Establishing early warning systems in an enterprise enables it to respond to opportunistic or systemic disruptions by modifying the business model accordingly. We analyze the forms of direct and indirect data and knowledge that inform such early warning systems.

1. Triggering Business Model Innovation
Programs Through Early Warning Systems
Inputs must be in place so that disruptions of the status quo — whether
opportunistic or systemic — will set off early warning signals that ensure
an organization modifies its business models.
Executive Summary
The business world is moving ever faster thanks
to the pervasive, free communication infrastruc-
ture that ensures that good and bad news cir-
cumnavigates the globe much more rapidly than
ever before. Business model innovation programs
have become popular because the rate at which
business models change has accelerated due to
the pervasive free communication network. Early
warning systems are devised to identify when
business conditions fail to fit into the status quo
used to devise business models.
From the perspective of an organization that has
not determined the root causes, impacts, action
plans and systemic changes to business models
that require alterations, these disruptive events
can be catastrophic to the enterprise, perhaps
by allowing a major consumer to be won over
by another market participant. With a disruptive
technology that either benefits consumers
through new capabilities or services that radically
alter the cost structure used to price goods or
services, the speed at which these disruptions
can be dispensed with are likely to greatly impact
organizational health.
The ability to wield information critical to
monitoring changes from the status quo originat-
ing within your organization’s operations (often
operational risk mitigating information) and infor-
mation obtained from traditional news channels
and less traditional information channels,
including social media, is critical to the making of
an effective early warning system. Equally critical
to monitoring changes is the ability to:
• Gain insight into what has changed.
• Establish analytical models that can identify
the potential impact of changes and the impact
those changes have on the organizational
business models.
• Sufficiently trust the information to accept the
call to action based on what has changed.
• Synthesize and communicate action plans
to either take protective action or pounce on
market opportunities.
• Orchestrate the appropriate action.
• Monitor the effects of any executed action and
fine-tune the action plans based on market
forces resulting from any executed actions.
This paper will illustrate how companies can
benefit from thwarting such disruptions that are
becoming quite commonplace by arming their
processes that govern business model innova-
tions with input from an early warning system.
• Cognizant 20-20 Insights
cognizant 20-20 insights | august 2013

2. 2
Just What Is Business
Model Innovation?
Data has become the great enabler thanks to
the quasi-free communications infrastructure
that ensures that the news, be it good or bad,
cannot be kept a secret for very long. Data
comes from a variety of sources, most of which
are not within your own organization. Business
model innovation is the discipline of innovating
new business plans to align with the changes
in the marketplace and to participate with new
products, services, customers and markets. The
theory behind business model innovation is that
the business model currently achieving value for
the organization, which historically would have
an effective life of three to five years, now has
a much shorter effective lifespan and requires
constant enhancing to continue to yield high
incremental values to the enterprise. Business
model innovation is a practice of converting inno-
vations to incremental value for the enterprise.
What Do Early Warning Systems Have
to Do with Business Model Innovation?
Early warning systems are a class of systems that
provide foresight to changes in the status quo,
which can be categorized as either:
• Opportunistic disruptions (e.g., a competitor
is out of stock on a particular raw material
which opens an opportunity for additional
sales), or
• Systemic disruptions (e.g., a new means of
manufacturing is innovated which drives prices
down by 25% with no discernible change in
quality for a competitor).
Opportunistic disruptions are ones which can be
acted upon, and they may result in a systemic
disruption (e.g., a competitor’s customer who
is provided goods due to the competitor’s out-
of-stock condition becomes a repeat customer)
while systemic disruptions require a change to
core business model components to be thwarted
(e.g., the entire manufacturing process requires
changes to drive manufacturing costs down by at
least 25% to remain in a competitive position).
Business model innovation factories require the
early warning system as an input to distinguish
which disruptions to the status quo are candidates
for modifications to the core business models.
The Alignment of Data Required
for Business Model Innovation and
Early Warnings
An effective early warning system requires:
• Data.
• A robust base of historical data to determine
what elements lie outside the status quo.
• Contextual information collected from a variety
of sources.
• A means to identify the alternative actions to
fit the disruption (in the case above, an oppor-
tunity to capture an additional 10% annually of
product sales).
• A means to select the best of the potential
alternative actions.
• A means to disseminate the chosen alternative
action.
• A means to collaborate so that coordinat-
ed activities drive interested parties to the
selected action.
• A feedback mechanism to measure the impact
and potentially fine-tune any actions taken to
ensure the intended outcomes are achieved.
Early warnings provide the ability to react to
disruptions earlier than nonautomated means
would allow. However, there are hurdles to disrup-
tions, which must be factored into the prioritiza-
tion process. According to the Harvard Business
Review,1
a disruption that would require a change
in momentum, or the status quo, is such a hurdle.
These disruption barriers are described quite well
in The Tipping Point2
and include the following:
• A disruption that would require investment in
and implementation of new technology, as with
Apple’s product and service ecosystem (iPods,
iPhones, iPads and app stores).
• A disruption that would require a change in the
business environment.
• A disruption that would require adoption of
new innovations.
• A disruption that would require nothing less
than changing business models.
This paper will assess the composition of an
early warning system and discuss each of the
components described above.
cognizant 20-20 insights

