chip
businesses in every industry have fallen as new kids on
the block charm Wall Street.
Decision making needs to be fast and it absolutely must
be innovative—in a diferent league from what we’ve
done before. Such decision making is a team efort,
especially for decisions that require or produce innovation.
Te truth is that such innovative decision making has
little to do with the explicit, largely numeric data we’ve
focused on for over 20 years.
Tose of us who have worked in large enterprises have
seen sufcient evidence to conclude that many decisions
have a rather shaky relationship with facts and business
intelligence, and limited relevance even to stated business
goals. How many successful decisions have been declared
as based on “gut feel” and unsuccessful ones blamed on
“lack of reliable information”? How often have we seen
political expedience override a strongly argued rationale?
Ten there’s the directive to “just take one more look
at the fgures” when the numbers contradict the group
wisdom of the boardroom.
What are we missing? Our longtime focus on BI is blinding
us to the fact that the most efective and productive path
from information to innovation is through interaction. A
few ideas do pop into our heads out of nowhere, but
most of our best ideas—useful, productive ideas that can
be implemented—are born from interaction with peers,
colleagues, and even managers.
Te human mind is ultimately a social construct, which
leads us directly to social networking and Web 2.0, which
represents the evolving democratization of the Internet.
Creativity has been open sourced. Centralized control has
given way to geographically separated cooperation. Social
media (such as Twitter and Facebook) allow people to
openly share their observations and opinions and expose
themselves to feedback. Te Web has sparked our innova-
tion, helped us cooperate, and put the focus on teams.
Unleashing such innovation and collaboration within
the business environment is not only desirable; it’s
mandatory. Te generation now entering the workforce
expects nothing less. In the corporate world, however, we
need more structure and control. Enter Enterprise 2.0
(McAfee, 2009)—the business favor of Web 2.0. For
decision making, Enterprise 2.0 opens up enormous
opportunities for interaction. It promises to unleash the
creativity of decision-making teams who gain access to
external information and shared insight. It ofers real-
time collaboration with peers and superiors that can drive
innovation and accelerate decision cycles. Furthermore,
it presents new opportunities for the business to directly
harvest and beneft from the wisdom and experience
of “edge workers”—those who work intimately with
customers, prospects, products, and partners. Tis is true
decision-making support.
Innovative Team-Based Decision Making
A decision-making team is simply a group of people who
have come together to address a business challenge. Team
members may come from a single or multiple units in the
organization; the team may be brand new or its members
11 BUSINESS INTELLIGENCE Journal • vol. 17, no. 3
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may have worked together on previous assignments. Te
team may be based in a single location or be virtual.
Besides the people on the team, we note that in order to
function, the team needs (1) information artifacts that
are used, shared, and created by the members and (2) a
web of interactions between the members. Tis ecosystem
is shown in Figure 1. It’s fairly simple and informal. It’s
the way it works today, and it’s also the way it doesn’t
quite work—we lose so much of importance that goes on
within the work of the team, such as:
?
Context. Te business environment and background
to the decision, the team members involved, and
initiating and closing actions
?
Interactions. All informal communication among
team members and with external parties, including
meetings (face-to-face and electronic), telephone calls,
instant messages, tweets, and even e-mail, if not
stored centrally
?
History. Te performance of team members, the
unfolding of thought processes leading to options
considered and discarded, the timing of events and
when information was requested/received, and a
formal record of how innovation occurred
?
Consequences. Closing the loop between expectations
set in the decision and what actually happened in the
real world
A typical example is a team brought together to
investigate and plan the CEO’s vision of a new process.
Its members come from across the business and from
IT, bringing their skills and knowledge of process and
information needs, approaches, and tools. After the CEO
briefs the team, members begin to gather documentation
on their PCs or even in a content store or team room tool.
As the project progresses, the team interacts with one
another, using and creating further information.
When a new team member comes on board, the only
information about what has occurred so far is what
exists in the team’s formal documents. Knowledge about
previously discarded options exists only in the heads of
the original team members, and the new team member
wastes time and energy exploring invalid options. Eventu-
ally, the team concludes on a new strategy and plan for
the process, and presents it to the CEO, who is only
partially satisfed. Some information had been lost—the
only record of the CEO’s briefng is in the participants’
handwritten notes, which are inconsistent and incom-
plete. Te team returns to work suitably chastened.
INFORMATION
(External, formal)
INTERACT
• Strategy
• Plan
• Process change
• Specifc action
• Form another team
• Etc.
Information input
and use
Shared information
Interaction
Person
Team
INNOVATE
Figure 1. The current decision-making ecosystem.
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Te lost information and undocumented work points
clearly to a need for some mechanism to formalize the
team’s process and progress.
Decision Making: In the Beginning Is the Word
Te need to make a decision arises from some novelty in
the environment, either internal or external. We become
aware of some change or new information that requires a
response, and we must decide what that response will be.
For example, if we see a drop in customer demand, the
emergence of a competitor, or a problem in the supply
chain, in each case, something has changed; some new
information has emerged—good, bad, or indiferent—and
we must decide what action is the appropriate response.
Decision making, then, is a reaction to change. Its goal,
in the broadest sense, is innovation. Tis is the beginning
and the end of decision making. Information, observed
in the world, is the trigger. Innovation, materialized in
action, is the goal—and as shown in Figure 1, interaction
is the bridge between the two.
Information has always been the focal point for busi-
ness intelligence. In this, BI is correct. However, BI is
too restrictive, both in the scope of the information
considered and in confning itself to the data provisioning
and analysis that is only part of decision making. Te
actual information required comes from a wide variety
of sources. First is internal, hard (highly structured and
modeled) information, long recognized by BI. Second
is soft (loosely, variably structured) information such as
documents, images, and videos that exist both inside and
outside the organization. Further information—hard and
soft, such as spreadsheets and presentations—is generated
in the BI environment itself and is used by decision
makers as well. All of this we call formal information.
