Data monetization describes the process of a business using data to obtain an economic benefit. In essence, data monetization is the process of utilizing data to increase revenue. According to management firm McKinsey & Company, an increasing share of the most successful companies in the world incorporate data and analytics to fuel their growth.
|Definition||Data monetization refers to the process of generating revenue or extracting value from the data an organization collects or possesses. It involves identifying opportunities to leverage data assets for financial gain, either by selling data directly, using data to enhance existing products and services, or creating new data-driven offerings. Data monetization has become increasingly important in the digital age as organizations recognize the value of data as a strategic asset. It encompasses various approaches, including data sales, data analytics services, data-driven advertising, and more. Successful data monetization requires a clear strategy, data privacy compliance, and the ability to extract meaningful insights from data.|
|Key Concepts||– Data Assets: Organizations possess data assets that can be transformed into valuable resources. – Data Value Chain: The process of data collection, storage, analysis, and application to create value. – Monetization Models: Various models, such as data sales, data licensing, subscription models, and data analytics services. – Data Privacy: Compliance with data privacy regulations to protect consumer information. – Data-Driven Innovation: Leveraging data for new product development, improved decision-making, and enhanced customer experiences. – Data Marketplace: Platforms or exchanges facilitating the buying and selling of data.|
|Characteristics||– Data Variety: Data monetization encompasses structured, unstructured, and semi-structured data. – Analytics Capabilities: The ability to extract insights from data is essential. – Data Governance: Proper management, quality control, and privacy compliance are critical. – Business Model Diversity: Different organizations adopt various data monetization models based on their industries and objectives. – Value Creation: Data monetization creates value not only through sales but also through improved operations and decision-making.|
|Implications||– Revenue Generation: Data monetization can be a significant source of revenue for organizations. – Competitive Advantage: Effectively leveraging data assets can provide a competitive edge. – Enhanced Customer Experiences: Data-driven insights lead to improved customer experiences. – Innovation: Data can fuel innovation and the development of new products and services. – Data Privacy: Organizations must prioritize data privacy and security to avoid legal and reputational risks.|
|Advantages||– Revenue Generation: Data monetization offers opportunities to generate revenue streams beyond core business activities. – Enhanced Insights: Extracting insights from data can lead to more informed decision-making. – Competitive Edge: Effective data monetization can provide a competitive advantage in the market. – Innovation: Data-driven innovation can lead to the development of new products and services. – Value Creation: Data monetization can create value not only for the organization but also for customers and partners.|
|Drawbacks||– Data Privacy Risks: Mishandling of data can lead to data privacy breaches and legal consequences. – Data Quality Issues: Poor data quality can result in inaccurate insights and decisions. – Data Monetization Costs: Developing data monetization capabilities can be costly. – Market Saturation: In some industries, data markets can become saturated, making it challenging to stand out. – Ethical Considerations: Balancing data monetization with ethical use of customer data is crucial for reputation and trust.|
|Applications||Data monetization is applied across various industries, including finance, healthcare, e-commerce, advertising, and more. It is used for purposes such as targeted marketing, risk assessment, personalized recommendations, and predictive analytics.|
|Use Cases||– Data Sales: Organizations sell their data to other businesses, researchers, or third-party data brokers. – Data Analytics Services: Providing data analytics services to other organizations seeking insights from their data. – Data-Driven Advertising: Using data to target advertising to specific audiences. – Subscription Models: Offering access to data through subscription-based services. – Product Enhancement: Using data to improve existing products or create new ones. – Risk Assessment: In industries like insurance, data is used for risk assessment and pricing. – Healthcare Analytics: Analyzing healthcare data to improve patient outcomes and reduce costs. – Smart Cities: Leveraging data for urban planning and improving city services.|
Understanding data monetization
Data is becoming increasingly ubiquitous as the widespread adoption of big data systems and Internet of Things (IoT) devices allow consumers and businesses alike to collect data on anything and everything. In fact, it is estimated that the world creates around 2.5 million terabytes of data every day, with more than 90% created in the past two years.
Despite the relative ease with which data can be collected and analyzed, the practice of monetizing data remains relatively uncommon. Research out of Germany discovered that less than one in five companies had established data monetization initiatives and a mere 0.5% use data in decision making. Instead, most companies spend vast amounts of time and money storing their data instead of determining how they can make money from it.
Data has undebatable intrinsic value, but the most value is derived when the business demonstrates the ability to derive insights from that data and monetize it accordingly.
How is data monetized?
Different data monetization methods will be appropriate to different companies according to their particular business strategies, growth stage, or industry.
Here are a few ways data can be monetized:
- Data as a service – the most simple method where data is sold to customers in a raw, anonymous, or aggregate form. The customer is responsible for analyzing the data to facilitate a financial gain.
- Analytics-enabled platform as a service – these are platforms sold to customers that provide scalable and versatile data analytics in real-time. They can be cloud-based or installed on-premise and can incorporate a wide array of data formats.
- Insight as a service – where internal and external data sources are combined and analyzed to generate insights. This method tends to be confined to specific contexts and datasets. For example, John Deere combined external soil and weather data with internal crop timing and fertilizer usage data to create an intelligent farming system that is sold to farmers
How can businesses successfully incorporate data monetization?
To understand how a business can incorporate data monetization, consider these pointers:
Understand the role and value of data
In theory, data should facilitate business performance, reduce risk, and prove compliance. However, this can only occur when the business understands the relevance of its data and how valuable it is. Some companies fail to realize this as they do not consider data to be an asset.
Data monetization must be embedded into strategy
Business strategy should always be underpinned by data management initiatives and vice versa. It is important management understands how data is connected to strategy before it implements structures to monetize it.
This requires the company to assemble a cross-functional, multi-disciplinary team with members from sales, marketing, operations, and data management.
Communicating the value of data to facilitate growth
As the studies mentioned in previous sections have demonstrated, data monetisation remains a mystery in some organizations. Even when the practice is in place, employees may not understand the underlying reasons for its success.
Communicating the value of data monetisation to internal and external stakeholders will become paramount in ensuring market competitiveness, among other things.
- Data monetization describes the process of a business using data to obtain an economic benefit. Despite the relative ease with which data can now be collected, the practice remains relatively uncommon.
- Data monetization can be monetized in a few different ways. These include data as a service, analytics-enabled platform as a service, and insight as a service.
- To ensure businesses make the most of their data, it is important they first understand its role and value. Data and business strategy must also support each other and the value of data should be understood by internal and external stakeholders.
Connected Business Model Types And Frameworks
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