Knowledge Center: Data Monetization

Data Monetization

Data monetization is converting company-generated data into a verifiable economic gain. Businesses frequently benefit from monetizing their data, such as higher income or lower expenditures. Companies may also leverage their data to create less visible benefits, such as new alliances or better supplier terms, by sharing their data in a mutually beneficial relationship with third parties. In certain circumstances, enterprises see the potential of their data and begin providing data services to a sufficient number of outside firms. Facebook and Google were early adopters of this strategy, leveraging their open platforms to produce massive data assets for sale throughout the world.

  • Internal data monetization – A company’s data is utilized internally, resulting in an economic advantage. This is frequently the case with firms that use analytics to unearth insights, resulting in increased profit, cost savings, or risk avoidance. Internal data monetization is now the most frequent since it requires significantly less security, intellectual property, and legal safeguards than other varieties. The internal structure and circumstances of the business constrain the possible economic rewards from this form of data monetization.
  • External data monetization – A person or organization makes data they own available to third parties for a charge or acts as a broker for such data. This monetization is less widespread and necessitates various mechanisms for disseminating data to potential buyers and customers. However, the economic advantage of data collection, packaging, and distribution may be substantial.
  1. Improved decision-making leads to real-time crowd-sourced research, more revenues, lower expenses, lower risk, and higher compliance.
  2. Detailed and quick decision making
  3. Targeted and accurate marketing strategizing.
  4. New product and solution ideas that are not apparent without access to and analysis of the correct data
  1. Identification of existing data sources – this includes data that is already accessible for monetization as well as any external data sources that may add value to what is already available.
  2. Connecting, aggregating, attributing, validating, authenticating, and exchanging data enables data to be transformed directly into actionable or revenue-generating information or services.
  3. Set conditions and pricing and promote data trade – data vetting, storage, and access ways. Many worldwide businesses, for example, have closed and compartmentalized data storage infrastructures, which impedes efficient data access and cooperative and real-time interchange.
  4. Conduct research and analytics – extract predicted insights from current data to use as a foundation for leveraging data to decrease risk, improve product development or performance, or improve customer experience or business results.
  5. Action and leveraging – the last stage of data monetization is identifying alternative or superior data-centric goods, ideas, or services. Examples are real-time actionable prompted alerts or upgraded channels such as online or mobile response mechanisms.
  • Data-as-a-Service

This easiest data monetization technique often runs on a direct business-to-customer (B2C) approach. When the source data contains personally identifiable information, the data might be raw and unstructured, aggregated for a high-level overview, or anonymized (PII). This is a method of directly monetizing data. This option also has the lowest revenue creation potential. Raw datasets must still be evaluated to provide insight because Data-as-a-Service delivers raw data. This implies that purchasers benefit once they load and assess data using analytics or business intelligence software and tools.

  • Insight-as-a-Service

In contrast to Data-as-a-Service, which gives raw data for purchasers to analyze, Insight-as-a-Service provides summarized analytical insights, such as competitor insights or consumer behavior patterns. The information is derived from various sources, including internal datasets and primary and secondary data sources from outside sources. These insights can be sold as a one-time report or continually through embedded analytics apps for continuing income generation. Another instance of direct data monetization. More work is necessary for firms leveraging data monetization in this context to develop insights and visualizations. This strategy must also be linked with the criteria of prospective buyers, which means that incomplete understanding may create no money.

  • Analytics-as-a-Service

Customers can obtain insights in exchange for payment, similar to Insight-as-a-Service. The distinction here is the breadth of data access and analytics capability. Customers, for example, have real-time, regulated access to analytics and BI visualization tools managed by the selling data supplier. This data source might be a research firm with large-scale datasets on a particular sector. This is another method of directly monetizing data. The customer benefits from zero setups and zero maintenance, similar to how cloud computing eliminates the need for corporations to handle server infrastructure. It functions similarly to an internal analytics environment, except that the data supplier is the sole owner.

  • Data-Driven Business Models

A data-driven business model seeks to maximize efficiency and production by utilizing every available data source. Sales, marketing, human resources, finance, or any other corporate function might be included. This is an indirect technique of data monetization that benefits the organization by evaluating its data. For example, system logs and crash dump files are produced when a server goes down. This information may be consolidated and analyzed to discover recurring network issues and increase IT service desk productivity. Another scenario would be if client purchasing habits shifted, leading items to become overstocked. Sales metrics may be used to visualize sales volumes over time and to proactively spot patterns to enhance supply chain efficiency and stock levels.

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