Whitepaper

Data Monetization – Use Cases, Implementation and Added Value

Author
Dr. Sebastian Derwisch, Data Scientist | BARC - Business Application Research Center
In Partnership With

The data monetization landscape is evolving

Data is a central resource. Only those who manage to extract value from their data will remain competitive, leading many organizations to seek new ways of creating value from their data. Data monetization is the process of identifying and marketing data or data-based products to generate monetary value.

This survey-based study, conducted by , seeks to answer the following questions:

  • How relevant is data monetization and what level of maturity have current projects reached?
  • How are companies monetizing data?
  • What use cases have organizations already implemented? What use cases are being planned?
  • Which technologies are used to implement data monetization projects?
  • What are the benefits of data monetization?
  • What are the biggest challenges in implementing data monetization?

Key findings include:

  • Data monetization is at an early stage of adoption, but is expanding.
  • Providing analysis results for process improvement is the main way companies monetize data.
  • The most common technologies used monetise data are BI software and data integration tools.
  • The benefits of data monetization are many—top amongst them are new revenue sources, development of new services, and improved customer loyalty.
  • Data quality issues represent the biggest obstacle to monetizing data followed by data security concerns.


Data products with embedded analytics integration lead the market

The core of data monetization is data products (i.e., products that are based on raw, refined or analyzed data). Data products can take many forms, including consumable data sets, analysis results and operational applications that contain analysis results. These can come as reports, extensions to existing products, digital platforms or can be incorporated into new business models.

Data monetization includes both internal and external opportunities. Internal data monetization aims to improve internal processes such as marketing and customer experience or the maintenance of equipment. On the other hand, external data monetization involves the use of data to extend an organization’s product offering with data, data-driven services or business models to create new revenue streams.

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About The Author

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Dr. Sebastian Derwisch

Data Scientist | BARC - Business Application Research Center

Dr. Sebastian Derwisch is an Analyst and Data Scientist at the Business Application Research Center. He advises companies in the areas of use case identification for data analytics, tool selection for advanced analytics and the organization of data science teams. Sebastian Derwisch also conducts proof of data values for advanced analytics projects and data science coachings. He has authored market studies such as the BARC Score Advanced Analytics Platforms and research articles regarding data analytics.