Module 4 - Data as a Business Model - Online

D: DRIVE
How to become Data Driven?
This programme has been funded with
support from the European Commission
Module 4: Data as a Business Model

Smart Data Smart Region | www.smartdata.how
This programme has been funded with support from the European Commission. The author is
solely responsible for this publication (communication) and the Commission accepts no
responsibility for any use that may be made of the information contained therein.
The objective of this module is to learn what are the
opportunities in the data business world and how are
traditional business models updated with this emerging
data opportunity.
Upon completion of this module you will:
- Learn how to recognize the opportunites of where big
data can benefit your company
- Answer the right questions about your company and
then go through the steps of integrating the solutions
into your company
- Understand the pillars of a data driven business
strategy
- Find out more about 6 different data business models
Duration of the module: approximately 2 – 3 hours
Module 4: Data as a
Business Model

1 Getting started on the Big Data Journey
2
This programme has been funded with support from the European Commission. The author is
solely responsible for this publication (communication) and the Commission accepts no
responsibility for any use that may be made of the information contained therein.
– Five Pillars of a Data driven Strategy
Data Driven Business
– Where can Big Data create
Advantages inside your company?
– Steps of Integration
3
– 6 main Data Driven Business Models
• Product Innovators
• System Innovators
• Data Providers
• Data Brokers
• Value Chain Integrators
• Delivery Network Collaborators
– Another Perspective: Data enabled
business model innovation
Designing Data Driven Business Models

GETTING STARTED
ON THE BIG DATA
JOURNEY
1. Where can Big Data create Advantages
inside your company?
2. Steps of Integration

The rise of big data is an exciting — if in some
cases scary — development for business. Together
with the complementary technology forces of
social, mobile, the cloud, and unified
communications, big data brings countless new
opportunities for learning about customers and
their wants and needs. It also brings the potential
for disruption, and realignment. Organizations
that truly embrace big data can create new
opportunities for strategic differentiation in this
era of engagement. Those that don’t fully engage,
or that misunderstand the opportunities, can lose
out.

Understanding where big data can
drive competitive advantage is
essential to realizing its value. For
many companies, the insights
drawn from big data have already
resulted in profitable, sustainable
growth in three areas:
CUSTOMER INTIMACY
PRODUCT INNOVATION
OPERATIONS

CUSTOMER
INTIMACY
Big data puts the customer at the heart of corporate strategy. Organizations
are inundated with customer data from interactive websites, online
communities, and government and third-party data banks. Information on
social-media platforms such as Facebook is particularly telling, with users
sharing nearly 30 billion pieces of content daily. At the same time, it is now
possible to bring together social-media feeds with disparate sources, including
weather data, cultural events, and internal data such as customer contact
information. Further, advanced analytical tools allow for faster, more effective,
and less costly processing and create the potential to rapidly develop new
insights.
1

PRODUCT INNOVATION
Crowdsourcing and other social product innovation techniques are made possible
because of big data. It is now possible to transform hundreds of millions of rich
tweets, a cacophony of unstructured data, into insights on products and services
that resonate with consumers. At the heart of this work is the ability to use
sophisticated machine-based computational linguistics to conduct temporal
sentiment analysis on a company's product portfolio and its customers. The
resulting output informs product marketing and product innovation strategies.
Data, and the related analytics, is also becoming a standalone product.
Technology and analytics firms have emerged to provide rich insights from data—for
example, compiling and analyzing transaction data between retailers and their
suppliers. Retailers that own this data, and more importantly the analytics, can use
it to improve operations, offer additional services to customers, and even replace
third-party organizations that currently provide these services, thus generating
entirely new revenue streams.
Finally, imagine the potential big data brings to running experiments—taking a
business problem or hypothesis and working with large data sets to model,
integrate, analyze, and determine what works and what doesn't, refine the process,
and repeat.
2

OPERATIONS
Supply chain data offers a variety of information-rich interactions, including physical
product movements captured through radio frequency identification (RFID) and
microsensors. The scope extends across suppliers, manufacturing sites, customers,
and in-service partners and results in reduced inventory, improved productivity, and
lower costs.
Such opportunities are reserved for those who understand that data is an asset to
be capitalized upon. Allowing customer information to stagnate in a storage area is a
wasted opportunity. And the value of all data is not so much the information
captured but how the information is viewed from the customer's perspective. This
means considering not only the impact on profits, but also how changing customer
preferences affect the market place. For example, when considering the effect of a
product promotion, collecting data on competitors' promotions, specifically on
substitute products, can reveal how customer preferences have evolved.
3

