by Rashik Parmar, Ian
Mackenzie, David Cohn, and David Gann
The search for new business
ideas and new business models is hit-or-miss in most corporations, despite the
extraordinary pressure on executives to grow their businesses. Management
scholars have considered various reasons for this failure. One well-documented
explanation: Managers who are skilled at executing clearly defined strategies
are ill equipped for out-of-the-box thinking. In addition, when good ideas do
emerge, they’re often doomed because the company is organized to support one
way of doing business and doesn’t have the processes or metrics to support a
new one. That explanation, too, is well supported. Without a doubt, if you
tackle business innovation systematically—rather than hoping people will get
creative during an “innovation jam” or a special offsite—you improve the odds
of success (and decrease the chances you’ll be left staring at a blank sheet of
paper). Traditional, tested ways of framing the search for ideas exist, of
course. One is competency based: It asks, How can we build on the
capabilities and assets that already make us distinctive to enter new
businesses and markets? Another is customer focused:What does a
close study of customers’ behavior tell us about their tacit, unmet needs? A
third addresses changes in the business environment: If we follow “megatrends”
or other shifts to their logical conclusion, what future business opportunities
will become clear?
We’d like to propose a
fourth approach. It complements the existing frameworks but focuses on
opportunities generated by the explosion in digital information and tools.
Simply put, our approach poses this question: How can we create value
for customers using data and analytic tools we own or could have access to? Over
the past five years, we’ve explored that question with a broad range of IBM
clients. In the course of that work, we’ve seen advances in IT facilitate the
hunt for new business value in five distinct—but often overlapping—patterns.
Those patterns form the basis of our framework. We believe that by examining
them methodically, managers in most industries can conceive solid ideas for new
businesses. (To learn about the underlying technical trends, see the sidebar
“Why Are These Patterns Emerging Now?”)
Why Are These Patterns
Emerging Now?
For decades, when we
thought about how IT could create value for business, we focused on automating
and reducing the cost of operational and management processes. Then the advent
of the internet created opportunities to build entirely new business models
(witness Google, Amazon, eBay, and the revolution in electronic content
distribution). Now a third wave of IT-enabled innovation is being powered by
three drivers:
The Explosion in Digital
Data
Digitization is making
massive amounts of data readily available. Data about suppliers and partners
can be had in near real time, customers are increasingly willing to share all
manner of information, and wired objects—the Internet of Things—are coming
online in droves. The value of these resources is just beginning to be
understood.
Better Tools for Data
Our capacity to
integrate, analyze, and exploit structured data continues to improve, and our
ability to understand and learn from data has been transformed. The humanlike
performance of IBM’s Watson on Jeopardy! signaled a major
change. Now we know that we can get “the answer” from technology; we just have
to decide what to ask. As we learn the right questions, we’ll move from the era
of information to the era of insight.
Business in the Cloud
For most of history,
business transactions occurred in physical space. As business becomes more
virtual, its nature changes. For example, increasingly complex processes are
now handled by standard software; they can be turned into service offerings
through low-cost, high-powered cloud computing. This digitization of business
generates opportunities to reduce operating costs and to create new offerings
for customers.
While each of these trends
can create value in its own right, we are now seeing companies learn to combine
two or three of them to invent powerful new propositions.
None of the patterns
depends on bleeding-edge technology. The first one, in fact, is very familiar: using
data that physical objects now generate (or could generate) to improve a
product or service or create new business value. Examples of this
include smart metering of energy usage that allows utilities to optimize
pricing, and devices installed in automobiles that let an insurance company
know how safely someone drives. The second pattern is also familiar: digitizing
physical assets. Fifteen years ago you could have read this article
only in a printed magazine; now you can read it on half a dozen different
digital platforms, send it to friends, and say what you think of it via social
media. The third pattern is somewhat more recent: combining data within
and across industries. (Here we start to enter the realm of “big
data.”) An example of this would be a smart-city initiative like the one in Rio
de Janeiro, where private utilities, transportation companies, and city
agencies consolidate information so that they can deal with natural disasters
more effectively. The fourth pattern is trading data; here, a
company whose information is valuable to another company sells it, as when a
cell phone service identifies traffic jams by seeing where customers in cars
are slowed down and shares the information with a navigation-device company.
