discuss this article (comments from your own experience, critique, insights)

December 201

3

(12:4) | MIS Quarterly Executive 213

MISQUarterly
Executive

Data Monetization in the Supply Chain1,2
Data is now being created and transferred at an unprecedented rate, fueling the growth

in business intelligence and analytics (BI&A)3 to discover opportunities for improving and
innovating in supply chains and to enhance supply-chain collaboration.4 In retailing, new
supplier/customer ecosystems are emerging in which BI&A services are offered through a
supplier portal, which can be cloud-based. Cloud-based BI&A platforms allow retailers and
their suppliers to share data and analytics, often for a price. Or a company may monetize its
data by exchanging it for other benefits (e.g., merchandising benefits). These data-sharing
ecosystems often involve new players (e.g., public cloud platform providers and/or third-party
data coordinators, negotiators or analysts).

Many companies would like to monetize their data. Data monetization is when the intangible
value of data is converted into real value, usually by selling it. Data may also be monetized by

1 Cynthia Beath, Jeanne Ross and Barbara Wixom are the accepting senior editors for this article.
2 An earlier version of this article was presented at the pre-ICIS SIM/MISQE workshop in Orlando, Florida, in December 2012.
We are grateful to Omar El Sawy and other participants at the workshop for their insightful comments. We would also like to thank
the anonymous retailer and big data analytics company that provided so much time and insight concerning their experiences with
monetizing big data.
3 For background information on big data and BI&A, see Chen, H., Chiang, R. H. L. and Storey, V. C. “Business Intelligence
and Analytics,” MIS Quarterly (36:4), 2012, pp. 1165-1188; Hopkins, M. S., LaValle, S., Lesser, E., Shockley, R. and Kruschwitz,
N. “Big Data, Analytics and the Path from Insights to Value,” Sloan Management Review (52:2), 2011, pp. 21-32; and Wixom, B.
H., Watson, H. J. and Werner, T. “Developing an Enterprise Business Intelligence Capability: The Norfolk Southern Journey,” MIS
Quarterly Executive (10:2), 2011, pp. 61-71.
4 For a discussion on BI as an IT capability for supply-chain collaboration, see Rai, A., Im, G. and Hornyak, R. “How CIOs Can
Align IT capabilities for Supply Chain Relationships,” MIS Quarterly Executive (8:1), 2009, pp. 9-18.

Data Monetization: Lessons from a
Retailer’s Journey

In today’s era of big data, business intelligence and analytics, and cloud computing, the
previously elusive goal of data monetization has become more achievable. Our analysis
of a four-stage journey of a leading U.S. retailer identifies the potential benefits and
drawbacks of data monetization. Based on this company’s experiences, we provide
lessons that can help other companies considering data monetization initiatives.1,2

Mohammad S. Najjar
University of Memphis (U.S.)

William J. Kettinger
University of Memphis (U.S.)

214 MIS Quarterly Executive | December 2013 (12:4) misqe.org | © 2013 University of Minnesota

Data Monetization

converting it into other tangible benefits (e.g.,
supplier funded advertising and discounts),
or by avoiding costs (e.g., IT costs). Potential
buyers of an organization’s data include a direct
supplier, an upstream supply-chain partner, a
data aggregator, an analytics service provider or
even a competitor. Three current IT trends are
enhancing the potential for data monetization: big
data, BI&A and the cloud.

Retail firms, with their exacting merchandising
strategies and tight supply-chain relationships,
have taken the lead in demonstrating that
monetizing data can provide a significant revenue
stream and be an IT cost-sharing mechanism.
Point-of-sale, consumer-loyalty and inventory
data can be sold to suppliers, and some of the cost
of analyzing a retailer’s data can be recovered
from its suppliers.

Research has shown that data sharing in the
supply chain improves supply-chain performance.
Suppliers typically are interested in using a
retailer’s point-of-sale data to enhance planning
and better manage inventory, thus reducing
the bullwhip effect5 (i.e., the phenomenon of
demand variability amplification). Manufacturers
can use downstream data about retail sales to
improve product design, optimize operations and
develop fact-based marketing and promotional
campaigns. The availability of sales data to the
supply chain means that demand can be more
accurately forecast and, hence, inventory levels
can be better predicted; in some cases, assemble-
to-order can be achieved. Some suppliers may
even use such data for strategic decisions by
looking for product affinities to make merger or
acquisition decisions.

Furthermore, data sharing can be a strategic
tool in managing supply chains and channel
relationships; sharing consumer or market data
with supply-chain partners can influence their
behavior.6 Nevertheless, a company must decide
5 See Lee, H. L., Padmanabhan, V. and Whang, S. “The Bullwhip
Effect in Supply Chains,” Sloan Management Review (38:3), 1997,
pp. 93-102.
6  For more discussion on the benefits of data sharing in the supply 
chain, see Zhou, H. and Benton Jr., W. C. “Supply Chain Practice and
Information Sharing,” Journal of Operations Management (25:6),
2007, pp. 1348-1365; Eyuboglu, N. and Atac, O. A. “Information
Power: A Means for Increased Control in Channels of Distribution,”
Psychology & Marketing (8:3), 1991, pp. 197-213; Waller, M.,
Johnson, M. E. and Davis, T. “Vendor-Managed Inventory in the
Retail Supply Chain,” Journal of Business Logistics (20:1), 1999, pp.
183-203; and Lee, H. L., Padmanabhan, V. and Whang, S., op. cit.,
2004, pp. 1875-1886.

whether and when sharing its data with suppliers
and other partners will pay off. The benefits a
data-sharing strategy will have for the overall
supply chain and distribution channel must be
balanced against the benefits of holding data
close to the chest.7 While the improvement in
supply-chain performance might be a good reason
for companies to share data with supply-chain
partners, a more explicit direct dollar value of the
data can be another tempting motivation.