Such formal information in the decision-making environ-
ment must be, and generally is, managed to ensure
quality, reliability, and availability for decision makers.
As I’ve shown elsewhere (Devlin, 2009), such manage-
ment does not necessarily involve pushing it all through
a data warehouse. What is required is a comprehensive
environment for storing and managing all information
artifacts, optimized for their specifc structures and uses.
As we saw in the previous example, informal information
also exists—composed almost entirely of soft information
generated as part of the decision-making process, such as
phone calls, instant messages, and conference recordings.
Tis information is generally lost in the casual process
shown in Figure 1 because there is no single place to col-
lect and manage it. However, such informal information
is the key to understanding and managing interaction.
A new vision of Team Decisions: The iSight model
Figure 2 shows a new model—which we call the iSight
model—for innovative team-based decision making.
Comparing it with Figure 1, we see a number of changes.
First, the individuals in the team are defned in terms of
their primary role: investigation. Second, the activities
of the team are brought together and managed in a
functional block: the interaction. Tird, the information
resources—both formal and informal—of the team are
integrated in a single, logical store.
Personal iSight: Investigation
When an individual acts alone within a team decision
context, her primary role is to investigate information
and, in the process, gain personal insights into its
meaning, signifcance, and implications. Tere are four
constituent functions:
?
Integrate. Collecting formal information from the
business, the world at large, and other team informa-
tion, the user creates new and combined information
artifacts in the personal realm.
?
Interpret. Human interpretation supplies meaning and
judgment to information received. It often requires
detailed analysis and integration of further informa-
tion to provide a sufcient understanding of the
situation and possible causes. Interpretation is rational;
it is the typical analytical behavior that is the focus of
today’s BI and ofce productivity tools.
13 BUSINESS INTELLIGENCE Journal • vol. 17, no. 3
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?
Intuit. Internalized (or tacit) information and knowl-
edge become the basis upon which a user has fashes
of inspiration about what is really going on, possible
solutions, and so on. Tis intuitive spark, for which
minimal software support is possible, is driven by
personal intention and accelerated through
team interactions.
Interpretation keeps us in the rationalistic, cause-
and-efect world of Newtonian physics—the land of
the left brain. Novel thought arises in intuition, in
the realm of the right brain. It is also inspired by live
discussion between co-workers, which is part
of interaction.
?
Intend. Another highly personal and internal behavior
with minimal software support, the user is motivated
by business, team, and personal goals to gather and
analyze information, make conclusions in a particular
direction, and play a role in decision making.
Interpretation and intuition have no direction. For direc-
tion, we need to set intention. Intention drives behavior.
At its simplest and purest, intention drives the business
analyst in search of complete, relevant information. More
deeply, intention drives the interpretation in a particular
direction in order to prove or disprove hypotheses. More
deeply still, intention drives the decision-making process
toward a business goal such as maximizing proft or
reducing customer churn. More darkly, perhaps, intention
includes personal goals, which may be overt and aligned
to the business’s objectives—but may also be covert, even
illegal, and aimed primarily toward personal gain.
In terms of software support, integration and interpreta-
tion have had the most attention, with the continuing
evolution of BI tools and an almost exclusive concentra-
tion on numerical data. Intention and intuition are
hidden aspects of the personal sphere; they cannot easily
be supported by software tools. However, we can envisage
that software can develop to a stage where some support
is possible. For example, software could detect and record
intention through pattern analysis and data mining. Te
most obvious reason would be to track and prevent illegal
and inappropriate decisions. Another reason would be
to facilitate distinguishing between the intended and
collateral efects of decisions.
Team iSight: Interaction
As noted, the interaction function is the link between
the behaviors of individuals and the actions of the team
as a whole. Interaction is the engine that drives the team
together and toward the goal of making a decision.
INFORMATION
(External, formal)
INVESTIGATE
INTERACT
• Strategy
• Plan
• Process change
• Specifc action
• Form another team
• Etc.
TEAM
PERSON
TEAM
INFORMATION
(Formal and informal)
INNOVATE
Implement
Improvise
Imagine
Interpret Intend
Integrate Intuit
Figure 2. iSight model of team-based decision making.
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From a team view, interaction focuses on the ongoing
orchestration of the individual interactions of team
members and the information they use and create. It
means monitoring all team conversations and gatekeep-
ing all inbound and outbound information. For the
individual, to interact means to step up to the whiteboard,
grab a marker, and begin drawing boxes and arrows, all
while talking animatedly.
For the team environment, interaction is capturing the
drawings and the conversation, extracting meaning from
them, relating the content to other information artifacts,
and storing it all for future use. Tink team room
software … on steroids. Although not all conversation
is recorded digitally, we now have the technology to do
so. Of course, such recordings raise issues of privacy and
personal freedom. However, the more of the conversation
that is recorded, the greater the proportion of informal
information that can be captured, analyzed, and reused.
Humans are social animals, and business is a highly
social entity. Personal intention, intuition, and interpreta-
tion are enhanced through social interaction. Social
networking refnes our interpretations, expands our
intuition, and tests our intentions. Business decisions are
increasingly being made collectively, either openly and
democratically or covertly in an autocratic environment.
Business thrives when there is interaction among col-
leagues, contacts, and customers, and further serves as
the inspiration for or source of innovation.
In a world where businesses are increasingly international
and geographically dispersed and where people are
increasingly more comfortable conducting social
intercourse online, an ever larger set of information is
exchanged electronically, discussions are going digital,
and conclusions are reached in the ether.