The fifth pattern, codifying a capability, allows a company to
take any process in which it is best-in-class—managing travel expenses, for
instance—and sell it to other companies, using cloud computing.
The new businesses we’ve
seen run the gamut from incremental to game-changing. Some simply enhance the
current business (they’re sustaining innovations, in Clay Christensen’s
terminology). Others are more disruptive: They require a new business model—and
often a separate business unit—to support them. Still others evolve or could
evolve into platform-based businesses—in which a stable core technology is
surrounded by complementary products and services, typically provided by other
companies. (Think iTunes and song and video recordings.)
In this article we’ll
take you through each of the five patterns, providing examples drawn from our
clients’ and our own experience. We’ll also provide a set of questions that can
help you figure out whether a pattern is relevant to your business.
Pattern 1: Augmenting
Products to Generate Data
Because of advances in
sensors, wireless communications, and big data, it’s now feasible to gather and
crunch enormous amounts of data in a variety of contexts—from wind turbines to
kitchen appliances to intelligent scalpels. Those data can be used to improve
the design, operation, maintenance, and repair of assets or to enhance how an
activity is carried out. Such capabilities, in turn, can become the basis of
new services or new business models. A classic example is Rolls-Royce’s engine
health management (EHM) capability. In the mid-2000s new sensor technology and
data management allowed Rolls-Royce to identify airplane engine problems at an
early stage, thereby optimizing maintenance and repair schedules, and to
improve engine design. The ability to control costs encouraged the company to
adopt a business model in which it retained ownership of the engines and
provided maintenance and repairs, charging airlines an all-in fee based on
actual hours flown, as part of a “power-by-the-hour” offering. The new data
from the sensors also facilitated other services, such as parts inventory
management and flight efficiency reporting.
One could imagine
Rolls-Royce extending this capability further—to engines for cruise ships and
turbines—and even building a platform around it. The company could develop an
IT-based system with the capacity to handle large volumes of sensor-generated
data, and open it up to third-party applications geared to particular
industrial contexts.
A more recent augmented
product is SKF’s intelligent bearings, which contain miniaturized,
self-powering sensors that continuously communicate their operating conditions.
With this technology, bearings can be monitored in situ, which was previously
impossible or impractical. SKF provides the data as an additional service that
allows customers to see the extent of any damage within a bearing and take
remedial action—for example, adding lubricant or mitigating overloads—well
before a failure occurs. Machinery thus becomes more reliable and less
vulnerable to downtime. The sensors also measure the load the bearing actually
experiences—information that can be used to improve system and bearing
design—and can detect problems outside the bearings, such as significant
vibrations within the equipment.
There’s no reason
nonindustrial companies couldn’t take a page from this playbook. Indeed,
Progressive Insurance now offers a service called Snapshot, whereby the cost of
insurance is based in part on how the customer drives the car. Progressive
sends the customer a device that plugs into the car; it records things like
mileage, night driving, and heavy braking.
Pattern 2: Digitizing
Assets
Over the past two
decades, the digitization of music, books, and video has famously upended
entertainment industries, spawning new models such as iTunes, streaming video
services, e-readers, and more. As mobile technologies continue to fuel this
trend, more creative businesses are tapping into it and generating their own
enhanced services or new business models. Take the International Museum of
Women, an innovative nonprofit that hosts internet exhibitions of art created
by women around the world. It has an online community of 600,000 annual unique
visitors, 10,000 artistic contributors, 40,000 e-news subscribers, 11,000
Facebook fans, and 7,000 Twitter followers in more than 200 countries
worldwide. It can organize and host exhibitions for a fraction of what it costs
a traditional museum to borrow, ship, and display works, and it allows visitors
to communicate directly with the artists—without ever leaving home.
Digitized versions of
physical assets are transforming the way people operate in other industries as
well. For instance, sophisticated analytic and visualization techniques have
improved design in many manufacturing industries, from aerospace and automotive
to clothing and furniture. Three-D printing now provides an opportunity to
reverse the digitization process and make a physical object from digital
representations. (This is how GE builds some turbine parts.) And the
digitization of health records, of course, is expected to revolutionize the
health care industry, by making the treatment of patients more efficient and
appropriate, and slashing hundreds of billions of dollars in costs.