There are several challenges in involving
suppliers in monetizing data. Selling data
to suppliers may eliminate the competitive
advantage that can be gained from asymmetric8
information. Contracts have to be carefully
prepared to ensure the data sold or shared is
used for the mutual benefit of the firm and its
partners. Trust has to be nurtured. The privacy
and security of a company’s data may be at
risk if appropriate assurance practices are not
established. Data packaging has to be considered
to identify what data can be made available for
sale and in what format and at what price. Pricing
models need to be developed to take account
of the associated cost of making data available
and its value to the buyer. A company must
identify a suitable marketing model for its data.
Although data monetization best practices have
yet to be identified, this article describes how a
major U.S. retailer tackled these challenges. (The
research we conducted to create this case study is
described in the Appendix.)

Pathways to Data Monetization
Data monetization requires a strategic

choice on which of several pathways to
follow. It is important to assess the technical
(data infrastructure) and analytical (human)
capabilities of the company to determine which
strategic pathway a company should choose for
monetizing its data. The technical capability
includes the hardware, software and network
capabilities that enable the company to collect,

7  For more discussion on the benefits of data sharing in the supply 
chain, see: Zhou, H. and Benton Jr., W. C., op. cit., 2007; Eyuboglu,
N. and Atac, O. A., op. cit., 1991; Waller, M., Johnson, M. E., and
Davis, T., op. cit., 1999; and Lee, H. L., Padmanabhan, V., and
Whang, S., op. cit., 2004.
8 Information asymmetries occur when two people have different
information about the same thing. If one has additional inside
information, he or she can leverage or take advantage of that
information.

December 2013 (12:4) | MIS Quarterly Executive 215

Data Monetization: Lessons from a Retailer’s Journey

store and retrieve its data. The analytical
capability is the mathematical and business
analytical knowledge and skills of the employees
in the company or in supplier firms. A company
that has the data and the know-how (i.e., people
and BI&A) to use the data properly will have
an advantage in the era of big data. If both
capabilities are low, then the company has three
potential pathways to transition to the high
capabilities that will enable it to monetize its data
(see Figure 1).

Pathway 1: Move Direct to Higher Risk
and High Reward

This direct pathway can be a riskier path to
data monetization, as it requires simultaneously
building both technical and analytical capabilities.
As such, it requires the largest initial investment
of the three alternative pathways. To follow this
pathway, a company must invest in developing
its technical infrastructure while hiring and
training employees with the required business,
mathematical and analytical skills. While costly,
following this pathway will quickly position a
company to be ready for monetizing its data and
collaborating with supply-chain partners.

Pathway 2: Build Analytical Capability
First

Following this pathway, a company chooses
to develop its analytical capability first. This
requires training employees and/or hiring
business analysts with the required set of
business, mathematical and analytical skills. As
its analytical capability grow, the company may
leverage them by generating more data (from
internal sources) or buying data (from external
sources). But growing an in-house analytical
capability may not be sufficient to reach the
point where the company can demonstrate the
value of its big data and thus pave the way to data
monetization. It may also require the company’s
technical capability to be expanded. This pathway
requires a higher internal investment to develop
the in-house analytical capability.

Pathway 3: Build Technical Data
Infrastructure First

Instead of first developing its own analytical
capability, a company may choose to extend
or outsource its technical data infrastructure
to produce an attractive collection of data that
can be sold to suppliers. The creation of an
appropriate digital platform is a prerequisite for a
company and its suppliers to share data securely.
A company can build this platform internally or
use the expertise of a service provider; the use

Figure 1: Three Pathways to Data Monetization—Moving From Low-Low to High-High
Capabilities

Build both capabilities
internally or hire a third
party

Acquire (buy) data to
leverage your analytical
capability

Exploit suppliers’
BI&A

Monetize and dig
deeper collectively as
partners

Technical Capability

Analytical
Capability

Low

Low

High

High

1
2

3

216 MIS Quarterly Executive | December 2013 (12:4) misqe.org | © 2013 University of Minnesota

Data Monetization

of cloud-based infrastructure can increase the
flexibility, scalability and speed of developing
the platform. By building a platform that will
enable it to market its saleable data, a company
can more quickly monetize its data and possibly
avoid some analytical costs by leveraging the
analytical capabilities of its suppliers rather than
developing the analytical capability in-house.
This pathway maximizes the potential data
monetization pay-off because it enables sales of
data and reduces startup costs. However, it does
make the company more reliant on its partners as
major sources of analytics.

The Data Monetization
Journey of “DrugCo”

The case of “DrugCo,” a U.S.-based Fortune
500 drug retailer with several thousand stores in
more than half of U.S. states, illustrates a company
that has followed Pathway 3. This company, which
wishes to remain anonymous, is recognized as
being relatively mature in BI and data use, and it
has been monetizing its data for almost 10 years.
The case shows how cost and the willingness to
work with external parties and openly share data
were important issues that motivated DrugCo to
monetize its data.