3. “When Going Viral Social Media Marketing Can
Be Bad for Business”3
describes situations where
viral messaging caused unintended consequenc-
es and put the brand benefit at risk. However,
such coverage does not necessarily need to be
part of a viral media campaign, as good and bad
news can go viral, intended or not, and not nec-
essarily sourced by your organization. As stated
in the viral marketing article, “if your team is not
ready and able to handle it well, prepare for the
backlash.”
There is a barrier to a disruption having a sustain-
able impact on the marketplace, as illustrated in
Figure 1. For example, you can expect customers
and consumers to reach a tipping point much
more often than a new innovation that revolution-
izes the business, yet there is a much higher level
of effort — meaning the disruption must be more
significant if it requires a high level of effort to
yield benefit. This disruption barrier is a key mea-
surement in early warnings and business model
innovation initiatives.
Defining an Early Warning
An early warning is a business event that signifies
that something has changed in the status quo.
This event will most likely show up in your organi-
zation’s transactional systems when:
• It has either positively or negatively affected
your bottom line, or
• It was processed in your internal processing
cycle that records the positive and negative
impact of business events, which is normally a
monthly process.
An early warning will most probably not be
sourced in your internal systems used to record
and measure operational performance. We
will discuss the sources of data used for early
warnings later in this document.
Early warnings can be opportunities and risks
to the organization. Just as teams within the
enterprise are corralled to ensure the dispensa-
tion of risks and ensure that risks do not “appear
on the front page of The Wall Street Journal,”
an equal set of effort should be made to ensure
that opportunities that present themselves in
the marketplace are identified and acted upon
to positively impact the value realized by the
enterprise.
Early warnings are often detectors for disrup-
tions in the marketplace or are, according to the
Harvard Business Review,4
in reality “a story of
rational responses to a changing environment.”
These disruptions are often “the rational retreat
from a … marketplace participant that plagues
managers of successful firms, tempted to make
all the right decisions right up …to the point when
… they are flung off a cliff.“
Early warnings will fall into one of eight
categories. Generally, the more catastrophic an
early warning is, the faster news about the under-
pinnings to the early warning will circumnavigate
the globe, which therefore reduces the available
early warning reaction time. Equally important,
the more catastrophic an early warning is, the
greater the impact to the enterprise are the
resultant risks and rewards. For example, if an
3cognizant 20-20 insights
Figure 1
Potential Responses to Disruptions
Expected number of disruptions
Trigger a tipping point
Invest in and implement enabling technologies
Adopt a business environment change
Adopt new innovations
Adopt a new business model
Levelofeffortrequiredtodisrupt
(disruptionbarrier)

