Data Monetization

1. Authored by:
Radhakrishnan R
Data Monetization
Is Data the new Currency?
A Point of View

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Data Monetization
Is Data the new Currency?
Contents
INTRODUCTION ....................................................................................................................................2
UNDERSTANDING PLAYERS IN THE DATA ECONOMY ...................................................................................3
THE DATA VALUE STAGES ......................................................................................................................3
THE DATA MONETIZATION FRAMEWORK..................................................................................................4
DATA MONETIZATION USE CASES ...........................................................................................................6
CONCLUSION .......................................................................................................................................7
ABOUT INFOGAIN .................................................................................................................................7
REFERENCES ........................................................................................................................................7

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Introduction
A new type of asset is emerging in the digital economy – ‘Data’.
Companies are dealing with humungous volumes of customer data from various sources. This data is
further collected, reported and analyzed. Analyzing and using data is nothing new, it is a well know way
to enhance efficiency, build relationships, and convert opportunities. The concept is gaining more traction
now because digital technologies enable massive data collection along with the power to analyze data
effectively. This has led to a new paradigm, Data Economy that uses data to generate more value.
As defined by Wikipedia, “Data monetization, a form of monetization, is generating revenue from
available data sources or real time streamed data by instituting the discovery, capture, storage, analysis,
dissemination, and use of that data.”
Data Monetization to put it simply is the process where an organization sells data that is generated by
interactions with their customers and business partners. This brings in the potential of a new revenue
stream based on information based offerings.
While the concept of data monetization excites CXO’s, there are several questions that will need to be
considered. This includes:
 Where do I start?
 What is my data asset value as of today?
 Is the data unique to my business?
 Who is the target data consumer?
 What are the use cases that can be mapped to the data I have?
 Will I run into privacy issues?
 What are the risks and investments?
 How do I start?
 Do I have the infrastructure?
 Do I have resources and skills?
 Do I have the data that is ready consumable by the end user?
 Does my data provide insights into consumer behaviour?
 Can I transform the raw data into a usable and scalable format?
The above questions will definitely need to be answered and detailed out. A methodological approach,
with the right business model and the right partner is necessary to operationalize the Data Monetization
dream.

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Understanding Players in the Data Economy
Before getting into the approach, let’s understand the players involved in the Data Economy.
The Data Producers are the players who generate
and control the data. The data can be collected
through various channels. The Data Aggregators
collect data from disparate sources to uplift the
data. The Data Insight Providers provide additional
insights by using various statistical and
computations methods, machine learning,
semantic models and other techniques. The
Platform Providers provide the infrastructure to
host the data. In addition, they also provide the API
infrastructure to directly access the data. The Data
Presenters focus more on the user engagement
and user experience. Data Consumers are the ones that consume or buy this data.
There is a possibility of one player playing multiple roles. The key question to handle is what role do you
want to play in this market?
The Data Value Stages
Organizations over a period of time have collected and analyzed huge volumes of data. The value your
data can provide to Data Consumers can be best understood with the model of ‘Data Value Stages’.
Stage 1: The journey of data monetization starts with an organization offering the transaction data. The
data is raw with no additional processing. Examples
of such data include retail POS, clinical health. This
is an area where an organization can start with
limited investment.
Stage 2: This stage requires processing and uplifting
of data from its raw state into a more consumable
form. Some of the activities in this stage include data
transformation, data enhancement, data
manipulation, developing taxonomies, business
rules etc. This stage will require an investment.

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Stage 3: Going up the stack is the stage that adds analytical capabilities to the data that can support in
business decisions. Various insights can be offered on the data by applying machine learning, statistical
modeling and other analytical techniques. This stage will involve data scientists.
Stage 4: This stage provides a full blown solution that will present insights in an intelligent format.
Additionally, this will also have a robust API gateway for consumers to access data. This will require
substantial investment with focused sales and support. This stage offers high risk and high reward.
By assessing your current data and mapping to your monetization strategy, one can decide to take the
right position. The more insights the data can offer, the more valuable it gets. The Data Value stages are
demonstrated for representative purposes only. Organizations may choose to select the stage(s) based
on their market needs.
The Data Monetization Framework
Data Monetization is about converting the enterprise data into profits. The transition from raw data to
offering a full solution is not a straight line. While it is understood that technology plays an important
role in collecting, storing, analyzing and delivering data, the most important point to consider is having
the right strategy and business model that helps to effectively monetize data and create a competitive
edge. A ready reckoner framework for data monetization is provided below:

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Data Monetization focuses on solving problems that create a high utility value to end customers who
could not have generated this on their own. Data Consumers move towards providers who can provide
these valuable insights The top question that Data Owners should strive to solve is “what problem can
my data solve?” In addition, Data Owners must also consider the following aspects:
 What? - The value your data can provide
 Who? - The potential buyers of the data
 Why? - The reason for getting into the data monetization play
 How? - The strategy to get into the play
Organizations must also weigh the pros and cons of entering the data economy market as shown below:
An important aspect to consider in this play is Data Privacy. Data Monetization involves analysis of
customer data (demographics and personal) and deriving insights. Data providers must exercise extreme
caution while dealing with privacy. They must ensure the necessary security and encryption to protect
customer data based on the local laws. Additionally, they must have a proactive policy that encourages
customers to share their data. Customers can be incentivized to share the data. The policies must be
clearly defined and data should be shared only after mutual consent.
As enterprises start addressing more data consumers, there will be a need to provide data in different
ways for difference audiences. This makes it necessary to have an evolving and dynamic data
monetization framework.

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Data Monetization Use Cases
The following are some representative use cases for data monetization:
No. Data
Producer
Data Consumer Usage Scenarios
1 Banks and
Financials
Companies
Merchants Data owned by banks can generate insights on
the transactional data of customers. The offers
provided by merchants can be pushed through
the bank channels. Data Consumers can expect
a better conversion rate and Data Producers
can get commission on conversion
2 Retailers Fashion Design Firms Retailers who deal directly with customers
have insights into what designs customers
prefer
3 Telecom
Providers
Retailers, Restaurants Telecom providers have the GPS data of mobile
users. Retailers can push their marketing offers
in real time. As the offer is more relevant, the
Data Consumers expect a better conversion
and Data Producers can get commission on
conversion
4 Utility
Companies
Insurance Companies,
Retailers
Utility companies have customer’s home
attributes data such as temperature,
application usage. These insights can help data
consumers like home insurance companies,
retailers in several ways
5 IoT Devices
Retailers, Pharmaceutical
Companies, Insurance
Companies, Automobile
Companies
With IOT, it is possible to get location (GPS
Coordinates), vehicle (driving speed, breaking
force, diagnostic information) and living data
(blood pressure heart rate, temperature. This
information when applied to various scenarios
can provide opportunity for monetization
6 Travel
Companies
Retailers, Airlines, Restaurants
(F&B)
Travel companies possess the itinerary
information, the travel contracts and policies
for corporate customers that can be of high
value to the listed consumers
7 Insurance
Companies
Medical Research Groups,
Pharma Companies, Hospitals
Analytics when applied on patient data can
generate insights that are valuable to these
sectors

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Conclusion
The digital economy generates huge amounts of customer data. Data is inarguably a core asset. The value
of data that provides signals of opportunities, risks etc. is best determined by the data owner. Enterprises
have often considered this an option to gain a competitive advantage.
The process of rolling out a data monetization plan is complex. The focus for the data monetization
strategy should be on the defining the value of data to the end customers rather than getting influenced
by technological aspects. Further, the cost of capturing and storing this data is significant. However, there
is an opportunity of monetizing this valuable data. Companies will need to evaluate their current structure
and overall positioning by taking a systematic approach. The success depends on the business model,
adopting the right framework, commitment, connecting with the right partners and of course the quality
of data provided.
About Infogain
Infogain provides front-end, customer-facing technologies, processes and applications that lead to a
more efficient and streamlined customer experience for enterprises in the US, Europe, the Middle East,
Asia Pacific and India. Offering solutions for the high-tech, retail, insurance, healthcare and travel &
hospitality verticals, Infogain specializes in areas such as software product engineering, digital service
automation, cloud, mobility, testing and business intelligence & analytics. The company has 9 delivery
centers and more than 4000 employees globally. Infogain has a customer retention rate of 90%+ over a
five-year period.
References
 https://en.wikipedia.org/wiki/Infonomics
 https://en.wikipedia.org/wiki/Data_monetization