Like other companies in the small-box retailing
sector, DrugCo has:

● Many retail locations with narrowly
defined geographical boundaries

● Limited shelf space
● Many stock-keeping units (SKUs) across

the company
● A diverse customer base
● Differing inventories within each location

to satisfy the local customer needs.

For DrugCo, data analysis is crucial for
accurately assessing marketing campaigns,
analyzing sales patterns, examining on-shelf
availability and inventory levels, and customizing
SKUs for each store based on its unique local
consumer demand.

We describe key events that took place in the
company, and we present a four-stage model
that illustrates the four key stages it went
through on its data monetization journey (the
stages are depicted in Figure 2). We also provide
lessons learned from DrugCo’s journey for
other managers as they grapple with their data
monetization decisions.

In Stage 1, Building BI&A capabilities, DrugCo
built its technical and analytical capabilities to
address internal business needs.

In Stage 2, Connecting to and sharing
information with suppliers, DrugCo connected
to its supply-chain partners and started to share
information with them through its cloud-based

Figure 2: DrugCo’s Four-Stage Data Monetization Journey

Stage

Benefits
(Value) of

Data
Stage 1:
Building
BI&A
capabilities

Stage 2:
Connecting
to and
sharing
information
with
suppliers

Stage 3:
Monetizing
data by
charging
for it

Stage 4:
Further
monetizing
data and
avoiding
analytical
costs by
leveraging
suppliers’
resources

December 2013 (12:4) | MIS Quarterly Executive 217

Data Monetization: Lessons from a Retailer’s Journey

supplier portal, hosted by 3PP (a third-party data
analytics firm that works with DrugCo and which
also wishes to remain anonymous).

In Stage 3, Monetizing data by charging for it,
DrugCo started selling its data to suppliers via its
supplier portal.

In Stage 4, Further monetizing data and
avoiding analytical costs by leveraging suppliers’
resources, DrugCo leveraged its suppliers’ data
analytical capabilities and avoided some of
the costs of its analytical function. This stage
continues to the present day.

The characteristics of the four stages are
described in Table 1. The stages differ in the
technical and analytical (especially in people)
capabilities the company required, the type of
trust9 built, the focus of DrugCo’s information
strategy, governance mechanisms, and the
costs incurred and benefits achieved by various
stakeholders. While there has been ample
discussion of the first two stages, we were
surprised by the third stage and even more
surprised by the fourth.

As DrugCo moved from one stage to the next,
the benefits realized from its data increased.
DrugCo’s data was monetized in the form of
revenue generated directly from selling the
data, as well as through a decrease in labor and
infrastructure costs for analysis. The company
also realized benefits from new business
opportunities associated with new analytical
insights and enhanced its collaboration with
suppliers.

Stage 1: Building BI&A Capabilities
The growth of DrugCo’s data sources

meant that its traditional databases, database
management systems and analytical tools became
slow and inefficient. DrugCo’s VP of Pharmacy
Services described this environment:

“The database … probably had about 1.2
to 1.3 million transactions a day and those
transactions were very long … there were
literally hundreds of fields on one of these
transactions that could be evaluated.”

In response, DrugCo improved its in-house
technical data capability by developing a data
warehouse and using basic data analytical tools

9 Trust is categorized into contractual, goodwill and competence;
see Sako, M. Prices, Quality and Trust: Inter-firm Relations in
Britain and Japan, Cambridge University Press, Cambridge, 1992.

(e.g., Microsoft Access and Excel). Limited,
functionally based BI capability was used to
analyze and understand the implications of
DrugCo’s data. Business users would attempt to
perform basic ad hoc queries and, when faced
with more complex or time-consuming analyses,
would ask the IT department for help. The
main focus of this stage was to use data to meet
business needs and solve internal problems.
DrugCo’s CIO described how limited capabilities
meant limited analyses:

“If it takes you 45 minutes or an hour to get
an answer… you’re probably not going to
do a lot with it. But if you can do it within
30 seconds or a minute or two, you are
more likely to do more analytics and what-
if cases.”

Because all data use was internal to DrugCo
during Stage 1, inter-organizational trust was
not an issue. Information was used to inform
internal stakeholders and to run the business
more efficiently. Data exploitation was judged to
be going well since problems were being solved
and new insights were being generated. Various
policies were enforced to maintain the internal
security and privacy of DrugCo’s data.

The data exploitation costs in this stage were
the technical cost of building the data warehouse
and connecting it to the reporting tools, and the
analytical cost of analyzing the data.

Stage 2: Connecting to and Sharing
Information with Suppliers

In Stage 2, DrugCo created a secure, cloud-
based portal for communicating with its
suppliers. The portal provided access to point-
of-sale, customer-loyalty and transactional data
(e.g., purchases from DrugCo’s suppliers) and
various BI&A applications. As an analytical data
warehouse platform, it allowed suppliers to work
with and analyze DrugCo’s data so the company
and suppliers could collaborate on mutual
business goals. DrugCo’s Senior Director of
Category Management Support (CMS) explained
the importance of the supplier portal:

“The great thing about this portal and
this information is [that DrugCo and its
suppliers are] working on the same set of
reports a lot of times and we’re using the
same information.”

