1. Where is the growth promised from
Big Data?
By Sigvard Bore
Special Contribution by Randy Bartlett
Infosys Management Consulting Services – Strategy Practice
Overview
Several years ago, your executive leadership team returned from an industry conference
thoroughly convinced that Big Data was going to enable the next big thing for your
company. This possibility was further underscored when two of your largest strategic
partners hosted day-long workshops with various parts of your business essentially
stating the same thing. So, you launched a Big Data capability development effort
across your business working closely with your IT department, two of your trusted
partners, and your favored management consultants. To date, various improvements
have been attributed to your newly implemented Big Data capabilities, but none have
lead to the top-line growth you had been hoping for.
View Point
Does your company’s Big Data plan include sweeping growth objectives? If so, how do
you plan to process new ideas unearthed by your Big Data analytics? Can you get to
meaningful commercial outcomes without betting the farm until the idea’s commercial
viability has been proven? This point of view will explore the answers to these
fundamental questions, while also demystifying the topic of Big Data. And finally, we
will establish the critical need for a Business Model Innovation (BMI) capability to
complement any Big Data undertakings for companies that wish to fully realize the
growth promise of this exciting information-as-power area.

2. Demystifying Big Data
Big Data has emerged from the convergence of mega trends and various technologies: digital
commerce, cloud / distributed computing, cheaper storage, proliferation of social media and
participation across the globe, and sophisticated analytics. Bottom line, the environment is now in place
to make sophisticated analysis of massive quantities of data a reality for most organizations, and is no
longer the exclusive domain of large governmental agencies (e.g. NSA, CIA, etc.).
International Data Corp (IDC) claims it is imperative that organizations and IT leaders focus on the ever-
increasing volume, variety and velocity of information that forms Big Data:
 Volume – with the possibility of mining information from so many different sources (internal
and external, structured and unstructured), the sheer volume of that data requires unique
capabilities to deliver meaningful insights.
 Variety – in the Big Data world, data exists in a massive variety of formats, not just easily
defined, numeric database fields that are convenient for analysis; samples include emails,
images, video, audio, blogs, OLAP systems, and so on. Some estimates predict that more than
80% of an organization’s critical data will not be numeric.
 Velocity - According to Gartner, velocity "means both how fast data is being produced and how
fast the data must be processed to meet demand." Being able to react (or better yet, “pro-act”)
to what the flow of information is revealing will determine the winners when it comes to Big
Data.
Infosys believes another attribute needs to be addressed when managing Big Data expectations:
 Volatility – the flow of data is unpredictable and varied (e.g. a tragic event can cause a bloom of
activity in a given geography, or an emerging trend may finally reach critical mass and generate
exponential traffic). Furthermore, defining new relationships between disparate data sources is
as much art as science, and will be a continuously evolving undertaking.
How do you know you are addressing Big Data instead of just another very large data warehousing
effort? It is the combination of the above mentioned attributes that truly define Big Data (not just one
or two). That said, meeting these attributes does not guarantee any ideas will surface – having a clearly
defined operational process supporting your Big Data effort is critical for the delivery of any true
business value. Furthermore, the structure and discipline brought by such a process helps ensure the
veritable mountain of data does not overwhelm or spiral out of control, and instead becomes an ever-
optimized spring of useful business insights. Infosys’ P.H.A.S.E. framework (see Figure 1 below) was
specifically developed to meet this operational process need for Big Data.
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3. Figure 1
The major P.H.A.S.E. steps are fairly intuitive; however, there are three critical success factors when
putting the process in place:
1. Business leaders must work closely with statisticians, mathematicians, and technology architects
throughout the process
2. Recognize the iterative nature of Big Data analytics, and that value delivery increases over time
(your first iteration will not be as rewarding as your tenth)
3. New business ideas that surface must be articulated with the optimal business model in mind,
not constrained by the existing business model
We have discussed the key attributes of Big Data, and the fundamental need for an operational process
to generate ideas on a repeatable basis, but is there a way to evaluate a company’s current Big Data
capability? Infosys has also developed a maturity model (see Table 1 below) that can help any company
determine its own Big Data capabilities, and understand what steps are required to improve its maturity
rating. As with any capability, progression is a deliberate undertaking – no company will go from lagging
to pioneering overnight. Also, given that the various dimensions are closely coupled with one another,
the lowest dimensional rating actually becomes the overall rating. For example, having a very scalable
processing capability doesn’t mean much if you are only handling small volumes of simple internal data;
however, having that same scalable processing capability while also dealing with large volumes of
complex internal and external data does demonstrate meaningful Big Data capability.