Digitization is improving health care in other ways, too: Surgeons are using
digital models of the body to increase the accuracy, and reduce the
invasiveness, of highly sensitive surgery.
The management of
digitization itself could be a new business. Many industries need a long-term,
secure way to store their digital assets. Those assets might represent aircraft
designs, nuclear power plant operations, oil exploration logs, entertainment
content, or government records, but the preservation and access-control
requirements are essentially the same. Thus, an incumbent that can successfully
manage its own data could provide that capability as a service to others,
regardless of industry.
As more assets become
digitized, we expect competitive advantage to shift. Digitization typically
slashes distribution costs and makes the ability to move physical inventory
efficiently or secure favorable store locations less critical. But you can
expect that offering customers more choices and more tailored service will
become increasingly important. Going forward, we will see more players explore
ways to use the digital nature of the purchase process itself to strengthen
customer intimacy and transform the industry yet again. Organizations that can
help other companies master this challenge, too, stand to profit.
Pattern 3: Combining
Data Within and Across Industries
The science of big data,
along with new IT standards that allow enhanced data integration, makes it
possible to coordinate information across industries or sectors in new ways.
Consider the city of Bolzano, in northern Italy, where retired people account
for almost a quarter of the population. That puts considerable strain on social
and health services. Working with the city, IBM developed a network of sensors
in the home that monitor not only conditions such as temperature, CO2 level, and water usage, but also what constitutes “normal”
behavior patterns—for example, regular cooking times. Abnormalities trigger a
call to a relative or a friend, who can check that all is well with the senior
and alert the appropriate city service if necessary. Behind the scenes, a
common IT system links all the relevant agencies—social services, health, and
property maintenance—enabling a highly coordinated response. City officials
believe that this initiative has lowered assistance and care costs by 30% and
allowed many more retirees to stay in their homes, thereby reducing the need to
build and run special accommodations for them.
Other cities are
spearheading cross-sector initiatives as well. The Greater London Authority has
set up one that it hopes will inspire brand-new ways of doing business. To
manage the roadway congestion caused by a sizable jump in the number of small
vans delivering packages from e-retailers to city residents, it has launched
the Agile Urban Logistics project. The project combines data on deliveries from
the retailers with data on traffic conditions and optimization software. The
goal is to encourage the private sector to develop new business models, such as
shared-delivery services in specific areas.
Similar opportunities
can be found in the private sector. While some firms, such as Walmart and Dell,
have successfully integrated data across their supply chains, most supply
networks are relatively uncoordinated. Advances in IT could help address that
problem. In the auto industry, for instance, manufacturing plants that use
water to cool machinery need to carefully calibrate water temperatures. Access
to reliable data on upstream water temperature could make a meaningful
contribution to plant efficiency. Water suppliers could provide such
information as a service, which could yield additional revenue.
In Germany, a new
business is integrating data across one industry—health care—to improve
efficiency. Traditionally, medical and dental practices have used a variety of
formats (some paper, some electronic) to request payment from insurance
companies. The new service collects the information directly from the
practices’ IT systems, preserving confidentiality and standardizing and
cleaning the data, which it then delivers to each insurance company in its
required format. The service allows insurers to automate the payment process
and check all billings for fraud. The savings insurers gain as a result more
than cover the cost of the service.
Pattern 4: Trading Data
The ability to combine
disparate data sets allows companies to develop a variety of new offerings for
adjacent businesses. Take the recent partnership between Vodafone and TomTom, a
provider of satellite navigation devices and services. With its mobile network,
Vodafone can identify which of its subscribers are driving, where they are, and
how fast they’re moving. Such data can be used to pinpoint traffic
jams—information that is extremely valuable to TomTom, which buys it from
Vodafone. Cell phone data can also be used to improve transit and traffic
management and, we speculate, in more-commercial ways as well—for example, by
companies wishing to place context-sensitive advertisements, perhaps for
restaurants and stores that are close to a user’s location.