218 MIS Quarterly Executive | December 2013 (12:4) misqe.org | © 2013 University of Minnesota

Data Monetization

Table 1: Characteristics of the Four Stages of Data Monetization

Stage 1: Building
BI&A Capabilities

Stage 2: Connecting
to and Sharing
Information with
Suppliers

Stage 3: Monetizing Data
by Charging for It

Stage 4: Further
Monetizing Data and
Avoiding Analytical
Costs by Leveraging
Suppliers’ Resources

Technical
Capability

Implementing data
warehouse with
basic analytical
tools

Developing a supplier
portal

Extending the
supplier portal with
data integration and
customized reporting
capabilities for data

Offering a scalable
data platform to
accommodate expanded
use of the suppliers’
analytical capabilities

Analytical
Capability

Internally focused,
limited functional
analytical capability

More fully developed
internal and inter-
organizational analytical
capability

Matured internal and
inter-organizational
analytical capabilities;
Learning what data is
saleable

Exploiting analytical
capabilities of suppliers

Type of Trust Not an issue, as
BI&A is internally
focused

Contractual trust Contractual trust;
Goodwill trust

Contractual trust;
Goodwill trust;
Competence trust

Information
Strategy

Informing internally Supply-chain
optimization

Revenue generation Information
transparency

Governance
Mechanisms

Basic performance
metrics;
Information
assurance

Information sharing
contracts;
Data presentation
mechanisms and
standards;
Non-disclosure
agreements (NDAs)

Pricing structure;
Data purchase agreement;
NDAs

Evaluation of supplier-
provided analytics

Achieved Benefits/Associated Costs

Achieved
Benefits
(DrugCo)

Data is used to meet
specific business
needs and solve
problems

Data is shared across
boundaries for supply-
chain efficiency

Data is sold to generate
monetary value and/or
share technical costs

Data is traded for
analytics to gain new
insights;
Cost savings and revenue
growth

Associated
Costs
(DrugCo)

Technical cost;
Analytical cost

Technical cost;
Analytical cost;
Contracting cost;

3PP’s fee

Contracting cost;
3PP’s fee

3PP’s fee

Achieved
Benefits
(Suppliers)

Refined BI&A using the
accessed data

Increased sales through
better understanding of
markets and DrugCo’s
business

Enhanced collaboration
with DrugCo;
Increased sales by shelf
monitoring

Associated
Costs
(Suppliers)

Analytical cost;
Contracting cost

Data cost;
Analytical cost

;
Contracting cost

Data cost;
Analytical cost

December 2013 (12:4) | MIS Quarterly Executive 219

Data Monetization: Lessons from a Retailer’s Journey

DrugCo owned the data it put on the supplier
portal, while 3PP offered data-analytics, data-
cleansing and consulting services, and owned
the portal infrastructure. DrugCo sent its data to
3PP, which cleansed it and then uploaded it to the
portal. Data security was enforced by preventing
suppliers from copying or downloading data
from the portal; they could only work with the
data while it was still on the portal. Once it was
connected with its suppliers, DrugCo had to
further develop its analytical capability so it could
respond to new inter-organizational analytical
needs, which imposed additional analytical costs
on DrugCo.

Trust is an important factor when external
parties are involved with data monetization. In
Stage 2, the data-sharing relationship between
DrugCo and its suppliers was still somewhat
immature. Non-disclosure agreements
(NDAs) were used to specify what suppliers
could and could not do with the data. These
agreements created contractual trust—a
mutual understanding between DrugCo and its
suppliers based on the agreements. DrugCo’s
Senior Director of CMS described the contracting
approach:

“We’ve limited the use of the data. It’s
specifically limited to the purpose of
growing the business of our company.”

3PP acted as a liaison between DrugCo and its
suppliers, providing value-adding activities by
hosting DrugCo’s data on the supplier portal, and
BI&A services, administrating the information-
sharing contracts, contracting directly with some
suppliers (e.g., alcohol suppliers, which legally are
not allowed to contract directly with DrugCo to
purchase its data) and managing different aspects
of the relationship, such as negotiating pricing of
DrugCo’s data.

During this stage, data was shared for supply-
chain optimization. The suppliers accessed part
of DrugCo’s data, analyzed it and were able to
enhance their marketing campaigns, production
planning, pricing and inventory management.

The governance of DrugCo’s supplier portal
was designed to be collaborative. Major suppliers
joined an advisory board that oversaw how
the supplier portal was implemented. Voting
was used to prioritize enhancements and to
determine data presentation mechanisms and
standards. The VP of Retail Solutions at 3PP

explained the structure and function of the
advisory board:

“[At any time] there’s around 18 to 20
suppliers on [DrugCo’s] advisory board
and there are eight that are on their senior
council … the larger group meets twice
a year and the senior group meets four
times a year … they prioritize the changes
or enhancements they want to see in the
program and pass them to DrugCo …
DrugCo is only a member … It’s a user-
driven advisory board.”

DrugCo’s costs during Stage 2 were the
technical cost of building the supplier portal,
the analytical cost for the additional inter-
organization analyses, and the contracting cost
for preparing contracts and NDAs with suppliers
and third parties. 3PP incurred the cost of
hosting the portal and providing additional
analytical services. Suppliers connected to the
portal also incurred contracting costs for the
NDAs and analytical costs for analyzing the data
they accessed. With direct access to the portal,
suppliers could dynamically manipulate vast
amounts of DrugCo data to answer questions on
the fly.

Stage 2 laid the technical foundation (i.e., in
the supplier portal) for data monetization and
showed that DrugCo’s data was valuable to its
suppliers.