Transforming analytical capabilities and big data platforms begins with a well thought-out, three-pronged approach.
STEPS OF INTEGRATION
Identify where big
data can be a game
changer
•What key business and functional
capabilities are required?
•What IT capabilities are needed to
support and grow the business?
•Where are the major gas in
capabilities to support the
business?
Build future-state
capability scenarios
•What are the options for future
business capabilities and
technologies?
•How do the options compare for
capabilities, costs, risk, and
flexibility?
•What functional, analytical, and
technology decisions are needed
to support these capabilities?
Define benefits and
road map
•What is the investments payback
period?
•What is the implementation road
map?
•What are the key milestones?
•What skills are needed? Where are
the talent gaps?
•What are the risks?
•What is the third-party
engagement strategy

A traditional retailer company had not seen positive same-store sales for years, and the market was getting
more competitive. A member of the executive team complained that "online retailers are eating our lunch."
Poor economic conditions, changing consumer behaviors, new competitors, more channels, and more data
were all having an impact. There was a strong push to move aggressively into e-commerce and online
channels. The retailer had spent millions of dollars on one-off projects to fix the problems, but nothing was
working. Several factors were turning the company toward competing on analytics, from competitors'
investments and a sharp rise in structured and unstructured data to a need for more insightful data.
How did a big box retailer utilised these steps?

Identify where
big data can be a
game changer
For the big-box retailer, new capabilities were needed if the business had any
chance of pulling out of its current malaise and gaining a competitive
advantage—the kind that would last despite hits from ever-changing, volatile
markets and increased competition. The team engaged all areas of the business
from merchandising, forecasting, and purchasing to distribution, allocation, and
transportation to understand where analytics could improve results. Emphasis
was placed on predictive analytics rather than reactive data access. So instead of
answering why take-and-bake pizza sales are declining, the retailer focused on
predicting sales decline and volume shifts in the take-and-bake pizza category
over time and across geographic regions. In another example, the business
wanted to move from reacting to safety issues to predicting them before they
occur. The retailer planned to use social media data to "listen" for problems,
which would not only make the company more customer-centric but also provide
a shield to future crises. The plan was to set up a business-information
organization with four goals in mind:
1. Deliver information tailored to meet specific needs across the organization.
2. Build the skills needed to answer the competition, today and tomorrow.
3. Create a collaborative analytical platform across the organization.
4. Gain a consistent view of what is sold across channels and geographies.

Build future-
state capability
scenarios
The retailer was eager to develop scenarios for future capabilities, which were
evaluated in terms of total costs, risks, and flexibility and determined within the
context of the corporate culture. For example, is the business data driven, or is
the company comfortable with hypothesis-based thinking and experimentation?
Both are the essence of big data. They have also identified trade-offs for each
scenario, including comparison of capabilities, migration priorities, and timeline
estimates. For example, which is most effective: a global data topology at
headquarters or a local-regional-global combination? For a big data go-forward
architecture, what are the trade-offs in using Hadoop versus Cassandra? These
were assessed in the context of crucial opportunities, such as leveraging leading-
edge technologies and providing a collaboration platform, integrating advanced
analytics with existing and go-forward architecture, and building a scalable
platform for multiple analytic types. This technology would enable five key
capabilities and serve as the basis for future benefits:
– Predict customers' purchasing and buying behaviors.
– Develop tailored pricing, space, and assortment at stores.
– Identify and leverage elasticities, affinities, and propensities used in pricing.
– Optimize global sourcing from multiple locations and business units.
– Devise models to suggest ways to reduce energy use and carbon emissions.

