Triggering Business Model Innovation Programs Through Early Warning Systems

Triggering Business Model Innovation Programs Through Early Warning SystemsInputs 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 SummaryThe 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.

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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.

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“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 WarningAn 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

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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

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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 WarningsAmong 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


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, DispensationWhile 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 RequirementsAn 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


• 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

Info

rmat

ion

SourcesDelivery Vehicle

Indirect Source Formats

CustomerPartner Web

Distributor Reports

ConsumerRetailer Reports

Advertisers

PartnerRegulators and Watchdogs

Direct

Regulator Internal Sources

Websites

TransactionsMobile Facilities

Call Centers

InteractionsExternally Obtained

Mobile Facilitated

IntelligenceRFID

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


Figure 5

Early Warning System “Information Funnel”

Relevant

Heard Inferences

Highly Externally Sourced

Learned InferencesHeard Inferences

Highly Externally Sourced

Learned Inferences

Actionable

FocusedTrustworthy

Value

Action

Information

Early Warning Insights

Extracted

Originated

Captured

Protected

Enterprise Strategy

Diversions

DisruptiveEvents Sustainable

ValueDiversions

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 InformationMuch 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.


Figure 6

Early Warning Attributes and Sequence of Activities

Dete

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Prio

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Assi

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Exec

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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

Coor

dina

tion

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 WarningsThere 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.


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 ComponentsThe 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 SystemIt 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.


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 EventsPotential

ActionableEarly

Warnings

PrioritizedActionable

Early Warnings

Prioritization Engine

Collaboration of Early

Warnings

Early Warning

Event Listener

EarlyWarningActions

Removal of Completed Early Warnings

Early WarningsWorkflow

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 SystemThe 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.)

About CognizantCognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process out-sourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry and business process expertise, and a global, collaborative workforce that embodies the future of work. With over 50 delivery centers worldwide and approximately 164,300 employees as of June 30, 2013, Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: 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, especially since time is a critical ingredient 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.

Footnotes1 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 AuthorMark 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 [email protected].

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