Stage 3: Monetizing Data by Charging
for it

In Stage 3, with the supplier portal running
successfully and suppliers having a good feel for
DrugCo’s data and its value, DrugCo began to
extract more value from its data by monetizing it:

“They [retailers in general] accumulate
billions of records every year of point-of-
sales transaction data and they are taking
that huge amount of data and creating
their own commercial data clouds for
their suppliers to analyze … A consumer-
packaged-goods brand can just log in
and see not only how their own products
are doing in those stores but also how a
competitor’s products are doing in those
stores.” VP of Marketing, 3PP

The supplier portal was enhanced by adding
additional data sets (particularly loyalty data)

220 MIS Quarterly Executive | December 2013 (12:4) misqe.org | © 2013 University of Minnesota

Data Monetization

and customized reporting capabilities to
provide a wider range of reports to the data-
buying suppliers. DrugCo’s internal and inter-
organizational analytical capabilities matured,
and it started to identify what data was saleable.

Data was offered in different packages,
each of which had a different level of data
granularity, reporting capability and price tag.
By now, DrugCo had a dedicated executive on
its merchandising team for selling its data, and
this executive worked with 3PP to market these
data packages directly to DrugCo’s suppliers.
Prices were often negotiated. If a supplier
chose a higher level of information access and
granularity, the price increased. There were four
levels of data packaging—Basic, Bronze, Silver
and Gold—for point-of-sale data (see Table
2). Only a limited number of DrugCo’s major
suppliers were allowed to purchase the highest
Gold level package. As discussed later, a supplier
had to invest resources in its relationship with
DrugCo to become a candidate for the Gold level.

A data-purchase agreement and NDA were
prepared for DrugCo and any supplier who
wanted to buy data. Trust in Stage 3 included
goodwill trust (based on beliefs) in addition to
contractual trust (based on written agreements).
When goodwill trust exists, partners are willing

to go beyond stipulated contractual agreements.
Thus, DrugCo trusted that the supplier would not
only adhere to the data-purchase agreement, but
would also use the data for the benefit of both
parties. In essence, DrugCo’s major suppliers
learned to tell DrugCo when they saw a problem
that needed to be addressed, regardless of
whether doing so was of immediate benefit to
the supplier.

Big suppliers (such Johnson & Johnson,
Procter & Gamble, Coca Cola, PepsiCo, 3M,
Novartis and Unilever) have been applying
analytical tools for a long time to better predict
demand and develop successful marketing
campaigns; they are equipped with significant
know-how in terms of BI&A:

“There are hundreds of CPG [consumer
packaged goods] companies … analyzing
detailed data from retailers … mixing
it together with econometric and
demographic data, weather data, various
kinds of geographic data, and trying
to better understand the markets and
figure out how to better sell the products.”
Cofounder and CEO, 3PP

Table 2: Four Levels of Data Packaging

Level Data Access and Analytics Provided Current No.
of Suppliers

Percentage of
Suppliers

Basic • Supplier items only at POS transaction level detail filtered by SKU
• Information provided shows supplier inventory level status
• Access provided only through

prebuilt reports

358 55.3%

Bronze
Basic Package plus:
• Summaries for all approved classes/categories provided by a few

prebuilt reports

128 19.8%

Silver
Bronze Package plus:
• All items at POS transaction level detail for approved classes

filtered by class
• Ability to upload up to 10 GB of DrugCo’s data for enhanced

analysis by supplier
• Third-party analysis tool provided for ad-hoc analysis by supplier
• (Limited) basket view of categories a supplier operates in

82 12.7%

Gold
Silver Package plus:
• (Full) basket view for all baskets, regardless of categories or

supplier
• Custom reports built for individual supplier or built for a set

timeframe

79 12.2%

December 2013 (12:4) | MIS Quarterly Executive 221

Data Monetization: Lessons from a Retailer’s Journey

With access to more granular data, suppliers
were able to fine-tune their operations by
predicting sales trends more accurately and
thus better develop marketing and promotional
campaigns:

“They [suppliers] can see a trial and
repeat. They can see how a BOGO [Buy
One Get One] type of promotional offer is
performing, how our customers react to
that differently than maybe a BOGO 50
[50% off ] or a price point.” Senior Director
of CMS, DrugCo

During Stage 3, 3PP provided additional
services to DrugCo, including training
and supporting suppliers, negotiating and
administering data-package contracts, BI&A
services and marketing of DrugCo’s data.

The information strategy of DrugCo at this
stage shifted toward revenue generation; data
was being sold and was generating a revenue
stream for DrugCo. This revenue offset some of
the costs of the underlying infrastructure, such
as the data warehouse, the supplier portal and
reporting tools.

Although DrugCo did not need to make
additional investments in technical and analytical
capabilities during Stage 3, it was still bearing
3PP’s ongoing costs for hosting the cloud-
based data and portal, and providing additional
analytical services. It also incurred contracting
costs for preparing the purchase agreements
with data-buying suppliers. Suppliers were
incurring the costs of buying DrugCo’s data,
negotiating the contracts for the data and
analyzing the data. The suppliers benefitted
by understanding the markets and DrugCo’s
business better. They were able to increase their
sales by using DrugCo’s granular data to design
promotions and to leverage product affinities
for additional promotional effectiveness. The
Chairman & CEO of Procter & Gamble stressed
the value of real-time, granular data:

“For companies like ours who rely on
external data partners, [getting the data]
becomes part of the currency for the
relationship. So as we deal with retailers,
I may not be interested in getting that
Tide ad this week, but if you give me your
data in real time for the next four weeks,

that’s more valuable to me … It would be
heretical in this company to say that data
is more valuable than a brand, but it’s the
data sources that help create the brand
and keep it dynamic.”10

Stage 4: Further Monetizing Data
and Avoiding Analytical Costs by
Leveraging Suppliers’ Resources

The final stage extended DrugCo’s data
monetization journey to new horizons, which
enabled it to take even greater advantage of the
analytical capabilities of its suppliers:

“The purpose of that [suppliers having
access to our data] is for them to be able to
help us be smarter about how we run our
business.” CIO, DrugCo

The technical platform for DrugCo’s data
was expanded to meet new scale requirements
arising from the suppliers’ use of the platform
to perform advanced analyses on the data. Also,
advanced human capabilities were required to
use applications that incorporated advanced
analytical techniques (such as optimization,
predictive modeling, simulation, time series
modeling and principal component analysis).
However, DrugCo avoided these additional
analytical costs by exploiting its suppliers’
analytical capabilities; it began to rely more on
the business insights generated by suppliers’
analyses of the data they purchased from
DrugCo. The Cofounder and CEO of 3PP
elaborated on the symbiotic relationship
between retailers and CPG suppliers:

“CPG companies are often quite
sophisticated … The retailers look at the
CPG companies for advice [on] how to
stock their shelves, how to do promotions,
what products to sell, to whom [and] under
what circumstances … There’s a symbiotic
relationship in the sense that the retailer
gets advice from the CPG company, and the
more information the CPG company has
about what’s going on at the retailer and
in the market, the better advice they would
get, and of course there’s the money angle

10 Interview with Robert McDonald, Chairman & CEO of Procter
& Gamble, downloaded from http://www.mckinsey.com/insights/
consumer_and_retail/inside_p_and_ampgs_digital_revolution.

222 MIS Quarterly Executive | December 2013 (12:4) misqe.org | © 2013 University of Minnesota

Data Monetization

… Retailers, like anyone else, are always
looking for revenue sources and retail is a
tough market, [with] very tight margins,
and the more revenue they can get the
better.”

With access to DrugCo’s data, suppliers
started to understand the markets and DrugCo’s
business better; a supplier could get better
insights into how it and DrugCo could together
grow their businesses. This led, in turn, to
DrugCo gaining a better understanding of its
own promotions and its customers, and how they
were buying products over time.

DrugCo’s suppliers can now develop affinity
analysis reports—which show what products
are usually sold together—faster and more
accurately, and pass these reports to DrugCo.
The reports enable DrugCo to run separate
promotions and advertising campaigns for
highly related products instead of promoting
and advertising them at the same time. The shift
of data analytics to the suppliers resulted in a
reduction of analytical costs for DrugCo.

Major suppliers offer insights to DrugCo
through direct interaction on a daily basis
between DrugCo’s merchandising team and
the suppliers’ sales agents, often supported by
BI&A analysts. In addition, supplier and DrugCo
representatives are both involved in meetings
of the supplier portal advisory board, where
entire sessions may focus on analytics insights
of benefit to DrugCo. For example, one major
supplier presented a co-merchandising affinity-
analysis program it had recently implemented,
which predicts what third product will be
purchased when two other products are bought.
After reviewing the program, the advisory board
voted and approved that it should be made
available to Gold members, and it was included in
the Gold level of data access.

In Stage 4, suppliers enhanced their
collaboration with DrugCo and increased their
sales; for example, they could use a shelf-monitor
program that looks at sales of their products and
detects a potential out-of-stock, which may cause
a consumer to switch and buy a competitor’s
product. Some suppliers became trusted sources
of data analysis. Based on these analyses,
suppliers developed merchandising strategies
and targeted promotional programs that DrugCo
could implement:

“What we do with retailers [is] what we
call Joint Business Planning or Joint Value
Creation … For us, getting data becomes a
big part of value whereas for the retailer
they have the data, so that’s become a big
part of our work together, and then how
can we use this data to help them, because
we have analytical capabilities that many
retailers don’t have, so often times we can
use the data to help them decide how to
merchandise or market their business in a
positive way.” Chairman & CEO, Procter &
Gamble11

An additional form of trust, competence
trust, was needed in Stage 4. DrugCo trusted
that its partners had the superior managerial
and technical capabilities needed to analyze its
data. The company trusted that some suppliers
had the capability and the willingness to use
and analyze its data in a way that benefitted
both parties while refraining from any misuse
or misconduct regarding the data. 3PP’s VP of
Retail Solutions described how DrugCo’s supplier
portal enabled the formation of competence
trust:

“[A retailer] would let their [suppliers]
see the actual performance of the SKUs
by day by store in a [market] basket level
perspective because they were starting
to trust the advice and counsel that their
suppliers were giving them … DrugCo can
watch how the analysis was done by the
[supplier] and argue it. The [supplier]
really can’t be sneaky because everything
they do is wide open.”

As DrugCo reached the fourth stage of the
journey to data monetization, it shifted to a
transparency strategy.12 With this strategy,
a company recognizes that the benefits of
sharing data with external partners exceed
those of withholding information from them.
However, DrugCo realized the importance of
limiting strategic information partnerships to
the suppliers entitled to the highest Gold level
11 Interview with Robert McDonald, Chairman & CEO of Procter
& Gamble, op. cit.
12  A transparency strategy is defined as one that selectively 
discloses information outside the boundaries of the firm to buyers, 
suppliers, competitors and other third parties like governments and
local communities; see Granados, N. and Gupta, A. “Transparency
Strategy: Competing with information in the Digital Age,” MIS
Quarterly (37:2), 2013, pp. 637-641.