Define benefits
and road map
Armed with these capabilities, the next questions revolved around resources. Did
it make financial sense to assign internal resources? Or would it be more cost-
effective to have external resources provide the big data analytics, at least
initially? Naturally, the decision would depend on the company's capabilities.
Technology needs were planned from two perspectives: data and architecture. A
data plan was charted from acquisition to storage and then to presentation using
a self-serve environment across both structured and unstructured data. Systems
architecture, which may involve a Hadoop-based integration, was planned in light
of the existing IT architecture, which was heavily reliant on relational data
warehouses leveraging Teradata and Oracle platforms. A road map outlined a
multi-million dollar investment plan that would deliver a positive payback in less
than five years. The company is now positioned to realize four key benefits from
its big data strategy:
– Deliver consistent information faster and with less expense.
– Summarize and distribute information more effectively across the business
to better understand performance and opportunities to leverage the global
organization.
– Develop repeatable BI and analytics instead of every group reinventing the
wheel to answer similar questions.
– Generate value-creating insights yet to be discovered through advanced
analytics.

DATA DRIVEN
BUSINESS
1. Five Pillars of a Data driven
Strategy

Organizations do not
need a Big Data
Strategy; they need a
Business Strategy that
incorporates Big Data.
Bill Schmarzo

Data has quickly become one of the most
important aspects of achieving a competitive
advantage in business. Creating a business where
data are leveraged to create real value is the
ultimate goal. A data-driven strategy is one where
data is a basic requirement for business, a value-
generator rather than a cost line-item of the
balance sheet. There are five pillars of a data-
driven business that serve as a framework for
creating real value from business data.

Articulate a
data
strategy
which serves
the strategic
imperatives
of the
business.
PILLAR 1
The future vision for a business should be at the forefront in defining how data are leveraged to create
value. A well-defined data strategy must start with business strategy. Tying a data strategy to the most
important company initiatives allows the business to focus its advanced analytics efforts and
technology choices on the areas that will provide the most value for the firm. A data strategy enables
data-driven decision-making using technology and applications that help a business achieve its
strategic imperatives.
Data strategy must be driven by the business not by the technology that services the business.
Focus first on what is driving your business, then move to defining the tactical elements of the data
strategy.
Data strategy must be clearly articulated and communicated to employees at all levels of the
organization so that your business as a whole can understand the importance of your data to
creating value.
Data strategy should be based on measurable outcomes and milestones. Clear steps with time
frames to get from the current state to desired outcomes are laid out and communicated across the
organization. If you cannot define a clear path to executing the strategy, then you don’t have the
right one.

Promote, train
and enforce a
culture of
“data-driven-
ness.”
PILLAR 2
The most successful businesses create a culture of data, one in which data drives decision-making.
They create a culture of measurement and adjustment based on data and analytics.
The culture is focused on educating the entire organization to appreciate the value which can be
generated through data. Businesses teach employees how to ask the right questions of data in order
to understand how data will relate to unique jobs and goals. A shared understanding of data and its
value helps create consensus and consistency and avoids analytical output being viewed
skeptically by the business.
The culture of decision-making leverages advanced analytics as its foundation. Businesses should
create a continuous cycle throughout the organization of evaluating impact and changing based on
the data and outcomes.
The culture of predicting outcomes and results through predictive analytics becomes the norm.
Continuous improvement includes feeding prediction errors back into predictive models for
continuous refinement.
The culture becomes a mind-set that consists of continuous testing; continuous improvement;
weighing and prioritizing decisions; sharing data with others in the organization; and using
analytics to inform and influence others.

Address the
realities of
human- and
technical-
capital
requirements.
PILLAR 3
An honest recognition of a business’s capabilities for generating real value from data is imperative to
becoming a data-driven business. A business must leverage strengths, adapt skill sets and shore up
gaps. This includes both human capital (skills and expertise) and technical capital (technology, systems
and infrastructure).
Human capital capabilities
Ensuring the right human capital capabilities is paramount.
It makes little sense to spend money on expensive systems
without having the talent to derive substantial value from
those systems.
• While businesses often recognize the need to bring on
more expertise, they struggle with identifying which skill
sets are most critical when hiring and training. A
business should base skill-set requirements on the
data strategy roadmap, identifying the skill sets which
are critical to execution.
• A primary goal for the business should be to build a
deep bench of analytical professionals throughout the
organization. Professionals should not only know how
to run analysis and use the analytical tools at their
disposal but have the capability to think critically about
business issues, applying tools and methods to
sophisticated and sometimes abstract questions.
• Human capabilities and skill sets need to be backed up
by continuous training and development.
Technical capabilities
New technology solutions may be needed to enhance current
IT and communications capabilities. Businesses should be
open to investment if it is determined that new technology is
aligned with the data strategy and will generate value.
• Businesses should avoid implementing sophisticated IT
systems until the business is prepared to leverage the
features provided by the systems. This includes having the
required data strategy, analytics talent, institutional will
and data-sourcing to allow the business to realize the
value that the technology can provide.
• Attention should be paid not only to back-end
infrastructure but also to data reporting, communication
and visualization tools. Effective reporting tools should
streamline data collection while simplifying query
functionality, allowing employees to more easily access
and refer to particular data.
• Significant consideration should be given to eliminating
data silos and centralizing data. Data is increasingly
powerful as it is brought together with other data, opening
the doors to today’s advanced analytics methods.

