Data monetization is generating new revenue in addition to the company's traditional turnover thanks to data. This includes exploiting them and creating value by selling them internally or externally.

1. HOW TO MAKE MONEY
FROM DATA NETWORKS?
Tuğçe ARSLAN

2. Data networks can use five of
the six possible monetization mod-
els, depending on how they receive
data from users and how data is
consumed.
Data networks are unique in
the world of network effects. Most
types of networks create value by
allowing participants to interact
with each other somehow. Howev-
er, data networks do not directly
connect participants. Instead, they
collect data from participants to
improve the product for all par-
ticipants. First, it automatically
overrides one of the monetization
models (interaction taxes (or com-
missions)) used by other networks.
Because there is no direct inter-
action between participants, they
cannot be taxed.
This is because monetization (or
value capture) must be aligned with
the product’s primary value prop-
osition (value creation). The data
collection method (data collection)
and the nature of product use (data
consumption).
First, let’s take a look at data re-
trieval. As I explained earlier, there
are two broad ways data networks
receive data from users:
• Active crowdsourcing, where
users must actively and directly
interact with a product to collect
data, such as on. In other words,
the quality of the product now
depends on user participation.
• Passive crowdsourcing where
data is automatically extracted
from all product adopters regard-
less of direct interaction, such as.
It depends entirely on user acqui-
sition, not user engagement.
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3. Then, data consumption can occur in two broad ways:
Some data networks, such as Tripadvisor and Waze, need to be actively and deliberately
used by end-users to derive value from them.
Others, like Mapbox, are used passively and tend to be embedded in third–party products
or workflows. While they do add power to some capabilities, they are not necessarily used
intentionally by end–users to derive value from them.
Premium Network Layer
As I explained earlier, premium networking tiers are built into users with a free product and
get paid with an optional, paid deck. Instead, interactions are limited to feeding and consum-
ing data into the product.
As a result, it isn’t easy to use this model with data networks (for example, Waze) that must
be actively used to realize value propositions. Actively used data networks encourage partici-
pation through data availability–restricting this availability will also hinder value propositions.
For example, if Waze limits data access based on the type of event reported. As a result, pre-
mium network layers are a better suite for passively used data networks.
In both free and paid plans, all users can contribute to the data network.
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4. Data Network with Paywall
As I explained earlier, active crowdsourcing requires many participants to initiate a data
network effect. Putting the entire network behind a paywall would interfere with this goal with
passive crowdsourcing, e.g., products that automatically collect data from all customers.
Additionally, it can be tough to combine active usage with the passive crowdsourcing seen
in paywalled data networks. Paying customers are often reluctant to share their activity data
(e.g., sales intelligence or contact information) directly with others. After all, they’re paying to
use your product, and privacy will likely be a purchase consideration. When used to inform a
recommendation algorithm (e.g., XANT), enhance the capabilities of an established product
(e.g., Mapbox).
Complementary Products
They are individual features or capabilities that increase the value a customer receives from
the product. This is difficult to apply to “background” products as it does not improve data qual-
ity or access. Instead, users need to actively use the product to derive value from these plugins.
And, as we’ve seen in advertising, this is most effective where engagement generates more
engagement, meaning it’s more suitable for data networks that rely on active crowdsourcing.
Derived Products
A derived product leverages interactions and participation on a network to produce a
directly monetizable asset. This means making data generated by free users a standalone prod-
uct for third parties in data networks. However, data users must consume it for a feedback loop
(and data network effect) to exist.
Since monetization is entirely separate from the users of the data network, data collection
and product usage have no impact.
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