4. cognizant 20-20 insights 4
SEC investigation is looming, the criticality of
this information is fairly high, and, therefore,
the reaction time to identify the early warning,
synthesize an action plan and execute on that
plan is fairly short. Such news can be accelerated
considerably if it gets “sticky,” a term for those
capable of spotlighting the news to share it with
a larger audience at an accelerated pace. If the
news gets extremely sticky, it is considered viral.
In this highly communicative global marketplace,
anyone can make information sticky or viral using
publicly-available social media tools.
As shown in Figure 2, there are multiple
dimensions to events captured as early warnings.
Early warnings can be event-driven, such as a
supplier being out of stock for a critical inventory
component, or process-driven, meaning the
process used by a supplier doesn’t consistently
capture potential out-of-stock situations. Early
warnings also have a magnitude, measured by the
potential incremental value that can be lost or won
due to the appropriate action taken in response to
it. And lastly, an early warning can be externally
sourced (such as a competitor that cherry-picks
top clients) or internally sourced (such as a key
supplier’s plant going off-line with no contingency
plans in place to satisfy orders for products using
key ingredients supplied from that plant).
A recent article that appeared in Social Media
Today illustrated how poor social media publicity
can be amplified in its potential damage. In the
article a single hack of the Associated Press’s
Twitter account led to a single tweet that had
tremendous impact, although short-lived, on the
U.S. capital markets.5
Information Silos and Early Warnings
Among the challenges to detecting and collating
early warnings through the organization is
overcoming information silos (i.e., information
that is for the purpose of early warnings which is
departmentally stored and accessible to a limited
audience). Most frequently, such information is
locked up in desktop software (i.e., Access, Excel,
etc.) or a legacy application with limited distribu-
tion (i.e., Foxpro, DB2, etc.).
As stated by ProjectVision,6
information has
become a commodity; it is now inexpen-
sive, abundant and nearly impossible to hide.
“Companies that started or rode the wave of
this new paradigm have had a great start; all
companies at some point or another will confront
information silos as they grow or try to adapt to
the new paradigm of information management.”
In the process of translating an early warning
to an executable action, there is a process to
finding the data, then converting the data into
usable information and finally deriving actionable
strategies that represent an appropriate action in
response to the early warning.
ProjectVision has devised a ratio which can be
used to measure the time required to convert
data to information, stated as follows:
Information Velocity (V) =
[The time spent digesting data (D)
+
The time spent converting data into information (I)
The time spent finding the data (R).
Organizations that have a high information
Figure 2
Early Warning Events and Their Disruptions to the Status Quo
Minor
Moderate
High
Catastrophic
Opportunities:
Capture or Extend
Incremental Value.
Exposed Operational Risks:
Protect existing value from
being captured or reduced.
Status Quo
(no change in
captured, created,
extended or protected
value due to exposed
opportunities or risks)
Transitive: Event driven EWS Reaction Time Systemic: Process driven

5. cognizant 20-20 insights 5
velocity (V) typically find themselves with more
time to make decisions than their competi-
tion, and often will have more information to
work with as well. The early warning system, if
not hampered by information silos, will directly
impact the velocity of information.7
Early Warning Detection, Dispensation
While minor events and processes will have
minimal impact on the bottom line and have sig-
nificant timeframes in which a response to the
events can be derived and executed, major or
catastrophic events and processes do not follow
the same timeline. This is because as news cir-
cumnavigates the globe, catastrophic and major
events and processes that can be captured by
early warnings will be enticing to all who know of
the event or take appropriate action in the hopes
of benefiting from its occurrence.
As the communications infrastructure becomes
more efficient, the ability to use manual and other
antiquated means to detect disruptions, derive
appropriate actions, collaborate on those planned
actions and execute actions is highly reduced. A
level of automation to accelerate the timeframe
from detection to execution is mandatory
in today’s highly communicative and global
marketplace.
Much attention is given to the topic of business
model innovation, or changing the business
model to accelerate the collaborative use of infor-
mation to positively change the value stream
of the organization. The early warning system
here represents nothing less than a vehicle to
automate the information
assets used in a business
model innovation initiative.
A recent article in Forbes8
noted the interesting ongoing
shift where a “handful of
companies are recognizing
that they can create massive
growth by combining difficult
to replicate assets with entre-
preneurial behaviors…which
is heralding in a new era of innovation.” This
combination requires the ability to synthesize
the combination of information required to foster
this combination, which requires an information
factory as defined in early warning systems.
Early Warning System
Data Requirements
An early warning system will consume data from
a variety of sources:
• Indirect sources, such as Web sites maintained
by partnering organizations, distributors and
retailer reports, advertisers, regulators and
watchdogs.
• Direct sources, such as applications, logs and
other information from Web sites, logs and
other information from mobile facilities and
logs and other information from call centers
that your organization originates.
The early warning
system here represents
nothing less than a
vehicle to automate the
information assets used
in a business model
innovation initiative.
Figure 3
The Relationship Among Key Enterprise Components
Customers
Strategies
CapabilitiesCapabilities
Customers
Strategies
• Consumer Interfaces
• Financial
• Value Chain
• Brand
• Products & Services
• Lifetime Relationship
• Consumerism
• Markets
• Technologies
• Innovations
• Logistics
• Financing

