Data Owner
Any organization which digitally captures activities and transactions (banks, exchanges, etc.)
Monetize your data
Data monetization is the process of using data to obtain quantifiable economic benefit, such as making business performance improvements, sharing data with partners, or selling data.
Benefits of data monetization include cost reduction, customer retention, revenue increases, and opportunities for new data-related services.
Monetization of data starts with understanding
Data lineage and provenance
What you can and can’t do with the data
Applicable use cases
Data Licensing
If you want to license your data, you need to
Make your data more discoverable and AI / ML ready
Have confirmed use case(s)
Provide tools to reduce time and resources for evaluation and onboarding
Make the buying process simple
See
See what data you have
Trace
Trace lineage and provenance
Understand
Understand your rights to the data – what you can and can’t do with it
Digitize
Digitize all the terms and conditions of the data license, including usage rights, and share those with counterparties
Connect
Connect CRM to Contract Management, Service Delivery, Entitlements, Applications andAccounting
Observe
Observe data usage internally and externally, enable common client frameworks
Track
Track revenue and spend allocations
Invoice
Invoice accurately, in real-time based on observations
Automate
Automate the process around data subscription – full STP from license to delivery and utilization
Taxonomy
Taxonomy is a two-sided classification system, provides standardized values used within a dataset, focusing on:
Information about what each dataset describes, how it is sourced
Potential use cases
Data monetization is the process of using data to obtain quantifiable economic benefits, such as making business performance improvements, sharing data with partners, or selling data. Benefits of data monetization include cost reduction, customer retention, revenue increases, and opportunities for new data-related services.
Monetization of data starts with understanding:
- Data lineage and provenance
- What you can and can’t do with the data
- Applicable use cases
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