December 2013 (12:4) | MIS Quarterly Executive 223

Data Monetization: Lessons from a Retailer’s Journey

data package. Allowing a supplier to purchase
the Gold level package is viewed as a strategic
merchandising decision and is based on the
volume of transactions with the supplier, the
number of people (i.e., the supplier’s data
analysts and salespeople) who are dedicated to
work only with DrugCo and DrugCo’s recognition
of the supplier as a trusted advisor. Suppliers
now compete to be designated by DrugCo as
a “category captain.” These suppliers review
the performance of the entire category and
recommend a store-level sales strategy, including
assortment, shelf-space assignments, promotion,
and pricing.13 Category captains have the closest
and most regular contact with DrugCo and invest
time, effort and resources into the strategic
development of their categories within DrugCo.
They deploy dedicated analysts who only work
with DrugCo and thus become trusted partners.
In return, category captains have some degree
of decision-making authority and an influential
voice at DrugCo. DrugCo evaluates its suppliers’
analytical performance based on the value of
the analytics and recommendations provided
by them and their track record of promoting
DrugCo’s business.

Lessons Learned
Several important lessons emerge from the

DrugCo case. We believe the following practices
will contribute to the successful monetization of
data.

1. Consider How Creating and Sharing
Data Will Change Relationships and
Business Models

It is important to consider the dynamics
among supply-chain members and to think
about how data monetization might change the
traditional relationships in the supply chain.
Retailers can expect their major suppliers to
compete for a category captain role to become
a trusted advisor and a source of valuable
business recommendations. Companies need to
carefully consider the trade-off between higher
levels of information transparency with their
supply-chain partners and the possible risk of

13 For an analysis and recommendations for choosing a category
captain, see Subramanian, U., Raju, J. S., Dhar, S. K. and Wang,
Y. “Competitive Consequences of Using a Category Captain,”
Management Science (56:10), 2010, pp. 1739-1765.

losing information advantages over suppliers,
customers and competitors.

Data monetization creates a new business
model for the company, in which revenue
generation, cost structure, value proposition and
relationships change. The company’s data is not
only used to run the business, but also becomes
a digital product the company can use to
generate revenue and cover the costs associated
with creating and gathering data. Leveraging
suppliers’ analytical capabilities introduces a
new era of informational collaboration among
partners and supply-chain members. Suppliers
can add value to their relationships with retailers
by offering business insights and new business-
growth opportunities. Third parties can provide
value-adding services to create and sustain a
data monetization platform.

As the dynamics of competition and
cooperation among companies continue to
evolve, IT provides opportunities for value
co-creation. A data monetization relationship
is a good example of the co-creation of IT-
based value between companies at the assets,
complementary capabilities, knowledge-sharing
and governance levels.14

2. Identify Where You Currently Are
in the Data Monetization Journey and
Where You Want to End Up

An ideal end state of a data monetization
initiative will result in deeper insights from
the associated ecosystem, a revenue stream, a
reduction in infrastructure and analysis costs,
and trusted use of data by supply-chain partners.
The following are several aspects that concerned
stakeholders have to pay attention to, prior to
and during their data monetization journey.

Prepare Your Data for Sale. The integration
of additional relevant data sets into the
company’s data will increase the value of the
data to data buyers. For example, DrugCo
enhanced the value of its data to its suppliers
by adding loyalty data. Companies should also
package the data for sale to meet different needs,
analytical capabilities and willingness to pay.
Multiple levels of data packaging (see Table 2) is
a useful technique.

14 For more discussion on co-creating IT value, see Grover, V. and
Kohli, R. “Cocreating IT Value: New Capabilities and Metrics for
Multifirm Environments,” MIS Quarterly (36:1), 2012, pp. 225-232.

224 MIS Quarterly Executive | December 2013 (12:4) misqe.org | © 2013 University of Minnesota

Data Monetization

Assess the Need for Value-Adding Third
Parties to Join the Data Monetization
Ecosystem. Third parties can provide various
value-adding activities in the data monetization
ecosystem. Examples include orchestrating
the relationship between the company and the
data buyer by hosting the data, contracting with
data buyers, offering training and support, and
providing technical and analytical capabilities.
A third party can also be instrumental in the
company’s effort to obtain and build the required
technical and analytical capabilities. Assessing
what can be outsourced can be instrumental
to building and sustaining a data monetization
initiative.

Market Your Data and Challenge Your
Suppliers to Get Onboard. A marketing strategy
is needed to advertise and promote the value
of the company’s data. The company has to
approach potential data buyers and highlight
how and why the data is useful, as suggested by
DrugCo’s Senior Director of CMS:

“Challenge them saying: “Well, your
competitors understand this better now.
You know you’re falling behind.”

Even when third parties participate in the
data monetization initiative, the company still
has to be involved in selling its data:

“You have to be involved with pushing it
and selling it. You don’t really outsource
the selling of the data.” Senior Director of
CMS, DrugCo

Avoid Some Analytical Costs by
Leveraging Suppliers’ Analytical Resources.
A data monetization initiative can create new
opportunities for the company to exploit its
suppliers’ ability to analyze data. It is not
uncommon for there to be more analytical
resources on the supplier’s side dedicated to
working on and analyzing the company’s data, as
highlighted by DrugCo’s CIO:

“More [analytical] people on the [supplier]
side have access to [our data] than we do
internally.”