Maintain
creativity in
sourcing,
selecting and
prioritizing
data types.
PILLAR 4
To best achieve powerful results from data, businesses need to source and select powerful
combinations of data. Sourcing external data to combine with internal data can yield impactful
analytics. It is especially important to not overlook sources of internal data that can give new and
proprietary insights.
A process for determining and ensuring data are accurate, timely and secure is critical. With-
out certainty that data is accurate, it will throw into question any insights generated.
Collect the right data to meet the needs of the business’s data initiatives. Choosing data based
on the data needs generated by initiatives provides several benefits:
Interesting data is not always the most useful. Grounding data-sourcing in the
initiatives makes it easier to discern between the two.
There are near unlimited sources of data, so focusing on just the data that will meet
the needs of specific initiatives allows the business to home in on value-generating
activities.
The data currently being collected might not be the best data for the business needs.
Understanding how the current data reconciles with the data needs allows the
business to adjust which data are being collecting and how it is being collected.
Don’t overlook the potential value of unstructured data such as text, voice and other
under-utilized data types. Advanced data mining techniques, natural language
processing and text analytics allow for this information to be used in powerful ways.
Consider the power of data from unconventional sources when combined with the
firm’s own data. For in-stance, sensor data from smart devices or data from Web and
social media are examples of potential useful data that could be powerful additions
to a business’s data strategy and associated analytics initiatives.

Maximize the
value of data
while
maintaining
high levels of
data security,
quality and
agility.
PILLAR 5
Businesses often recognize the need to use data but struggle when it comes to implementing a
company-wide data strategy. This often results from a poorly-defined data governance structure. It is
easy for a business to fall into one of two camps: 1) data and analytics initiatives are kept structurally
separate from the firm’s ongoing operation, often in their own division or department, hindering the
ability for the business to create a culture of “data-driven-ness;” or 2) all data and systems are open
company-wide, which leads to data quality and security issues.
Data and analytics should not be left entirely to data scientists and IT departments – they require technical
savvy and organizational coordination. To succeed, businesses need to embed data and analytics deep into their
organizations to ensure that information and insights are shared across business units and functions.
Businesses should identify how analytical decisions are currently being made. Examine how that decision-
making process can be reinforced and altered with data and feedback.
There should be clear understanding of who is accountable for facilitating any given analysis and leveraging its
insights. From executive-level to analyst, there should be no questions of ownership.
Firms need to effectively manage the supply and demand for analytics services across the business. This can
involve tracking departments or units that are consistently under-utilizing analytic capabilities, which will reveal
divisions that may be lagging behind in becoming data-driven.
Breaking down organizational walls between initiatives, workflows and employees can be key to combining
data in powerful ways. Data silos are often created by departments or units not just keeping their data techno-
logically separate but also structurally.
Pay attention to regulatory and compliance requirements, both to meet industry-specific requirements and to
ensure individual-level data meet the requirements defined by the business – client/customer expectations.

Creating a data-driven business requires
significant upfront and continuous effort. This
effort has proven, time and again, to bring about
benefits that far outweigh the costs. The five
pillars described here are the framework for
achieving that type of success but specific
application will depend on industry, vertical and
context.