An ambitious “open
platform” collaboration between the UK’s Meteorological Office, IBM, and
Imperial College’s business school and Grantham Institute for Climate Change
aims to create a whole new exchange for detailed global weather data. Numerous
organizations—including insurers and agencies concerned with responding to
natural disasters—need that kind of data. While a great deal of it is
available, few standards for it exist, which makes it challenging to share or
combine. Furthermore, commonly accepted standards for analytic weather models
have not yet been developed. The gaps in both areas constrain the quality of
assessments and decision making. The new venture aims to fill those gaps with
an online platform that’s open to a wide range of contributors. In a sense it
will provide a marketplace for weather knowledge, data, and modeling
techniques. The organizations behind it hope it will help galvanize innovative
solutions for assessing and managing climate-related risk. (Note that this
initiative exemplifies two patterns—trading data and combining data across
industries.)
Pattern 5: Codifying a
Distinctive Service Capability
Ever since their
invention, IT systems have helped automate business processes. Now companies
have a practical way to take the processes they’ve perfected, standardize them,
and sell them to other parties. Any process that is best-in-class—but not
central to a company’s competitive advantage—can thus be turned into a
profitable business. Cloud computing has put such opportunities within even
closer reach, because it allows companies to easily distribute software,
simplify version control, and offer customers “pay as you go” pricing.
IBM’s Global Expense
Reporting Solutions were originally developed to automate all the steps in the
company’s internal travel booking and expense-reporting processes. IBM found
that, in addition to reducing related administrative costs by 60% to 75%, the
systems helped ensure that employees complied with corporate T&E policies,
lowering total expense spending by up to 4%. A few years later, realizing that
many of its customers would be interested in achieving comparable savings, IBM
turned the systems into a service, which it has since sold to organizations
worldwide, effectively giving birth to a new business. Analyzing the resulting
data flow has allowed IBM to better focus the customers’ internal audit
processes. IBM now also offers an internally developed accounts receivable
system as a service to third parties.
Citigroup provides
another example. The bank developed models for transaction data to analyze the
flow of money in different parts of the financial system, uncovering
inefficiencies that hindered its clients’ ability to make effective use of
different payment mechanisms. Over a five-year period, those models have been
honed into a stream of client services. CitiDirect BE Mobile enables financial
institutions and their customers to track the status of payments anytime,
anywhere. In the first year it was offered, the system grew to support $11
billion in transactions; now it supports approximately 10 times that amount. In
October 2013 the bank launched CitiDirect BE Tablet, which is designed to help
C-level executives manage the financial flows of their global companies more
effectively.
It’s not just IT
processes that present opportunities for new value creation. We know of one
major UK catalog retailer that has developed an especially efficient and agile
system for designing and producing online catalogs. This lets it offer a much
bigger range of products while maintaining less than half the stock of
competitors. If the company made this industry-leading capability available to
other retailers as a service, it could launch a new line of business. That
business could in theory be developed into a disruptive platform that
third-party retailers could use as a market channel.
Combining the Patterns
The five patterns are a
helpful way to structure a conversation about new business ideas—and, as we’ve
shown, there are good examples of all five—but actual initiatives often
encompass two or three of the patterns. (In fact, as we were writing this
article, we were aware that some of our examples could be used to illustrate
more than one pattern!) In addition, what begins as a relatively simple
extension of an existing business often grows into a whole new business.
Take the smart energy
meters being rolled out in nearly every developed country, which record the
consumption of energy over the course of the day and communicate that
information back to the energy provider. These devices started out by
augmenting the utilities’ businesses along several dimensions: They made it
possible to adopt intraday pricing that reflected demand patterns, to optimize
operations and infrastructure usage, and to provide customers with the
information needed to manage their own usage. But before long it became clear
that the meters created opportunities for altogether new businesses. They
could, for instance, gather data on the energy usage patterns of appliances,
which could be sold back to their manufacturers, or be used to provide enhanced
services to homeowners, such as the feed-in of locally produced energy (say, from
solar panels).
Smart metering could
also support a platform-based business, we believe. When the German energy
utility company E.ON formed a new business unit focused on a smart meter
capability, IBM developed an IT system (software and infrastructure) to support
the various activities—data capture, data aggregation, dynamic pricing
models—that E.ON Metering needed to undertake. It turns out that the modular
design of this system allowed it to be customized for other utility providers
as well. (Full disclosure: This new venture is being jointly developed by IBM
and E.ON.) And smart meters might even become the technology platform for
delivering a wide range of applications to homeowners, from security systems to
entertainment systems.