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4. Table 1
So just how far-reaching are the promises of a Big Data capability for organizations? The possibilities are
virtually endless, but a small sampling includes: hyper-efficient inventory management, real-time risk
portfolio updates, increasingly accurate demand forecasts, new market identification, individual
circumstance marketing, and so on. Having said that, it is possible to categorize all of the Big Data-
generated insights into two fundamental categories: operational excellence and new business ideas
(see Figure 2 below).
Figure 2
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5. Unlocking Real Growth from Big Data
Companies have been focused on operational excellence for years, and have developed strong
capabilities to handle these types of imperatives… not so for new business ideas. And therein lays the
crux of the Big Data growth challenge: what to actually DO with the continuous feed of new ideas that
surface from Big Data analytics. The hard truth is that most companies are not ready to handle a
breakthrough idea properly even once, much less on an ongoing basis.
Therefore, having an ability to efficiently determine the commercial viability of new ideas is just as
important as the Big Data capability that spawned these ideas. Without it, organizations will have a
wonderful idea engine, but very few places to go.
With that in mind, most companies do set out with the right outcomes in mind when handling new
business ideas, but more often than not, stumble into common pitfalls in execution (see Figure 2 below):
Figure 3
To overcome these pitfalls, organizations must embrace several guiding principles for handling
innovative growth ideas:
1) Be prepared to adapt and pivot – the new idea is not a destination, but rather a point of
departure. Growth ideas from Big Data analytics are raw ideas that more often identify
opportunity areas rather than specific offering details.
2) Time it right – bad timing trumps good ideas… NOTE: the maturity/sophistication of your Big
Data capability can do much to ensure proper timing.
3) Prepare and explore – do not over-plan; rather, wear an explorer’s hat and expect the
unexpected. An explorer mindset encourages learning rather than simply proving or disproving a
point.
4) Get out of the building – there is no substitute for actual customer reaction and feedback, and it
is the only true way to verify an idea’s value and growth assumptions… do not make the mistake
of designing the perfect, fully functional offering before obtaining real customer reactions.
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6. The above principles must be at the core of any capability an organization develops to handle new
business ideas – the bigger the idea, the more critical they become! Infosys has developed its Business
Model Innovation (BMI) offering around these principles while leveraging leading best practices from
the innovation and startup domains. Fundamentally, BMI seeks to provide the idea commercialization
analysis that sits in front of any possible scale decision, which improves the commercial viability
prediction AND helps ensure that good ideas are not shelved unnecessarily. In addition, it is a collection
of adaptable tools that can be tailored and reused again, perfectly aligned with the funnel of ideas
produced by a Big Data analytics capability. Whether your organization leverages Infosys’ BMI offering,
or chooses to develop its own solution, the following attributes should be the goal:
 Integrates leading practices at all key decisions points along the idea to commercial outcome
prediction process
 Identifies the optimal business model to support the innovation idea
 Uses a rapid test-and-learn process to achieve customer “fit” on a low cost , quick-to-market
basis
 Accommodates changes (pivots) to fully vet and optimize ideas
 Applies customer, product, and market analytics to improve the risk/return potential of
different business model choices (prototype comparison of business model options)
 Improves the accuracy of commercial outcome prediction (by at least several percentage points)
 Delivers a repeatable BMI capability
Business Model Innovation in Action (illustrative case study)
The following case study illustrates the critical and complementary role Business Model Innovation plays
when coupled with a growth-focused Big Data analytics capability.
ACME Publishing is a global leader in the information services industry, with a strong competency in the
North American and European legal and regulatory publishing markets. ACME has spent the last three
years developing its Big Data capabilities, and last month, several new business ideas surfaced.
Preliminary analysis of these opportunities revealed the following:
1) $50M opportunity to provide tailored regulatory information for three emerging Asia-Pac
markets (mostly to existing customers looking to expand into key emerging markets)
2) $30M opportunity to provide a royalty payment capability to the European professional
education market
3) $200M opportunity to provide a content delivery service from a super Contract Research
Organization (CRO) and its various pharmaceutical partners & affiliate CROs
4) $150M opportunity to bundle its disparate offerings for existing and new legal practices in North
America
NOTE: The above preliminary analysis mostly employs a market analysis approach, which emphasizes
revenue potential and not much else in favor of speed, and considers a 3-5 year horizon – a very
common practice for most companies considering innovation ideas… (continued)
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7. It is important to note that ACME Publishing did not get to this point overnight; rather, it went through
its own period of maturation over several years, and still had goals for the future relative to its Big Data
capabilities (see Table 2 below).