DESIGNING DATA
BUSINESS MODELS
1. 6 main Data Driven Business
Models
– Product Innovators
– System Innovators
– Data Providers
– Data Brokers
– Value Chain Integrators
– Delivery Network
Collaborators
2. Another Perspective: Data
enabled business model
innovation

Situation
• Complimentary technology forces
of networked devices, mobile,
cheap data storage and data
analytics bring countless
opportunities for businesses to
learn about their customers wants
and needs, diversify their
products.
• The explosion of data also brings
opportunities to create new
business models with these assets
• Organizations that embrace big
data can create new opportunities
for strategic differentiation
Complication
• Organizations not directly in the
business of doing something with
data struggle to connect data
opportunities to their current
products and services
• This is usually caused by a lack of
strategy, a lack of capabilities or of
supportive processes and systems
• Often this results in failures of big
data projects or difficulty to
launch new initiatives due to
skepticism.
Question
• To help companies define data-
driven strategy, the following
questions need to be answered:
• What kind of data-driven
business models are emerging in
the market place today?
• What value can be created using
data to create a new business
model with data?
• What is the approach to create
and implement a new business
model with data?
• What aspects does an
rganization need to think of in
order to implement a new
business model with data?
Organizations are looking
to leverage the value that
lies within the data they
generate, process or
acquire.

Data-enabled differentiation Data brokering Data-based delivery networks
• The product is still the primary source of
value, but using data drom the product
is used to improve the product or
service offering
• Data-enabled differentiation is typically
a solo opportunity – products from a
single vendor are the dominant gateway
to the opportunity
• There are situations where company
data only provides sufficient value when
combined with other sources or the
company does not have the capabilities
to fully tap the opportunity on its own.
• When the opportunity cannot be tapped
by a single vendor with a single product,
data brokering opportunities arise.
• Multiple companies work together and
share data to tap data opportunities.
• Companies specialize in one or two
capabilities needed to enable the
delivery network.
Solo opportunities Collaborative opportunities
1. Product Innovators
enhance their products and
services with data
2. System Innovators
use data to integrate
multiple product types
3. Data Providers
gather and sell raw data
without adding too much
value to it
4. Data Brokers
gather and combine data
from multiple sources,
create additional value with
analytics and sell insights
5. Value Chain Integrators
share data with system-
integrator partners to extend
product offerings or reduce
costs
6. Delivery Network
Collaborators
share data to drive deal
making, foster marketplaces
and enable advertising
6 MAIN DATA DRIVEN BUSINESS MODELS

A format to explain the following business models
Business Model
Schematic view of
key elements of the
business model using
the business model
canvas
• Key activity
• Value Proposition
• Customers
• Data Repository
• Channels
• ….
Capability
Requirements
Indication of the
capabilities needed
to implement the
business model using
the four stages of
the data value chain:
• Data generation
• Data storage
• Data analytics
• Data usage
Characteristics
Key characteristics of
the business model
Example
Example of a
company that has
implemented this
business model
Value realized
What value dos the
example company
derive from the
business model?
EXAMPLE

Business Model
Capability Requirements
Characteristics
Example: Tvilight
1. Product Innovator
Key Activity
Data Repository
Value Proposition Customers
Data Generation Data Storage Data Analytics Data Usage
Usage or sales data from a single product type from a single vendor is used to add features
to the product, improve the service offering or to create an additional product
Tvilight is a Dutch start-up that has developed a smart streetlamp system. Lamps only light
up in the presence of a person, bicycle or car, and remain dim the rest of the time.
• Key activity (1) of Tvilight is the designing and manufacturing of embedded streetlamp
sensors
• The main value proposition (2) is a sensor-enabled wireless streetlamp which is sold to
municipalities (3), enabling the customer to reduce their energy costs by 80%
• Monitoring data from individual streetlamps is sent wirelessly to Tvilight’s Data
Repository (4).
• The data is used in a new value proposition (5) that improves the service offering: web-
based software for remote monitoring, management and control of street lighting
infrastructures
Value realized:
• The functionality of the original product (street lamp) is improved by sensors and
wireless communication
• Usage data gathered from the product is used to create a second value proposition
(software for remote management)
1 2 3
4 5

Business Model
Characteristics
Example: Nike+
2. Systems Innovator
Key Activity
Data Repository
Value Proposition Customer
Relationship
Looks beyond a single product category to a broader smart systems offering – different
product types from a single manufacturer are architecturally related and can interact in
order to deliver value to the customer.
In 2006 Nike introduced a new range of personal tracking and measurement products.
• Key activity (1) of Nike is to manufacture sports apparel
• Value proposition (2) delivered to customers (3) is a range of related products: A running
app for mobile phone, network-enabled tracking bracelet and sports watch.
• Product usage data is sent to Nike via mobile (4) and stored (5)
• The data is communicated to the user through the Nike+ Platform (6), where the athlete
can track and analyze its sporting activities and share them with others.
• The Nike+ Platform provides a new channel to stimulate product sales in a context-
specific way, or enable third-party advertising
• Customer engagement is realized by community building and allowing the user to share
personal achievements on social media
Value realized:
• Customer lock-in – Products gain utility when combined, switching costs are high
• Customer engagement - social media integration, community
• New channel to sell and promote products (Nike+ Platform)
1 3
5
Customers
Channel
2
6
4