More than ever, all of this information can be stored,
tracked, interpreted, and reused. We have reached a
tipping point where social networking and collaboration
tools can provide the framework for decision making
and enhance the process itself so that decision makers
can obtain real benefts. Tese benefts include savings in
time and expenses; reuse of prior experience and artifacts;
and, most important, improvements in the quality,
efectiveness, and tracking of decision making.
Tree further functions support interaction within the
team environment:
?
Implement. Tis is the process of creating a collabora-
tive team—people are added and removed, meetings
are arranged, and documents are shared. Implementa-
tion covers the drudgery we forget until we’re asked
to create a team to solve some problem and struggle
to gather phone numbers and e-mail addresses or
book meeting rooms. Without the mundane activities
of implementation, the magic of imagination and
improvisation never materialize.
?
Imagine. Imagining is the team-level counterpart of
personal intuition. In an efective team, ideas arise
as the team members interact. Conversation and
challenge inspire new thinking within individuals;
individuals feed their new thinking into the conversa-
tion, which in turn generates new ideas within the
team. Conceptual combination—the synthesis and
merging of previously separate, individual concepts—
gives rise to new ideas.
Brainstorming, group ideation, feedback, co-editing,
mind-mapping, whiteboarding, and creating decision
templates are among a wide variety of techniques used
in team imagining. Some have been developed for
physically co-located teams, where all or much of the
interaction is face to face. Some techniques have been
extended for electronic use or specifcally created for
virtual teams using Web 2.0 techniques to support
electronic communication.
?
Improvise. Although the drive in imagining is to
expand the set of possible ideas and solutions, impro-
visation looks more closely at what is actually possible
in the given situation while accounting for constraints
such as budget, physical and stafng limitations,
and competition. As shown in Figure 2, imagining,
improvisation, and implementation coexist in a tight
symbiotic loop; an efective team moves fuidly back
and forth among the three. Te discipline of improvi-
15 BUSINESS INTELLIGENCE Journal • vol. 17, no. 3
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sation balances the creative energy of imagination; the
team contracts or expands accordingly.
Consensus building, voting, and obtaining buy-in
from team members and external stakeholders are
all part of the social process of improvisation. Many
of the techniques of imagining—such as co-editing,
building decision templates, and mind-mapping—can
also be used for improvisation to aid in the develop-
ment and documentation of a solution that is likely to
gain wider support and lead to a politically acceptable
and implementable change in the business.
In the End Is the Word
Human interaction has, since time immemorial, been
face to face. Such communication is highly efective
and information rich. Te invention of writing more
than 5,000 years ago enabled interaction at a distance
and provided a permanent record of the information
conveyed. However, in comparison, writing is informa-
tion poor. Until recently, our choices in interaction
included proximate, rich information versus incomplete,
long-distance information; feeting, comprehensive
information versus permanent, incomplete information.
Electronic communication and digitization have dramati-
cally shifted the balance among information density,
proximity, and permanence. We see the possibility of
optimizing all three in the business environment—pro-
vided we store and manage the informal information
currently lost in traditional, poorly tooled team decision
making. Ungoverned or poorly managed information is
always in danger of being lost; therefore, in the formal,
team-based, decision-making approach, we must care-
fully manage such information. Te central role of the
interaction component—both within the team and as the
linkage point between individuals and the team—allows
it to moderate and manage all information input to and
output from the team information store.
Today’s social networking and team room tools support
the capture and management of all text-based interac-
tions and fle sharing between team members. However,
such interaction is slow and relatively information poor.
Voice messaging is also now common, with the potential
for storing both audio content and text captured via voice
recognition. Advances in processing power and band-
width could enable storage and use of visual interaction.
As we move toward this more interactive environment,
we can envision a more attractive environment for team
members. Participation and trust levels increase. Meet-
ings can be replayed to check what was actually said.
Decisions can be more easily reviewed and revisited to
understand how a particular outcome evolved and, if
necessary, take action to avoid future problems.
At the heart of every decision is a decision context consist-
ing of the people, their behaviors, and the information
used. In a highly predictable business environment, the
people and information involved in a particular decision
context are relatively stable; whenever a particular type of
decision is required, the same actors and information are
involved, creating a tacit information store for future use.
In modern, fast-moving businesses, multidisciplinary
teams of people are brought together on an ad hoc basis
to deal with specifc situations. In this case, only limited
tacit knowledge can be carried forward, so it becomes
mandatory to explicitly capture and store informal (as
well as formal) information. In fact, capturing and
storing the decision context are primary drivers for
interacting. It’s hardly novel now to simply facilitate
interaction between team members and it does little to
encourage usage; faithfully recording the decision context
provides the incentive for team members to participate
and enables the decision outcome to be reviewed for:
Decision making is a reaction
to change. Its goal, in the
broadest sense, is innovation.
This is the beginning and the
end of decision making.
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?
Coherence. Closing the loop on the decision. Did we
achieve the desired outcome? Were there unintended
consequences and could we have identifed them in
the decision-making process?
?
Reuse. Can the team knowledge be captured and
reused in whole or in part for future decisions? Can we
speed up similar decisions in the future? If so, how?
?
Participation. Were there team members who were
central to the decision? Were they overly infuential?
Were there under-contributing members? Were
personal agendas or motivations at play in the decision
and, if so, were they ethical and legal?
Early iSights: Collaboration, Content, and BI—In
That Order
Te focus of the iSight model is threefold. First, deep and
extensive interaction in a well-managed team environ-
ment drives innovative decision making. Second, as the
informal information from these interactions is stored
and managed, it creates a platform for in-depth decision-
making support. Tird, analyzing and interpreting this
informal information in an automated way creates tacit
knowledge and allows teams to reuse previous experience
in the future.
Tese three focus areas map directly to current software
domains:
?