6. cognizant 20-20 insights 6
• Externally sourced information, such as
that collected from RFID tags, mobile devices,
social media, modifications made on tracked
Web sites, traditional and nontraditional news
media, public relations bulletins, acquired intel-
ligence, affiliate referrals and other sources.
Each of these sources may be superfluous infor-
mation or may signal a change to the status
quo, which will require a swift determination
on whether action is warranted and then, if
warranted, execution of the appropriate coor-
dinated action to protect, extract, originate or
capture value for the organization (see Figure 4).
The sources of information can be:
• Logs: Formatted activity logs made available
from a call center, Web site, RFID, mobile device
or other facility that records its activity into
a log.
• Messages: A message received from an
internal or external source, such as a SWIFT
message, an EDI transaction or other message-
based sources.
• Streams: An asynchronous source of infor-
mation that streams information to a specific
listening target, such as Twitter, market tickers,
newswires and other sources.
• Documents: Any form of object that is
available as an information source, such as an
XML file, a public relations briefing, a weather
map, a schematic or other non-tabular infor-
mation source.
• Tabular sources: Information normally housed
in databases and originated from sources
internal to an organization (internal ERP
systems, spreadsheets, CRM systems, etc.)
or external (such as vendors, customers and
industry associations).
Figure 4
Information Sources for Early Warning Systems
Information
Sources
Delivery Vehicle
Indirect Source Formats
Customer
Partner Web
Distributor Reports
Consumer
Retailer Reports
Advertisers
Partner
Regulators and Watchdogs
Direct
Regulator Internal Sources
Websites
Transactions
Mobile Facilities
Call Centers
Interactions
Externally Obtained
Mobile Facilitated
Intelligence
RFID
Social Media
Traditional Media News and PR Media
Inforamtion Merchant Acquired Intelligence
Information
Intermediary
Affiliate Referrals
Other
Legend: n Log n Message n Stream n Document n Tabular/Internal n Tabular/External

7. cognizant 20-20 insights 7
Figure 5
Early Warning System “Information Funnel”
Relevant
Heard Inferences
Highly Externally Sourced
Learned Inferences
Heard Inferences
Highly Externally Sourced
Learned Inferences
Actionable
Focused
Trustworthy
Value
Action
Information
Early Warning Insights
Extracted
Originated
Captured
Protected
Enterprise
Strategy
Diversions
Disruptive
Events Sustainable
Value
Diversions
Operational
Risks
The key consideration in reviewing the data
consumed by an early warning system is that if
the universe of data consumed for early warnings
is sourced from information your organization
either creates or sponsors, then the effective-
ness of your early warning system will be greatly
hampered.
Interrogating, Digesting Early
Warning Information
Much of the interest in big data is to house the
historical information to which incoming infor-
mation will be compared to discern either a new
pattern or an outlier that deserves attention. The
more information that is available to determine
this departure from the status quo, the greater
the confidence and more accurate the models
used to derive potential actions will be.
An early warning system seeks opportunities
and risks that impact the strategic, tactical and
operational activities of the organization that
potentially positively or negatively impact the
value achieved by the organization. Of particular
importance are the short-lived opportunities
and risks that, if left unaddressed, might change
market dominance and ultimately disrupt the
health of the organization.
Information monitored by an early warning
system seeks one of four things:
• Operational risks of all kinds.
• Internal and external disruptive events.
• Diversions to enterprise strategies of the orga-
nization.
• Diversions that potentially compromise the
sustainable value of the organization.
These tracked items will be obtained from:
• Internal sources, or the collective organization-
al knowledge and innovations, which are either
disruptive to the organization or innovations
that potentially will augment disruptions to the
marketplace.
• External sources, or inferences heard from a
variety of sources, as well as learned inferences
through intelligence programs either acquired
or sponsored.
Equally bad are organizations that take on and
address every source of input that represents a
potential operational risk or potentially disruptive
event. These organizations are so busy addressing
these potential organizational challenges that
either:
• Their cost structure is negatively impacted
because of the increased senior staffing
required to strategize and execute on these
potential operational risks and potentially
disruptive events, or
• The average return achieved from addressing
and executing on each of these potential oper-
ational risks and potentially disruptive events
is small.