Getting Started
When we work with
clients to uncover new business opportunities, we begin by describing the five
patterns, using one or two detailed examples, and then move right to questions
designed to inventory the raw material out of which new business value can be
carved. The questions seem simple, but answering them requires considerable
thought in most cases.
·
What data do we have?
·
What data can we access
that we are not capturing?
·
What data could we
create from our products or operations?
·
What helpful data could
we get from others?
·
What data do others have
that we could use in a joint initiative?
Armed with the answers,
the team cycles back through each pattern to explore whether it, or perhaps a
modification or combination of patterns, could be applicable in the company’s
business context. The questions include:
1. Augmenting Products
·
Which of the data relate
to our products and their use?
·
Which do we now keep and
which could we start keeping?
·
What insights could be
developed from the data?
·
How could those insights
provide new value to us, our customers, our suppliers, our competitors, or
players in another industry?
2. Digitizing Assets
·
Which of our assets are
either wholly or essentially digital?
·
How can we use their
digital nature to improve or augment their value?
·
Do we have physical
assets that could be turned into digital assets?
3. Combining data
·
How might our data be
combined with data held by others to create new value?
·
Could we act as the
catalyst for value creation by integrating data held by other players?
·
Who would benefit from
this integration and what business model would make it attractive to us and our
collaborators?
4. Trading Data
·
How could our data be
structured and analyzed to yield higher-value information?
·
Is there value in this
data to us internally, to our current customers, to potential new customers, or
to another industry?
5. Codifying a
Capability
·
Do we possess a
distinctive capability that others would value?
·
Is there a way to
standardize this capability so that it could be broadly useful?
·
Can we deliver this
capability as a digital service?
·
Who in our industry or
other industries would find this attractive?
·
How could the gathering,
management, and analysis of our data help us develop a capability that we could
codify?
Once we’ve worked our
way through the second set of questions, the process looks pretty much as you’d
expect it to: The various ideas are collated and prioritized; generally one or
two are tapped for further investigation; subgroups are charged with fleshing
out the ideas in more detail. They’re asked to develop a scenario in which an
idea creates significant new business value and to identify the key assumptions
that would need to hold true for that to happen. After a few weeks the team
reconvenes to present its work to a senior executive sponsor.
Success Factors
The successful
initiatives we’ve observed or participated in had four things in common (beyond
hygiene factors like a cross-functional team, adequate resources, and top
management support).
Strong Technology
Presence
Having the CIO, the CTO,
or whoever has overall responsibility for IT play a major role in the project
is critical; it’s even better if that person is the effort’s senior sponsor.
This implies, though, that the CIO/CTO role should be focused on business value
creation rather than business efficiency, which in turn has implications for
the background and skills of the CIO/CTO.
Inputs from External
Parties
The search for
innovations often benefits from outside perspectives—whether from customers,
suppliers, people in adjacent industries, or IT specialists. Firms that execute
ideas most effectively typically involve external parties when scaling up
implementation, because it’s a faster way to acquire the capabilities needed to
speed offerings to market.
Motivated Leadership
If an initiative is
disruptive rather than sustaining, it will need a strong leader who can
overcome the obstacles that are inevitably set by the incumbent business’s
well-established culture. Emerging leaders are often best suited to this role,
since they usually have a strong desire to prove themselves and to create
something new.
Emotional Commitment
Successful initiatives
move beyond the intellectual and become an emotional commitment—even a
mission—for the people involved.
For some years now,
information technology has been expanding away from its traditional role of
automating and reducing the cost of operational and managerial processes. Of
course, IT will continue to serve this function. But it’s becoming a stronger
force in the quest for new business opportunities. The faster technology
advances, the more opportunities seem to open up. It’s time companies took a
structured, systematic approach to examining these advances, carefully
considering how IT can enable not only better products and services but also
innovative business models and platforms. By thinking through what implications
the five patterns hold for their businesses, companies can find ways to engage
more fully with the digital economy—and cash in on its promise.
Rashik Parmar is the president of IBM’s Academy of
Technology, Ian Mackenzie is a senior lecturer at Harvard
Business School, David Cohn is a research scientist at IBM’s
Thomas J. Watson Research Center, and David Gann is vice
president of development
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