Interaction. Social interaction and networks, especially
between peers, are at the heart of Web 2.0 tools
and techniques. Tey extend to Enterprise 2.0 and
collaborative software, which aim toward specifc
business goals. Tese tools are at the core of iSight
interaction.
?
Informal information. In its broadest sense, content
management stores and manages nontraditional, often
highly unstructured information. Applying these
tools and techniques to the informal information that
underlies decision making—and creates much of the
context for it—is an obvious step in the direction
described for iSight.
?
Interpretation. Business intelligence tooling has
traditionally been applied to the formal, especially
numerical, information of the business. Extending
the scope of such tools to informal information and
placing a greater emphasis on text analysis can provide
the basis for a more automated and reasoned approach
to the rational aspects of decision making.
Note that many BI tools have begun to implement “col-
laborative BI.” Tis concept is substantially more limited
than what we discuss here; it is not a true starting point
for iSight. Collaborative BI currently starts from a model
where individuals perform analysis and then share it and
work through the analysis with peers and managers. Te
collaboration and content management described in the
frst two points above start from an entirely diferent set
of information and with a very diferent goal: that of
supporting the process of decision making rather than
the individual decisions themselves. Furthermore, as we
examine the full scope of information used in decision
making, we can clearly see that the scope of the BI
team in most organizations is far too narrow to cover
everything we need.
Implementing the iSight model requires organizational
collaboration and the tools to support it. Although the BI
team’s skills and knowledge are important, the focus of
the iSight model is diferent from that of traditional BI.
As collaboration becomes widespread (thanks, in part, to
advances in technology), we can introduce the use (and
archiving) of informal information while heeding privacy,
control, and transparency requirements. We can also
apply our BI tools to the archived content of our decision-
making to enjoy the full beneft of this model.
Conclusions
Te way we make business decisions is at a turning point.
Traditional BI—collecting mostly numerical informa-
tion from inside our enterprise, verifying the data’s
quality, and distributing it to business users—has been
our sole focus. Big data—and business’s demand for
speed and innovation—are transforming how decisions
must be made.
17 BUSINESS INTELLIGENCE Journal • vol. 17, no. 3
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Added to this challenge, the arrival of the millennial
generation—with its daily experience of digital interac-
tion with peers and the world in general—in positions
of responsibility in business is prompting forward-
looking organizations to ask: Is there is a better way to
make decisions?
To reach innovative solutions, organizations must adopt
and embrace team-based decision making, and the broad
use of digital communication and information storage
will help us implement the iSight model we’ve discussed.
Enterprises can help team members innovate by storing,
managing, and using the information generated but not
captured as decisions are made, such as information from
meetings, conversations, e-mails, instant messages, and
phone calls. Tis informal information is the digital
record of all the inputs that contributed to the decision-
making process of gathering (formal) information,
discussing it, hypothesizing what it means, suggesting
and dismissing ideas, forming a consensus, and coming
to a conclusion.
Te iSight model shows the type of functionality
required to make sense of informal information and
illustrates how to use it to enhance the decision-making
process. As a result, we can now envision an entirely new
decision-making environment that supports all types
of decisions—operational, tactical, and strategic—that
require innovative, team-based thinking. Te iSight
model described here provides the frst comprehensive
framework in which team-based decision making can
be understood, designed into software solutions, and
implemented in highly innovative and forward-looking
organizations. ?
Acknowledgment
Te author wishes to thank Scott Davis, CEO of Lyzasoft,
Inc. Te concepts and ideas in this paper were developed
in collaboration with him on a recent white paper
(Devlin, 2012), where further details can be found.
references
Devlin, Barry [2009]. “Business Integrated Insight (BI
2
):
Reinventing enterprise information management,”
9sight Consulting, August.
www.9sight.com/wp-bi2.htm
Devlin, Barry [2012]. “iSight for Innovation:
Breakthrough collaboration for decision making,”
9sight Consulting, March.
www.9sight.com/wp-isight.htm
Gigerenzer, Gerd, Peter M. Todd, and ABC Research
Group [2000]. Simple Heuristics Tat Make Us Smart,
Oxford University Press USA.
McAfee, Andrew [2009]. Enterprise 2.0: New
Collaborative Tools for Your Organization’s Toughest
Challenges, Harvard Business School Press.
Power, D.J. [2007]. “A Brief History of Decision Support
Systems,” version 4.1, DSSResources.com, March.
www.dssresources.com/history/dsshistory.html
doc_119290461.pdf
Collaborative Business Intelligence Socializing Team-Based Decision Making
9 BUSINESS INTELLIGENCE Journal • vol. 17, no. 3
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Collaborative
Business Intelligence:
Socializing
Team-Based
Decision Making
Barry Devlin
Abstract
For many of us, making decisions is a challenge; for others, it
can be torture. Despite nearly half a century of work in deci-
sion support and business intelligence (BI), many businesses’
decisions look vaguely dysfunctional.
If we examine how most organizations really make important
and innovative decisions, we see that most are made by
teams (permanent or transitory) of people rather than by
individuals. It’s high time we designed an effective approach
to true decision-making support—what we might call
innovative team-based decision making.
This article presents a new model we call iSight that maps the
path from the information cues that signal change is required,
through the team interactions and implementation, and on to
measurable and repeatable innovation. The key to this pro-
gression lies in informal information—the conversations and
meetings, messages, and e-mails that record the actual path
of decision making but which are largely lost today. Capturing
and using this informal information is increasingly possible as
we move to a world where nearly all information is digital.
Our iSight model provides the frst comprehensive framework
in which team-based decision making can be understood,
designed into software solutions, and implemented in highly
innovative and forward-looking organizations.