Business Model
Characteristics
Example: Vodafone
3. Data Provider
Key Activity
Data Repository
Value Proposition Customers
• In addition to the company’s core activity, raw data or aggregated data from its data
repository are sold to another business customer for a fee or a share of the earnings.
• Two types can be distinguished: Raw data sales and sales of insights/benchmarking
Since 2012 Vodafone sells anonymized raw network data to a partner company (Mezuro)
for a fee.
• Key activity (1) of Vodafone is providing telecom services.
• Value proposition (2) delivered to customers (3) is voice call, text message and internet
service through the company’s mobile network.
• Mobile phone usage data (4) is collected as part of the company’s core activity
• Data (5) about the geographical location of the company’s mobile sites is added to the
mobile phone usage data
• The dataset is anonymized by hashing (6) and sold to a partner company, Mezuro (7) for
a monthly fee
• Mezuro uses the data in addition to other sources to provide crowd analytics to the
public sector, estimating the usage intensity of city centers, train stations and roads
Value realized:
• Predictable revenue stream by using a subscription based model to sell data
• Access to a new market / customer segment
1 2 3
4
5
76

Glooko developed a blood glucose level logbook and analysis app based on existing blood
glucose data stream.
• Key activity (1) of Glooko is database management and analytics
• Glooko licenses the data specs and standards from glucose meter manufacturers (2) to
make its product compatible
• First of the value propositions is a link cable (3) that is sold to diabetes patients (4) to
connect their phone to their blood glucose meter
• Blood glucose meter data from the patient (4) is transmitted by the patient’s phone and
added to a meter reading database (5)
• Second part of the value propositions (6) is a log book and incidence reporting solution
that is delivered through a free app to patients (4) and for a subscription fee to hospitals
(7)
Business Model
Characteristics
Example: Glooko
4. Data Broker
Key Partners
Data Repository
Key Activity Value
Proposition
• Companies acquire data from key partners, from open sources or through data mining.
• The Data Brokering company focuses on excellent Data Analytics and Data Usage and
leaves Data Generation to others
Value realized:
• Complimentary products are sold to the customer - a mobile app and a cable to link
blood glucose meters to a mobile phone
• A predictable revenue stream is generated by offering a subscription service to hospitals
• Better effectiveness for hospitals and insurance companies
2
5
Customers
1
6
3 4
7

DuPont and John Deere are collaborating to deliver near real-time field level data to
farmers – supporting decision making related to planting, field management and harvesting
to maximize crop yields .
• John Deere’s key activity is manufacturing farming equipment (1)
• DuPont’s key activity is selling seeds and agricultural consulting (2)
• Both companies cater to the same customer segment: farmers (3)
• Value proposition of John Deere is farming equipment outfitted with sensors, GPS and
wireless transmission technology (4)
• John Deere equipment gathers data on crop yields, moisture and location, which is sent
wirelessly to a data repository owned by Deere (5)
• DuPont integrates John Deere’s data (6) in its value proposition (7), precision farming
software that uses field-specific data to support decision making
Business Model
Characteristics
Example: John Deere & DuPont
5. Value Chain Integrators
Company 1: John Deere
• Companies that serve the same customer segment exchange data with distributors and
system-integrator partners with the aim to extend the existing product offering or reduce
costs
• The business model is not geared towards sales or licensing out data, but rather towards
integration to optimize operational results
Value realized:
• Products from both companies gain utility by sharing data
• Risks and revenues are shared and individual competitive advantage is improved
• A barrier to competition is created, because use of product generated data allows to
offer services more intelligently than competitors
1
7
Customers
3Company 2: DuPont
5
4
26

