1 IoT Fundamentals: Definitions, Architectures, Challenges, and Promises
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which you can choose and the appropriate choice depends on your strategic
requirements [30].
• Centralized Marketplace – This is a platform owned by one party that
serves as a centralized location to exchange multiple kinds of data among
diverse participants. In this marketplace, both metadata and raw data are
stored.
• Decentralized Marketplace – This is a platform where participants are able
to exchange data directly in peer-to-peer transactions. In this context, the
marketplace only stores the metadata to enable data consumers to find the
provider/owner of the data.
– Selling Data Insights or Analysis – Performing data analytics on raw data
improves the quality of the information being sold. Not all companies have
the capability to analyze data, creating an opportunity for monetization that is
beneficial for both sides of the transaction. Analysis services can be offered in
marketplaces or through other channels.
• Indirect Data Monetization – Data can be used to improve business intelligence
and function, generate new products or services, and create new business models.
Generally, there are two approaches to making good use of your own data [30]:
– Data-Driven Optimization – Utilizing data in this way decreases cost and
increases the effectiveness and efficiency of business processes. This optimization is applicable across many fields. For example, manufacturing test
benches could be optimized by shortening the testing time or field data could
be utilized to improve the design of a product.
– Data-Driven Business Models – Monetizing by employing this strategy means
that process or product data is used to generate new business opportunities
or attract new customer groups through the development of new services or
products or by improving existing products or services. Building a data-driven
business model enables you to uncover innovative, new businesses rather
than adjacent businesses. These models are also important for diversifying
revenue streams. For example, Bosch makes use of manufacturing data to
create customized subscription-based services that monitor the conditions of
hydraulic systems.
The market of IoT data will keep growing as companies learn how powerful it
can be to provide data to others and how much others are willing to pay to obtain
data. The primary challenges around monetizing IoT data include [29]:
• Ensuring Data Quality – In order for customers to trust the data provided, it must
be of high-quality and complete. The data should also be accurate and timely and
have been obtained ethically.
• Determining Information Type – Providers of data will need to adapt and flex
to customer needs as companies may require IoT data in diverse forms or
43
which you can choose and the appropriate choice depends on your strategic
requirements [30].
• Centralized Marketplace – This is a platform owned by one party that
serves as a centralized location to exchange multiple kinds of data among
diverse participants. In this marketplace, both metadata and raw data are
stored.
• Decentralized Marketplace – This is a platform where participants are able
to exchange data directly in peer-to-peer transactions. In this context, the
marketplace only stores the metadata to enable data consumers to find the
provider/owner of the data.
– Selling Data Insights or Analysis – Performing data analytics on raw data
improves the quality of the information being sold. Not all companies have
the capability to analyze data, creating an opportunity for monetization that is
beneficial for both sides of the transaction. Analysis services can be offered in
marketplaces or through other channels.
• Indirect Data Monetization – Data can be used to improve business intelligence
and function, generate new products or services, and create new business models.
Generally, there are two approaches to making good use of your own data [30]:
– Data-Driven Optimization – Utilizing data in this way decreases cost and
increases the effectiveness and efficiency of business processes. This optimization is applicable across many fields. For example, manufacturing test
benches could be optimized by shortening the testing time or field data could
be utilized to improve the design of a product.
– Data-Driven Business Models – Monetizing by employing this strategy means
that process or product data is used to generate new business opportunities
or attract new customer groups through the development of new services or
products or by improving existing products or services. Building a data-driven
business model enables you to uncover innovative, new businesses rather
than adjacent businesses. These models are also important for diversifying
revenue streams. For example, Bosch makes use of manufacturing data to
create customized subscription-based services that monitor the conditions of
hydraulic systems.
The market of IoT data will keep growing as companies learn how powerful it
can be to provide data to others and how much others are willing to pay to obtain
data. The primary challenges around monetizing IoT data include [29]:
• Ensuring Data Quality – In order for customers to trust the data provided, it must
be of high-quality and complete. The data should also be accurate and timely and
have been obtained ethically.
• Determining Information Type – Providers of data will need to adapt and flex
to customer needs as companies may require IoT data in diverse forms or