Barry Devlin, Ph.D., is a founder of the data
warehousing industry and among the foremost
worldwide authorities on business intelligence
and the emerging feld of business insight. He is a
widely respected consultant, lecturer, and author of
the seminal book Data Warehouse: From Architecture
to Implementation. He is founder and principal of
9sight Consulting (www.9sight.com).
[email protected]
10 BUSINESS INTELLIGENCE Journal • vol. 17, no. 3
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Is Business Intelligence Working?
In the mid-1990s, Gartner analyst Howard Dresner
popularized the term business intelligence (BI), words that
suggest deep thought and extensive, rational decision
making. However, what we get from vendors and IT is
closer to what we used to call a decision support system
(DSS). Dan Power (2007) identifes fve classes of DSS,
within which BI fts mainly as a data-driven and, to a
lesser extent, model-driven DSS. Te actual focus of BI
tools is on the collection, analysis, and presentation of
largely numerical, mostly internal information to indi-
vidual decision makers. Te assumption is that having
provided enough “good” information, IT can stand back
and watch the business make “better” decisions.
Tis is only occasionally true, at best, as shown as far back
as 1999 (Gigerenzer et al, 2000). In recent years, much
of the focus has turned to operational BI and big data—
making ever-faster, smaller decisions based on ever-larger
data sets. Yes, the world of business is spinning ever faster
and daily decision making has to keep pace, but the type
of change we’re experiencing now is revolutionary. Te
social and economic fabric of our world is being torn
apart and remade in constant and repeated seismic events.
Sovereign debt ratings of formerly unassailable AAA
countries are being downgraded. Previously blue-chip
businesses in every industry have fallen as new kids on
the block charm Wall Street.
Decision making needs to be fast and it absolutely must
be innovative—in a diferent league from what we’ve
done before. Such decision making is a team efort,
especially for decisions that require or produce innovation.
Te truth is that such innovative decision making has
little to do with the explicit, largely numeric data we’ve
focused on for over 20 years.
Tose of us who have worked in large enterprises have
seen sufcient evidence to conclude that many decisions
have a rather shaky relationship with facts and business
intelligence, and limited relevance even to stated business
goals. How many successful decisions have been declared
as based on “gut feel” and unsuccessful ones blamed on
“lack of reliable information”? How often have we seen
political expedience override a strongly argued rationale?
Ten there’s the directive to “just take one more look
at the fgures” when the numbers contradict the group
wisdom of the boardroom.
What are we missing? Our longtime focus on BI is blinding
us to the fact that the most efective and productive path
from information to innovation is through interaction. A
few ideas do pop into our heads out of nowhere, but
most of our best ideas—useful, productive ideas that can
be implemented—are born from interaction with peers,
colleagues, and even managers.
Te human mind is ultimately a social construct, which
leads us directly to social networking and Web 2.0, which
represents the evolving democratization of the Internet.
Creativity has been open sourced. Centralized control has
given way to geographically separated cooperation. Social
media (such as Twitter and Facebook) allow people to
openly share their observations and opinions and expose
themselves to feedback. Te Web has sparked our innova-
tion, helped us cooperate, and put the focus on teams.
Unleashing such innovation and collaboration within
the business environment is not only desirable; it’s
mandatory. Te generation now entering the workforce
expects nothing less. In the corporate world, however, we
need more structure and control. Enter Enterprise 2.0
(McAfee, 2009)—the business favor of Web 2.0. For
decision making, Enterprise 2.0 opens up enormous
opportunities for interaction. It promises to unleash the
creativity of decision-making teams who gain access to
external information and shared insight. It ofers real-
time collaboration with peers and superiors that can drive
innovation and accelerate decision cycles. Furthermore,
it presents new opportunities for the business to directly
harvest and beneft from the wisdom and experience
of “edge workers”—those who work intimately with
customers, prospects, products, and partners. Tis is true
decision-making support.
Innovative Team-Based Decision Making
A decision-making team is simply a group of people who
have come together to address a business challenge. Team
members may come from a single or multiple units in the
organization; the team may be brand new or its members
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may have worked together on previous assignments. Te
team may be based in a single location or be virtual.
Besides the people on the team, we note that in order to
function, the team needs (1) information artifacts that
are used, shared, and created by the members and (2) a
web of interactions between the members. Tis ecosystem
is shown in Figure 1. It’s fairly simple and informal. It’s
the way it works today, and it’s also the way it doesn’t
quite work—we lose so much of importance that goes on
within the work of the team, such as:
?
Context. Te business environment and background
to the decision, the team members involved, and
initiating and closing actions
?
Interactions. All informal communication among
team members and with external parties, including
meetings (face-to-face and electronic), telephone calls,
instant messages, tweets, and even e-mail, if not
stored centrally
?
History. Te performance of team members, the
unfolding of thought processes leading to options
considered and discarded, the timing of events and
when information was requested/received, and a
formal record of how innovation occurred
?
Consequences. Closing the loop between expectations
set in the decision and what actually happened in the
real world
A typical example is a team brought together to
investigate and plan the CEO’s vision of a new process.
Its members come from across the business and from
IT, bringing their skills and knowledge of process and
information needs, approaches, and tools. After the CEO
briefs the team, members begin to gather documentation
on their PCs or even in a content store or team room tool.
As the project progresses, the team interacts with one
another, using and creating further information.
When a new team member comes on board, the only
information about what has occurred so far is what
exists in the team’s formal documents. Knowledge about
previously discarded options exists only in the heads of
the original team members, and the new team member
wastes time and energy exploring invalid options. Eventu-
ally, the team concludes on a new strategy and plan for
the process, and presents it to the CEO, who is only
partially satisfed. Some information had been lost—the
only record of the CEO’s briefng is in the participants’
handwritten notes, which are inconsistent and incom-
plete. Te team returns to work suitably chastened.