The delivery network depicted to the left is an example enabling advertising:
• KLM (light blue), an airline company, (1) sells flights (2) to travelers (3)
• Booking data is stored in a database (4), combined with flight scheduling information (5)
and shared with an advertising agency
• The advertising agency (green) (6) can identify the traveler through a tracking cookie and
determines the date and destination of the traveler’s flight (7)
• Hertz (black), a car rental company (8), is looking to rent cars to travelers (9)
• Hertz shares data on available cars with the advertising agency to add to its algorithm
(10)
• The advertising agency then shows available rental cars through websites that the
consumer visits (omitted), on the city and date that the traveler will arrive there
Business Model
Characteristics
Example: John Deere & DuPont
6. Delivery Network Collaborators (1/2)
• Stakeholders work together in a value creating network rather than a traditional value
chain. Often it is unclear who is the vendor and who is the customer or consumer, all
stakeholders benefit
• Companies share data to drive deal making, enable advertising and foster marketplaces
Value realized:
• Hertz obtains a new channel to reach consumers
• A fee is paid by Hertz to the advertising agency and to KLM each time a consumer clicks
the ad to rent a car
2
Customers
3
Company 1: KLM
1
9
Company 3: Hertz
8
7
Company 3:
Advertising Agency
6
10
5
4

Kaggle has made a delivery network business model, crowdsourcing data problems from
businesses to a community of data scientists.
• The two key activities for Kaggle (dark blue) are fostering a community for data
modelling competitions and connecting companies to top data scientists
• Company 1 (black) pays Kaggle to organize a data modelling competition. It provides raw
data and the challenge and receives the winning data models
• Company 2 (light blue) pays Kaggle for matchmaking to the community’s top data
scientists
• Kaggle’s community of data scientists (green) partakes in competitions to solve data
problems
Business Model
Characteristics
Example: Kaggle
6. Delivery Network Collaborators (2/2)
• Stakeholders work together in a value creating network rather than a traditional value
chain. Often it is unclear who is the vendor and who is the customer or consumer, all
stakeholders benefit
• Companies share data to drive deal making, enable advertising and foster marketplaces
Value realized:
• Kaggle – Fees through competitions and matchmaking
• Company 1 – Gets solution to data problem
• Company 2 – Finds skilled data scientists
• Data Scientist community – Exposure, connect with other experts, prize money for the
top data scientists
Kaagle Corp
Kaagle Community
Company 1
Company 2
Kaagle
Competition
Kaagle
Connect
Data Scientist Winning Model
Scientist
Top 0.5%Exposure
Fee
Raw Data + Challenge
Data Models
Winning
Model
Prize
Money
Raw Data+Briefing,
Money
Data Model
Fee
Find best data scientist
Community Access

Value
Clients
Existing
Existing / new offerings
New
Offering
Category
Systems
Data
Pricing
Unit based
Volume based
Activity based
Value based
Channels
Integrated value chain
Delivery network
• Deep sell: selling more of current offerings to existing clients
• E.g: Internal supply optimizations, data-enabled replenishments
• Cross sell: Data-enabled sales of new offerings to existing clients
• E.g. Amazon, Bol.com, (“other customers also bought…”)
• New sell: data-enabled sales of new offerings to new clients
• E.g. Insurance companies, banking services, online retailers
• Added functionality to existing product categories
• E.g. Smart metering, intelligent lighting
• Combined offering categories, potentially in ecosystem
• E.g. Lifestyle devices (Nike+, iPod with Itunes, FitBit)
• Commercialization of data through provision or brokerage
• E.g. Financial information (Experian); usage statistics (Vodafone)
• Dynamic pricing per unit based on economic modeling
• E.g. airline ticketing, online advertising
• Pricing based on (expected) volumes
• E.g. Quantity discounts (, freemium models (Spotify, LinkedIn)
• Pricing on (expected) time & material
• E.g. Engineering & Installation companies; service organizations
• Pricing based on client’s (expected) valuation
• E.g. Stock markets; Telecom companies
• Data enabled partnerships providing an extended offering
• E.g. Tomtom & Apple; John Deere & DuPont
• Data enabled delivery network to distribute content / products
• E.g. KLM & Hertz, Kaggle
ANOTHER
PERSPECTIVE:
Data has the
ability to
transform
business models
at many different
levels. 
Data enabled
business model
innovation

www.smartdata.howwww.facebook.com/smartdatasr

Module 4 - Data as a Business Model - Online

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