Recognize and Reward Your Top-
Performing Suppliers. Determining appropriate
measures to identify top-performing suppliers

in your data monetization ecosystem and
rewarding them will establish a collaborative
relationship in which actions are guided by the
principle of mutual benefit. A supplier can be
rewarded by allowing it to have a higher level of
data package and by nominating it as a category
captain. Decisions to recognize top performance
should not only be based on transaction volume,
but also on the supplier’s provision of human
capabilities and the quality of advice provided.
The performance of existing category captains
should be continuously monitored so that
underperforming category captains can be
replaced with new ones.

3. Develop Contracts to Ensure
Adherence to Data Monetization
Policies

Several contracts were developed between
DrugCo, 3PP and DrugCo’s suppliers throughout
the data monetization journey, notably NDAs
and data-sharing and -purchase contracts. These
contracts restricted the use of the shared or
purchased data to specific purposes. Suppliers
were obliged to use the data they purchased for
the sole purpose of growing the mutual business
of the suppliers and DrugCo.

4. Nurture Trust Between the Involved
Parties

Different forms of inter-organizational trust
exist between business partners. Trust can
lower the contracting cost and conflict level
required to reach a data-purchase agreement.
The progression from trust based on written
agreements to trust based on beliefs contributes
to the formation of a collaborative relationship
in which mutual benefits are considered by the
parties involved. Inter-organizational trust can
be built by communication of trustworthiness,
inter-organizational coordination to establish
governance mechanisms, and successful and
repeated interactions that demonstrate each
partner’s reliability. The transparency of the
collaboration portal can also nurture trust
between a company and its suppliers; suppliers
can be held accountable for their use of the
company’s data and the quality of the analysis
and advice they provide.

December 2013 (12:4) | MIS Quarterly Executive 225

Data Monetization: Lessons from a Retailer’s Journey

Concluding Comments
The DrugCo case demonstrates that getting

direct monetary value from a company’s data is
no longer elusive. Data analysis tools and cloud
computing have paved the way to monetizing
a company’s data. We have described how
DrugCo was able to monetize its data by going
through four distinct stages and ultimately
increased both tangible and intangible benefits.
Building technical and analytical capabilities
and connecting with the retailer’s suppliers
facilitated the emergence of a digital ecosystem
that enabled data monetization. DrugCo
managed to cut its analytical costs by leveraging
its suppliers’ well-established technical and
analytical capabilities. Joint benefits emerged
from this new relationship by generating a new
revenue stream and providing a cost-sharing
mechanism for the retailer, and offering suppliers
real-time access to the retailer’s data.

Appendix: Research Approach

The topic of data monetization arose when
one of the researchers interacted with an
executive of 3PP, a company that provides cloud-
based big data hosting as well as analytical and
consulting services. This firm had considerable
experience with building supplier portals
and/or cloud-based data ecosystems so
companies could monetize their data. At the
researcher’s request, 3PP identified several of
its clients that had monetized their data, and
the researcher approached them about the
possibility of in-depth cases concerning the “how
and why” of data monetization. DrugCo was
willing to discuss its journey on the condition
that it remained anonymous.

First, we carried out numerous rounds
of interviews at 3PP with the VP of Business
Analytics, VP of Retail Solutions, Client Project
Manager and Client Relationship Manager to
more fully understand data monetization in
general and 3PP’s experiences with DrugCo in
its role as a catalyst and facilitator of DrugCo’s
data monetization journey. The data provided by
these interviews was analyzed and formed the
initial picture of DrugCo’s journey.

Next, data gathered from the interviews
with 3PP was used to develop the interview

guide to be used at DrugCo. Executives at
DrugCo who were knowledgeable about and
had participated in DrugCo’s data monetization
journey were identified with the help of 3PP. In-
depth interviews were conducted with DrugCo’s
CIO, the Director of Category Management
Services and the VP of Pharmacy, who provided
details about DrugCo’s journey. Email follow-up
questioning also occurred.

Finally, follow-up corroborating interviews
were conducted with 3PP’s VP of Retail
Solutions, Client Project Manager and Client
Relationship Manager to triangulate accounts.
Secondary sources, including some additional
interviews at 3PP and public sources,
complemented our primary sources and allowed
us to form an overall view of data monetization.

About the Authors

Mohammad S. Najjar
Mohammad Najjar (msnajjar@memphis.edu)
is a Ph.D. candidate at the Fogelman College of
Business and Economics at the University of
Memphis. He received his M.B.A. and B.Sc. from
the University of Jordan. His research interests
include IS services, business intelligence,
information assurance and information
management. He has published in and reviewed
for several international conferences.

William J. Kettinger
William Kettinger (bill.kettinger@memphis.edu)
is Professor and FedEx Endowed Chair in MIS at
the Fogelman College of Business and Economics
at the University of Memphis. Kettinger’s focus
is practical, rigorous research appearing in
leading journals. He has received such honors
as a Society of Information Management’s Best
Paper Award and directed a SIM APC study of the
business drivers of IT value. He has served on the
editorial boards of MIS Quarterly, Information
Systems Research, Journal of the Association of
Information Systems and MIS Quarterly Executive.
He consults with global companies such as
enterpriseIQ®, AT&T and IBM.