INFORMATION
(External, formal)
INTERACT
• Strategy
• Plan
• Process change
• Specifc action
• Form another team
• Etc.
Information input
and use
Shared information
Interaction
Person
Team
INNOVATE
Figure 1. The current decision-making ecosystem.
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Te lost information and undocumented work points
clearly to a need for some mechanism to formalize the
team’s process and progress.
Decision Making: In the Beginning Is the Word
Te need to make a decision arises from some novelty in
the environment, either internal or external. We become
aware of some change or new information that requires a
response, and we must decide what that response will be.
For example, if we see a drop in customer demand, the
emergence of a competitor, or a problem in the supply
chain, in each case, something has changed; some new
information has emerged—good, bad, or indiferent—and
we must decide what action is the appropriate response.
Decision making, then, is a reaction to change. Its goal,
in the broadest sense, is innovation. Tis is the beginning
and the end of decision making. Information, observed
in the world, is the trigger. Innovation, materialized in
action, is the goal—and as shown in Figure 1, interaction
is the bridge between the two.
Information has always been the focal point for busi-
ness intelligence. In this, BI is correct. However, BI is
too restrictive, both in the scope of the information
considered and in confning itself to the data provisioning
and analysis that is only part of decision making. Te
actual information required comes from a wide variety
of sources. First is internal, hard (highly structured and
modeled) information, long recognized by BI. Second
is soft (loosely, variably structured) information such as
documents, images, and videos that exist both inside and
outside the organization. Further information—hard and
soft, such as spreadsheets and presentations—is generated
in the BI environment itself and is used by decision
makers as well. All of this we call formal information.
Such formal information in the decision-making environ-
ment must be, and generally is, managed to ensure
quality, reliability, and availability for decision makers.
As I’ve shown elsewhere (Devlin, 2009), such manage-
ment does not necessarily involve pushing it all through
a data warehouse. What is required is a comprehensive
environment for storing and managing all information
artifacts, optimized for their specifc structures and uses.
As we saw in the previous example, informal information
also exists—composed almost entirely of soft information
generated as part of the decision-making process, such as
phone calls, instant messages, and conference recordings.
Tis information is generally lost in the casual process
shown in Figure 1 because there is no single place to col-
lect and manage it. However, such informal information
is the key to understanding and managing interaction.
A new vision of Team Decisions: The iSight model
Figure 2 shows a new model—which we call the iSight
model—for innovative team-based decision making.
Comparing it with Figure 1, we see a number of changes.
First, the individuals in the team are defned in terms of
their primary role: investigation. Second, the activities
of the team are brought together and managed in a
functional block: the interaction. Tird, the information
resources—both formal and informal—of the team are
integrated in a single, logical store.
Personal iSight: Investigation
When an individual acts alone within a team decision
context, her primary role is to investigate information
and, in the process, gain personal insights into its
meaning, signifcance, and implications. Tere are four
constituent functions:
?
Integrate. Collecting formal information from the
business, the world at large, and other team informa-
tion, the user creates new and combined information
artifacts in the personal realm.
?
Interpret. Human interpretation supplies meaning and
judgment to information received. It often requires
detailed analysis and integration of further informa-
tion to provide a sufcient understanding of the
situation and possible causes. Interpretation is rational;
it is the typical analytical behavior that is the focus of
today’s BI and ofce productivity tools.
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?
Intuit. Internalized (or tacit) information and knowl-
edge become the basis upon which a user has fashes
of inspiration about what is really going on, possible
solutions, and so on. Tis intuitive spark, for which
minimal software support is possible, is driven by
personal intention and accelerated through
team interactions.
Interpretation keeps us in the rationalistic, cause-
and-efect world of Newtonian physics—the land of
the left brain. Novel thought arises in intuition, in
the realm of the right brain. It is also inspired by live
discussion between co-workers, which is part
of interaction.
?
Intend. Another highly personal and internal behavior
with minimal software support, the user is motivated
by business, team, and personal goals to gather and
analyze information, make conclusions in a particular
direction, and play a role in decision making.
Interpretation and intuition have no direction. For direc-
tion, we need to set intention. Intention drives behavior.
At its simplest and purest, intention drives the business
analyst in search of complete, relevant information. More
deeply, intention drives the interpretation in a particular
direction in order to prove or disprove hypotheses. More
deeply still, intention drives the decision-making process
toward a business goal such as maximizing proft or
reducing customer churn. More darkly, perhaps, intention
includes personal goals, which may be overt and aligned
to the business’s objectives—but may also be covert, even
illegal, and aimed primarily toward personal gain.
In terms of software support, integration and interpreta-
tion have had the most attention, with the continuing
evolution of BI tools and an almost exclusive concentra-
tion on numerical data. Intention and intuition are
hidden aspects of the personal sphere; they cannot easily
be supported by software tools. However, we can envisage
that software can develop to a stage where some support
is possible. For example, software could detect and record
intention through pattern analysis and data mining. Te
most obvious reason would be to track and prevent illegal
and inappropriate decisions. Another reason would be
to facilitate distinguishing between the intended and
collateral efects of decisions.
Team iSight: Interaction
As noted, the interaction function is the link between
the behaviors of individuals and the actions of the team
as a whole. Interaction is the engine that drives the team
together and toward the goal of making a decision.
INFORMATION
(External, formal)
INVESTIGATE
INTERACT
• Strategy
• Plan
• Process change
• Specifc action
• Form another team
• Etc.
TEAM
PERSON
TEAM
INFORMATION
(Formal and informal)
INNOVATE
Implement
Improvise
Imagine
Interpret Intend
Integrate Intuit
Figure 2. iSight model of team-based decision making.
