What is Data Monetization?
Modern organisations are increasingly recognizing the economic potential in their data - in the age of digital transformation, data is widely stated to be an organisation’s most valuable internal assets. Data monetization however, involves the strategic process of transforming internal data into new revenue generation, and most often not included in the general valuation of internal data.
By exploring how internal data can be packaged into sellable data products and making it available to external users, a whole new sphere of economic value and new insights can be generated.
This overview will dig deeper into the multifaceted area of data monetization, exploring its types, ethical considerations, steps, pricing strategies, and frameworks, with a focus on enabling organisations to turn their data into new revenue streams.
Data Driven Organizations Utilising Internal Data
Internal data can be strategically utilised to enhance internal operations, drive efficiencies, and contribute to revenue generation. Following are three types of internal data that organisations commonly possess and leverage to various benefits:
Operational Data: Data generated from day-to-day business activities. Examples include sales transactions, production metrics, and inventory levels. Analysing operational data can provide insights into process efficiency, identify bottlenecks, and streamline workflows.
Customer Data: Information about customer interactions, preferences, and behaviours is crucial for businesses. Internal customer data can be used to personalise offerings, improve customer experience, and implement targeted marketing strategies.
Supply Chain Data: For businesses involved in production and distribution, internal data on the supply chain is vital. This includes information on suppliers, logistics, and inventory management, which can be optimised for cost-efficiency.
Sensitivity in Data Monetization
Most types of internally gathered data can be used to generate operational efficiencies and benefits. However, it can also be of great value to external organisations aiming to gather insights into new business areas. Though, before delving into making internal data available to outside organisations, there are several factors to explore.
Individuals & PII classified data: User data can be of great value for external companies seeking insights into consumer behaviour. To balance business objectives with privacy, however, organisations can responsibly monetize data through anonymization and aggregation.
When monetizing user data, stringent privacy policies and user consent needs to be taken carefully into account, and expert advice is recommended before considering making user data available for monetization.
By removing personally identifiable information and consulting trusted third-party experts, businesses can extract valuable insights without compromising user privacy. This approach not only ensures compliance with regulations but also strengthens trust between companies and their user base, creating a win-win scenario for both parties.
Businesses: As written above, organisations generate substantial data during operations, encompassing transaction records, customer interactions, and various internal processes. This data is much less sensitive in terms of privacy issues, but can be risky to make available to the wrong buyer, should there for instance be a risk of making competitive insights available to direct competitors. It is therefore strongly recommended to carefully develop the data products and define who the data should be made available for.
Example of Industries Already Putting Data Monetization to Work
Financial Services Industry:
Financial services companies serve as typical examples of how to successfully generate revenue through data monetization. Credit card issuers and banks can strategically employ customer transaction data to refine cross-selling strategies. Partnerships with merchants can amplify revenue streams through data-driven reward programs.
Telecommunications Industry:
Telecommunications companies leverage data monetization by analysing customer usage patterns, preferences, and network performance. They can sell anonymized and aggregated data to advertisers, providing insights into consumer behaviour and enabling targeted advertising. Additionally, telecom companies can offer location-based services to third-party businesses, such as retail stores or advertisers, based on the geospatial data collected from mobile devices.
Healthcare Sector:
In the healthcare industry, organisations can monetize data through various means. Pharmaceutical companies can use patient data to identify trends, optimise clinical trials, and personalise drug development. Health insurance providers can utilise patient health records to create personalised wellness programs and offer insights to employers for employee well-being initiatives. Moreover, healthcare data analytics companies can aggregate and anonymize healthcare data to sell valuable insights to researchers, pharmaceutical companies, and other stakeholders.
How Data Monetization can be Streamlined with Automation
The ease of creating valuable data products and making these available to third parties through public data marketplaces has increased vastly over the last few years. Here, UnionAll has taken vast steps into automating the process, demanding less in terms of time and resources from the monetizing party when aiming to create and publish data products.
The market for external data is continuously growing, and we are seeing a strong increase in demand for new and still unexplored sectors for data monetization. The need to enrich internal company data with external insights, or utilise third party data for training AI-models is taking off with great velocity in the coming years. This opens up great opportunities for companies seeking to increase revenues and explore the realm of data monetization.
How to Monetize Your Data
Monetizing internal company data can be a long and resource intensive process, including the below described step:
Identify the Data
Find the data that can be monetized. This can be both internal data such as customer, network, or operations data.
Analyze the data
Assess the quality, which can be done automatically by applying AI, and determine the data asset’s sellability by analysing what insights that could be drawn from the data.
Define the Value Proposition
Identify potential customers or partners that could benefit from the data and decide on the business model and pricing strategy. Consider potential regulatory limitations as mentioned above.
Develop the Data Product
Source, clean, transform, encrypt, enrich, document, and publish the data asset on the desired platforms.
Market the Data Product
Market the data through relevant platforms and apply search engine optimization (SEO), or leverage existing business networks to reach potential buyers.
Making the Data Available on Data Marketplaces
Data marketplaces serve as dynamic platforms where organisations can publish, share, and extract value from their data. These marketplaces act as intermediaries, connecting data providers with potential buyers, fostering an ecosystem for data exchange. Understanding the dynamics of data marketplaces is important for organisations seeking to capitalise on data assets.
Data Product Go to Market
Pricing data for external buyers involves a thoughtful approach to ensure fairness, attractiveness, and alignment with the value provided. Here are some strategies and considerations for pricing data in the context of data monetization.
Analysing current supply of similar products and building an understanding of existing demand is important for establishing the initial price for the data products. Factors such as data freshness, granularity and size of the datasets compared to existing products on the market will also play a significant part in pricing the data.
How UnionAll is Automating the Process of Monetizing Data
UnionAll has taken vast steps into automating the above described process, demanding less in terms of time and resources from the monetizing party when aiming to create and publish data products. This process includes automated discovery, packaging and publishing. Based on vast insight into existing data on public marketplaces, as well as current demand for new data products, UnionAll can tailor products and price, as well as marketing campaigns and relevant content to get maximum throughput on value for published data products.
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