8. cognizant 20-20 insights 8
Figure 6
Early Warning Attributes and Sequence of Activities
Detection
Prioritization
Assignment
Execution
Monitor
Selection
Early Warning Detection System
Workflow to Monitor the Dispensation of Detected Early Warnings
Collaboration Infrastructure, Methods and Objects
Actionable, Relevant, Trustworthy Just-In-Time Introspective (Internal) and External Information
Just-in-time
internal and
external
information
Methods and
models used to
identify early
warnings
Measurement
of expected
outcomes
Prioritization
of early
warnings
Time
criticalities
Derive
potential actions
Select the most
appropriate
action
Collaborate
information
and actions
Determine and
collaborate tasks
necessary to
achieve actions
Selection of
best people
and approach
Record expected activities
and durations
Execute
assignments
Monitor
expected
outcomes
against actuals
Fine tune and
revise actions
Coordination
Clearly a level of automation is required which
prioritizes those potential operational risks and
potentially disruptive events worthy of being
addressed. The process of ingesting the deluge
of information, prioritizing it all and ensuring that
priorities — potential operational risks and poten-
tially disruptive events — are addressed is the
primary role of early warning systems.
Let us first look at the categorization of early
warnings to understand the underpinnings of the
prioritization process.
Steps for Detecting, Dispensing
Early Warnings
There are several attributes of an early warning
system (see Figure 6).
An early warning system must be able to:
• Detect early warnings from a deluge of just-
in-time events and messages obtained from
internal and external sources. The events,
when having a certain criticality or reaching
some pre-determined threshold measurement,
will trigger early warning processes.
• Maintain a prioritization process to determine
which early warnings deserve action and to
select signals based on how much time an orga-
nization has earmarked for responsive actions.
• Assign the selected early warnings to a
workflow process and track each task associ-
ated with the early warning through execution.
• Continually collaborate information and activi-
ties associated with an early warning from
creation to action execution.
• Monitor the results and issue opportunities to
fine-tune them.
The Business Model Innovation Factory 9
describes
a new reality that requires the refinement of
business models and the data required to support
the reality of an accelerated business climate.
Business models that once would last for long
periods of time with minimal refinement now
have half-lives thanks to our highly communica-
tive marketplace, with much lowered barriers
to disruptions upsetting the equilibrium of mar-
ketplaces. The Harvard Business Review10
also
covered this business model half-life due to the
ability for innovations to be exposed through the
highly communicative marketplace and replicated
more readily — thereby requiring business models
to morph at an increased rate.

9. cognizant 20-20 insights 9
Information silos challenge the ability to quickly
morph the information that supports this
business model half-life and requires an environ-
ment that fosters a level of collaboration across
all information silos, disciplines and sectors. This
new environment requires a level of collabora-
tion across all silos, disciplines and sectors, and
defines a series of principles to:
• Catalyze something bigger than is available
from the status quo.
• Operate within flexible networks that deliver
actionable information.
• Be actionable through a well-devised system
that supports informed decisions.
• Take necessary risks with confidence.
• Operate with the understanding that transfor-
mation will continue to be the new reality.
Business information systems require an early
warning system as a catalyst to identify that
the status quo has changed, and as such the
final actions to an early warning that is systemic
should be:
• Identify the ongoing triggers to identify similar
future early warnings.
• Create a workflow to codify the identified
triggers into the early warning system.
• Identify changes to the information required to
support similar early warnings.
• Identify changes to models used to assess the
magnitude of early warnings.
• Identify any business model factors (assump-
tions, metrics, etc.) that are challenged by this
early warning and determine if these factors
warrant revisiting the business model.
• Create a workflow for business model
innovation should it be determined that a
revision of one or more business models is
warranted.
• Create a workflow for any new information that
needs to be available for dispensing with early
warnings as a consequence of this systemic
change.
• Create a workflow for any modifications to
business models used to assess the magnitude
of early warnings. (Note that many of these
modifications will be an outcrop of the business
model innovation processes.)
Early Warning System Components
The early warning system utilizes several
components of information (see Figure 7). Pre-
configured information includes:
• Detection rules and models that will trigger
the creation of potential early warnings and
prioritize these into actionable early warnings.
• Workflow rules used to ensure that early
warnings are dispensed with collaborative
actions.
The information stores used to house early warn-
ings, the data associated with early warnings and
the activities associated with early warnings are:
• Potential early warnings flagged for prioritiza-
tion.
• Data associated with prioritized actionable
early warnings.
• Activities taken to derive and execute appro-
priate actions in response to a prioritized early
warning.
Three major workflows are associated with the
processing of early warnings:
• Lifecycle management components of an
early warning system, which ensure that the
system does not become cluttered.
• Discovery components of the early warning
system, which utilize listeners to translate oper-
ational risks, disruptive events and diversions
into prioritized, actionable early warnings.
• Execution components of the early warning
system, which utilize workflow and collabora-
tion components to ensure that collaborated,
timely activities inform appropriate actions in
response to prioritized early warnings.
A Smooth Early Warning System
It is important to keep a constant eye on the per-
formance of an early warning system. The last
thing any organization will be willing to bear is
underuse of an early warning system due to the
time required to:
• Identify and/or prioritize early warnings.
• Validate the reasonableness of information
used to activate early warnings.
• Record activities that lead to actions that
transpire in response to early warnings.