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From a team view, interaction focuses on the ongoing
orchestration of the individual interactions of team
members and the information they use and create. It
means monitoring all team conversations and gatekeep-
ing all inbound and outbound information. For the
individual, to interact means to step up to the whiteboard,
grab a marker, and begin drawing boxes and arrows, all
while talking animatedly.
For the team environment, interaction is capturing the
drawings and the conversation, extracting meaning from
them, relating the content to other information artifacts,
and storing it all for future use. Tink team room
software … on steroids. Although not all conversation
is recorded digitally, we now have the technology to do
so. Of course, such recordings raise issues of privacy and
personal freedom. However, the more of the conversation
that is recorded, the greater the proportion of informal
information that can be captured, analyzed, and reused.
Humans are social animals, and business is a highly
social entity. Personal intention, intuition, and interpreta-
tion are enhanced through social interaction. Social
networking refnes our interpretations, expands our
intuition, and tests our intentions. Business decisions are
increasingly being made collectively, either openly and
democratically or covertly in an autocratic environment.
Business thrives when there is interaction among col-
leagues, contacts, and customers, and further serves as
the inspiration for or source of innovation.
In a world where businesses are increasingly international
and geographically dispersed and where people are
increasingly more comfortable conducting social
intercourse online, an ever larger set of information is
exchanged electronically, discussions are going digital,
and conclusions are reached in the ether.
More than ever, all of this information can be stored,
tracked, interpreted, and reused. We have reached a
tipping point where social networking and collaboration
tools can provide the framework for decision making
and enhance the process itself so that decision makers
can obtain real benefts. Tese benefts include savings in
time and expenses; reuse of prior experience and artifacts;
and, most important, improvements in the quality,
efectiveness, and tracking of decision making.
Tree further functions support interaction within the
team environment:
?
Implement. Tis is the process of creating a collabora-
tive team—people are added and removed, meetings
are arranged, and documents are shared. Implementa-
tion covers the drudgery we forget until we’re asked
to create a team to solve some problem and struggle
to gather phone numbers and e-mail addresses or
book meeting rooms. Without the mundane activities
of implementation, the magic of imagination and
improvisation never materialize.
?
Imagine. Imagining is the team-level counterpart of
personal intuition. In an efective team, ideas arise
as the team members interact. Conversation and
challenge inspire new thinking within individuals;
individuals feed their new thinking into the conversa-
tion, which in turn generates new ideas within the
team. Conceptual combination—the synthesis and
merging of previously separate, individual concepts—
gives rise to new ideas.
Brainstorming, group ideation, feedback, co-editing,
mind-mapping, whiteboarding, and creating decision
templates are among a wide variety of techniques used
in team imagining. Some have been developed for
physically co-located teams, where all or much of the
interaction is face to face. Some techniques have been
extended for electronic use or specifcally created for
virtual teams using Web 2.0 techniques to support
electronic communication.
?
Improvise. Although the drive in imagining is to
expand the set of possible ideas and solutions, impro-
visation looks more closely at what is actually possible
in the given situation while accounting for constraints
such as budget, physical and stafng limitations,
and competition. As shown in Figure 2, imagining,
improvisation, and implementation coexist in a tight
symbiotic loop; an efective team moves fuidly back
and forth among the three. Te discipline of improvi-
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sation balances the creative energy of imagination; the
team contracts or expands accordingly.
Consensus building, voting, and obtaining buy-in
from team members and external stakeholders are
all part of the social process of improvisation. Many
of the techniques of imagining—such as co-editing,
building decision templates, and mind-mapping—can
also be used for improvisation to aid in the develop-
ment and documentation of a solution that is likely to
gain wider support and lead to a politically acceptable
and implementable change in the business.
In the End Is the Word
Human interaction has, since time immemorial, been
face to face. Such communication is highly efective
and information rich. Te invention of writing more
than 5,000 years ago enabled interaction at a distance
and provided a permanent record of the information
conveyed. However, in comparison, writing is informa-
tion poor. Until recently, our choices in interaction
included proximate, rich information versus incomplete,
long-distance information; feeting, comprehensive
information versus permanent, incomplete information.
Electronic communication and digitization have dramati-
cally shifted the balance among information density,
proximity, and permanence. We see the possibility of
optimizing all three in the business environment—pro-
vided we store and manage the informal information
currently lost in traditional, poorly tooled team decision
making. Ungoverned or poorly managed information is
always in danger of being lost; therefore, in the formal,
team-based, decision-making approach, we must care-
fully manage such information. Te central role of the
interaction component—both within the team and as the
linkage point between individuals and the team—allows
it to moderate and manage all information input to and
output from the team information store.
Today’s social networking and team room tools support
the capture and management of all text-based interac-
tions and fle sharing between team members. However,
such interaction is slow and relatively information poor.
Voice messaging is also now common, with the potential
for storing both audio content and text captured via voice
recognition. Advances in processing power and band-
width could enable storage and use of visual interaction.
As we move toward this more interactive environment,
we can envision a more attractive environment for team
members. Participation and trust levels increase. Meet-
ings can be replayed to check what was actually said.
Decisions can be more easily reviewed and revisited to
understand how a particular outcome evolved and, if
necessary, take action to avoid future problems.
At the heart of every decision is a decision context consist-
ing of the people, their behaviors, and the information
used. In a highly predictable business environment, the
people and information involved in a particular decision
context are relatively stable; whenever a particular type of
decision is required, the same actors and information are
involved, creating a tacit information store for future use.