Table 2
As illustrated by the maturity model, ACME had to grow its own Big Data capabilities in order to enable
the identification of the current collection of growth ideas – not surprisingly, the prior years had
revealed mostly operational excellence ideas. ACME started with some dimensions being lagging, while
others being mainstream. As a result, their first year of “Big Data” effort was focused on bringing those
three skill areas up to par, which was then followed by a push to move three areas into a leading
position. Their future plans include maturation across all dimensions, but will likely follow a similar
deliberate approach (avoiding doing too much at once), with clear value delivery checkpoints along the
way… (continued)
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8. Given limited investment funding, ACME Publishing needed to prioritize opportunities to pursue, which
after a first pass resulted in the following:
 Opportunity 4 was the clear favorite as it had the highest revenue potential, while also being in
ACME’s existing core competency
 Opportunity 1 was second as it had a proportionally smaller investment, but still very favorable
revenue potential. It, too, was still well within ACME’s core focus, but for a new geography
 Opportunity 3 was third primarily because of its top revenue potential, and most stakeholders
agreed they could probably leverage whatever they were doing within the legal & regulatory
space for the pharma space
 Opportunity 2 was ranked last, reflecting its lower revenue potential and focus on industries
outside ACME’s core sectors
Ordinarily, this would have led to development of a growth plan, but ACME’s CEO was still uneasy about
the level of investment that could be required before the ideas’ expected value could be validated.
So, despite incredible pressure to drive growth as quickly as possible, the CEO decided to engage with
Infosys for a 3-4 month period to apply its BMI methodology across the four ideas. Below are the
interesting findings that validated her hesitation, and ultimately positioned ACME Publishing on a much
more promising growth trajectory:
 Opportunity 4 was proven commercially unviable as the first series of actual client interactions
revealed (i.e. getting out of the building) – the demand simply wasn’t there, and the overall
revenue projection was dramatically lowered to $40M. Furthermore, when properly considering
all the business model implications, particularly the cost structure, the $40M revenue growth
would come at an unacceptable profit level.
 Opportunity 1 was proven commercially viable, but became less attractive when the full
business model implications were considered. The $50M revenue growth was proven accurate,
but the profitability was 1.5% lower than hoped.
 Opportunity 3, despite its revenue growth potential, showed the worst profitability. However,
upon removing the self-imposed business model restrictions (the ones that leveraged heavily
from their legal & regulatory areas), the idea actually had the highest revenue potential AND
highest profitability.
 Opportunity 2 ended up second, with a slightly increased revenue potential of $35M, but with a
very promising profit level (only second to opportunity 3).
So the net result of applying BMI in conjunction with its Big Data capabilities resulted in a different
prioritization, a much higher level of confidence in the value / growth potential of these ideas, a
repeatable capability that could better analyze the commercial viability of its continuously incoming
growth ideas, and a more confident CEO who was assured that ACME was finally going to begin realizing
the growth promise associated with its Big Data analytics.
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9. Conclusion
Growth from Big Data was never the promise. Rather, the identification of ideas for growth was, is, and
will continue to be the promise of sophisticated analytics applied to unimagined volumes of data. So
what must companies do to drive meaningful growth from their Big Data efforts?
1. Improve their Big Data maturity level to drive higher quality growth ideas
2. Augment this capability with the means to perform rapid commercialization analysis on a
continuous stream of new ideas
Like most “hot” business topics, the hyped benefits associated with Big Data assume some underlying
sophistication. Furthermore, just because someone tells you what to do, it doesn’t mean you will know
how to actually do it. Hopefully, this point of view has clarified exactly what it takes to drive real
revenue growth from your Big Data undertakings. It will require a commitment to build pioneering Big
Data maturity, and then an equally pioneering mindset when presented with a promising idea:
Organizations must think and behave like entrepreneurs, treating each idea as a startup, making sure it
has legs before sinking substantial capital into scaling a business around it.
About the Authors
Sigvard Bore is a Senior Principal in the Strategy practice at Infosys. He has more than 19 years of experience helping
industry clients with Business Model Innovation, Strategy Formulation, Organizational Design, Value-Driven Decision
Making, Process Optimization, Large-Scale Program Management, Workforce Mobilization, and Portfolio
Management (projects & applications). He can be reached at sigvard_bore@infosys.com
Randy Bartlett is a Senior Principal in the Strategy practice at Infosys. He has more than 20+ years of experience
providing and performing advanced business analytics. His activities include organizing analytical resources;
reviewing advanced analytics; and providing analytical advancements. Randy can be reached at
randy_bartlett@infosys.com
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