10. cognizant 20-20 insights 10
Early Warning Response Workflow
Figure 7
Diversions
Early Warning Workflow
LLifeecyccle MManagemeent
Dissccooveerry
LLiffecyycle Management
EExeccuutioon
Early
Warnings
Detection
Rules
& Models
Potential Removal of Nonprioritized
Potential Early Warnings
Operational Risks
Disruptive Events
Potential
Actionable
Early
Warnings
Prioritized
Actionable
Early
Warnings
Prioritization
Engine
Collaboration
of Early
Warnings
Early
Warning
Event
Listener
Early
Warning
Actions
Removal of Completed
Early Warnings
Early
Warnings
Workflow
Rules
Early
Warning
Listener
To solve the first item, a degree of automation
will be necessary to ensure that the early warning
system is performing as expected and that the
amount of history available to it is not impairing
its proper functioning.
The second item requires that an acknowledged
governance program is in place that validates the
reasonability of information used to generate the
early warnings and that ensures the rules used
to activate these signals come under similar
scrutiny.
The third item requires that an automated
workflow and collaboration process is in place
that facilitates the recording and collaboration of
business activities associated with early warnings.
Launching an Early Warning System
The first step in launching an early warning
system is taking an inventory of the information
used to keep track of operational risks and oppor-
tunities that are publicly exposed. It will become
relatively obvious from this inventory that the
majority of information used to detect early
warnings is obtained through informal processes
and is captured from sources outside of the orga-
nization.
Determining the gaps from information available
for analysis within the organization, planning the
effort to automate the collection and dissemina-
tion processes and devising the means for making
it simpler for business stakeholders to utilize
signals as a just-in-time resource are major steps
in operationalizing an early warning system.
The linkages between the early warning system
and the business innovation program your orga-
nization has undertaken or is about to undertake
should be well understood and codified so that
early warnings trigger business model innovation
processes when appropriate and business model
process changes enhance the rule set used for
triggering early warnings when appropriate. (It
would be foolhardy to assume that all enhance-
ments to business models are outcroppings of
early warnings.)

11. About Cognizant
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Another major step is to devise processes to
ensure and communicate the trustworthiness
of information consumed by the early warning
system,especiallysincetimeisacriticalingredient
to the viability of the early warning system.
Lastly, understand the processes used to detect
challenges to the status quo. These processes will
need to be present in the early warning system,
through models available for prioritization,
through data and through business rules used for
the detection and prioritization of early warnings.
Footnotes
1
Maxwell Wessel and Clayton M. Christensen, Surviving Disruption, Harvard Business Review, Dec., 2012.
2
Malcolm Gladwell, The Tipping Point, Little Brown, 2000.
3
“When Going Viral Social Media Marketing Can Be Bad for Business,” Artful Media Group, Feb. 5, 2013.
4
Maxwell Wessell, “Stop Reinventing Disruption,” Harvard Business Review, March 7, 2013.
5
“AP Twitter Hack Underscores Social Media Pitfalls,” USA Today, April 24, 2013.
6
Shingai Samudzi, “Breaking Down Information Silos,” ProjectVision, March 2013.
7
”The Golden Ratio of Information,” ProjectVision, 2013.
8
Scott Anthony, “Business Model Innovation Is the Fastest Path to Greatness,” Forbes, October 4, 2012.
9
Saul Kaplan, The Business Model Information Factory: How to Stay Relevant When the World is Changing,
John Wiley & Sons, 2012.
10
Casadeus-Masanell and Zhu, “Business Model Innovation and Competitive Imitation, the Case of Sponsor-
Based Business Models,” Harvard Business Review, September 1, 2011.
About the Author
Mark Albala is the Service Line Lead of Cognizant’s Data On-Demand Service Line, a series of services
and products that support the effectiveness and efficiency of managing information as part of Cogni-
zant’s overall capabilities in enterprise information management. A graduate of Syracuse University,
Mark has held senior consulting, thought leadership, advanced technical and business development
roles for organizations focused on the disciplines of business intelligence, governance and data ware-
housing. He can be reached at Mark.Albala@cognizant.com.