In modern, fast-moving businesses, multidisciplinary
teams of people are brought together on an ad hoc basis
to deal with specifc situations. In this case, only limited
tacit knowledge can be carried forward, so it becomes
mandatory to explicitly capture and store informal (as
well as formal) information. In fact, capturing and
storing the decision context are primary drivers for
interacting. It’s hardly novel now to simply facilitate
interaction between team members and it does little to
encourage usage; faithfully recording the decision context
provides the incentive for team members to participate
and enables the decision outcome to be reviewed for:
Decision making is a reaction
to change. Its goal, in the
broadest sense, is innovation.
This is the beginning and the
end of decision making.
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?
Coherence. Closing the loop on the decision. Did we
achieve the desired outcome? Were there unintended
consequences and could we have identifed them in
the decision-making process?
?
Reuse. Can the team knowledge be captured and
reused in whole or in part for future decisions? Can we
speed up similar decisions in the future? If so, how?
?
Participation. Were there team members who were
central to the decision? Were they overly infuential?
Were there under-contributing members? Were
personal agendas or motivations at play in the decision
and, if so, were they ethical and legal?
Early iSights: Collaboration, Content, and BI—In
That Order
Te focus of the iSight model is threefold. First, deep and
extensive interaction in a well-managed team environ-
ment drives innovative decision making. Second, as the
informal information from these interactions is stored
and managed, it creates a platform for in-depth decision-
making support. Tird, analyzing and interpreting this
informal information in an automated way creates tacit
knowledge and allows teams to reuse previous experience
in the future.
Tese three focus areas map directly to current software
domains:
?
Interaction. Social interaction and networks, especially
between peers, are at the heart of Web 2.0 tools
and techniques. Tey extend to Enterprise 2.0 and
collaborative software, which aim toward specifc
business goals. Tese tools are at the core of iSight
interaction.
?
Informal information. In its broadest sense, content
management stores and manages nontraditional, often
highly unstructured information. Applying these
tools and techniques to the informal information that
underlies decision making—and creates much of the
context for it—is an obvious step in the direction
described for iSight.
?
Interpretation. Business intelligence tooling has
traditionally been applied to the formal, especially
numerical, information of the business. Extending
the scope of such tools to informal information and
placing a greater emphasis on text analysis can provide
the basis for a more automated and reasoned approach
to the rational aspects of decision making.
Note that many BI tools have begun to implement “col-
laborative BI.” Tis concept is substantially more limited
than what we discuss here; it is not a true starting point
for iSight. Collaborative BI currently starts from a model
where individuals perform analysis and then share it and
work through the analysis with peers and managers. Te
collaboration and content management described in the
frst two points above start from an entirely diferent set
of information and with a very diferent goal: that of
supporting the process of decision making rather than
the individual decisions themselves. Furthermore, as we
examine the full scope of information used in decision
making, we can clearly see that the scope of the BI
team in most organizations is far too narrow to cover
everything we need.
Implementing the iSight model requires organizational
collaboration and the tools to support it. Although the BI
team’s skills and knowledge are important, the focus of
the iSight model is diferent from that of traditional BI.
As collaboration becomes widespread (thanks, in part, to
advances in technology), we can introduce the use (and
archiving) of informal information while heeding privacy,
control, and transparency requirements. We can also
apply our BI tools to the archived content of our decision-
making to enjoy the full beneft of this model.
Conclusions
Te way we make business decisions is at a turning point.
Traditional BI—collecting mostly numerical informa-
tion from inside our enterprise, verifying the data’s
quality, and distributing it to business users—has been
our sole focus. Big data—and business’s demand for
speed and innovation—are transforming how decisions
must be made.
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Added to this challenge, the arrival of the millennial
generation—with its daily experience of digital interac-
tion with peers and the world in general—in positions
of responsibility in business is prompting forward-
looking organizations to ask: Is there is a better way to
make decisions?
To reach innovative solutions, organizations must adopt
and embrace team-based decision making, and the broad
use of digital communication and information storage
will help us implement the iSight model we’ve discussed.
Enterprises can help team members innovate by storing,
managing, and using the information generated but not
captured as decisions are made, such as information from
meetings, conversations, e-mails, instant messages, and
phone calls. Tis informal information is the digital
record of all the inputs that contributed to the decision-
making process of gathering (formal) information,
discussing it, hypothesizing what it means, suggesting
and dismissing ideas, forming a consensus, and coming
to a conclusion.
Te iSight model shows the type of functionality
required to make sense of informal information and
illustrates how to use it to enhance the decision-making
process. As a result, we can now envision an entirely new
decision-making environment that supports all types
of decisions—operational, tactical, and strategic—that
require innovative, team-based thinking. Te iSight
model described here provides the frst comprehensive
framework in which team-based decision making can
be understood, designed into software solutions, and
implemented in highly innovative and forward-looking
organizations. ?
Acknowledgment
Te author wishes to thank Scott Davis, CEO of Lyzasoft,
Inc. Te concepts and ideas in this paper were developed
in collaboration with him on a recent white paper
(Devlin, 2012), where further details can be found.
references
Devlin, Barry [2009]. “Business Integrated Insight (BI
2
):
Reinventing enterprise information management,”
9sight Consulting, August.
www.9sight.com/wp-bi2.htm
Devlin, Barry [2012]. “iSight for Innovation:
Breakthrough collaboration for decision making,”
9sight Consulting, March.
www.9sight.com/wp-isight.htm
Gigerenzer, Gerd, Peter M. Todd, and ABC Research
Group [2000]. Simple Heuristics Tat Make Us Smart,
Oxford University Press USA.
McAfee, Andrew [2009]. Enterprise 2.0: New
Collaborative Tools for Your Organization’s Toughest
Challenges, Harvard Business School Press.
Power, D.J. [2007]. “A Brief History of Decision Support
Systems,” version 4.1, DSSResources.com, March.
www.dssresources.com/history/dsshistory.html
doc_119290461.pdf