Table Of Contents
Unlocking the potential of your data is vital to driving revenue and growth for your business. But with so many monetization models, it can be overwhelming to determine the best approach. This article will explore the different models and help you answer, “Which data monetization strategy is right for your business?
From data brokering to pricing data products, we’ll dive into the critical elements of each data monetization business model, the potential benefits and challenges, and best practices for implementation.
Data models are mathematical representations of real-world scenarios and can be used to make predictions and analyze patterns in data. These models can be commercialized by offering them as a product or service, allowing organizations to use their insights without developing their models from scratch. Commercializing data models can take many forms, from selling access to the model itself to offering consulting services that use the data sharing business model to provide insights and recommendations. Let’s delve into how we can commercialize data models, including their pros and cons.
Commercializing Data Model
The buying and selling data models present many opportunities for organizations to drive revenue and gain a competitive edge in their respective industries.
| Type of Data Licensing | Pros | Cons |
| Pay-as-you-go | 1. Flexibility in usage and payment
2. Pay only for what you use. 3. No commitment or long-term contract |
1. Lack of bulk discounts
2. Higher cost per data unit 3. Difficulty in predicting monthly expenses |
| Individual Data Product Subscription | 1. Discounts on individual data products
2. Dedicated support and service for individual products 3. Consistent pricing |
1. Limited flexibility in usage
2. It may not be cost-effective for infrequent use 3. Difficulty in finding the right package |
| Group of Data Products Subscription | 1. Discounts on a group of data products
2. Increased flexibility in usage 3. Consistent pricing for multiple products |
1. Commitment to multiple products
2. Difficult to add or remove products 3. Higher upfront cost. |
When choosing a data licensing model, it is crucial to consider the trade-off between cost, flexibility, and support. Organizations should also consider their data usage patterns, budget constraints, and any specific requirements they may have when evaluating which data monetization business model is right for them. By understanding the pros and cons of each type of data subscription model, organizations can make informed decisions that align with their business goals and help them succeed.
Productizing and Monetizing Data
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How High-Priced Data Assets Lead to a High-Touch Sales Cycle
Higher-priced data assets can lead to a high-touch sales cycle because data is often seen as more valuable, resulting in a longer sales cycle as potential customers take more time to evaluate the potential value of the data to their business. Additionally, the sales process for high-priced data assets is often more complex than for other types of products because the potential customers may have more specific needs and requirements for the data.
A high-touch sales cycle involves multiple touchpoints between the sales representative and the potential customer. This may include initial discovery calls to understand the customer’s needs and requirements, presentations of the higher-priced potential benefits, demonstrations of the data in action, and in-depth discussions of pricing and contract terms.
The high price of the data asset can also require a more focused and personalized approach to selling, as potential customers are more likely to want a clear investment return. This may involve tailoring the data offering to meet the customer’s specific needs and providing detailed information on how the data can be used to achieve their goals.
Although data assets that are priced higher often require more hands-on sales, monetizing data assets that are priced higher often leads to a good ROI. Depending on the data a business sells, it’s common to have a mix of high and low-priced datasets available for purchase on a data marketplace.
How Lower Price Data Assets are Marketing-Led and Self-Serve
Lower-priced data assets are often marketed and sold in a marketing-led and self-serve manner. These datasets are often more straightforward than higher-priced ones, so it’s easier for the data consumer to feel they have the information they need to purchase. In these cases, the focus is on making the data widely available to many potential customers to generate significant revenue through volume sales.
Marketing-led sales typically involve using digital marketing channels, such as email, social media, and search engine advertising, to reach a large number of potential customers. The focus is on creating awareness and interest in the data and driving potential customers to a self-serve platform where they can learn more about the data and purchase it on their own.
Self-serve platforms allow customers to easily find, evaluate, and purchase data assets without directly interacting with a sales representative. Revelate provides this functionality through a fully customizable data marketplace solution. With Revelate, tailored storefronts can be shown to users based on their credentials, allowing them to view the data products they are authorized to purchase. If a user is authorized to purchase a portion of a dataset only, the marketplace’s automated checks and balances ensure that they can still gain instant access to the data they can
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Lower-priced data assets are often seen as more accessible and easier to use. The marketing-led and self-serve approach to selling these assets helps keep the cost low for the customer while still providing a convenient and efficient way to access the data.
Operational Improvements Model
An indirect data monetization business model, which can also be referred to as an operational improvements model, is when an organization leverages the insights from data to make improvements across the organization, including products, services, and of course, operations.
- Automating Tasks: Data automation refers to using machine learning algorithms to analyze and identify patterns in data, allowing for the development of automated systems that can perform repetitive tasks efficiently and accurately.
- Improving Processes: By analyzing data and identifying areas of improvement in business processes, organizations can make changes to optimize and streamline their operations. For example, identifying bottlenecks in the supply chain and addressing them can lead to increased efficiency.
- Faster Decision Making: Organizations can make faster and more informed decisions by accessing real-time data. This is especially true in finance, where quick decision-making is essential. Machine learning algorithms can also be used to analyze data and provide recommendations, further speeding up decision-making.
- Making Processes Visible: By collecting and visualizing data, organizations can better understand their operations and make processes more transparent. This can help identify areas for improvement and increase accountability within the organization.
Leveraging data can improve productivity, efficiency, and transparency across an organization, leading to significant data monetization opportunities.
Partner Sharing and Improving Relationships Model
The partner sharing and improving relationships model is an internal data monetization strategy that aims to enhance partnerships and relationships between companies by sharing customer and market data.
- Understanding a shared customer or market: Companies that work together often share similar customer segments and target markets. By sharing data on customer behavior, buying patterns, and preferences, companies can better understand their shared customer base, allowing them to make data-driven decisions about how to serve these customers better.
- Sharing data for more favorable contract terms: Companies can negotiate more favorable contract terms and conditions. This could include improved pricing arrangements, better payment terms, or more favorable supply chain agreements. Sharing data can build trust between companies, making it easier to negotiate mutually beneficial deals.
- Insights into supply chains: Sharing data on supply chain operations can help companies identify inefficiencies and improvement opportunities. This could include reducing waste, improving logistics, or streamlining processes. By sharing this data, companies can work together to improve their supply chain operations, resulting in cost savings, improved efficiency, and more sustainable business practices.
By promoting open communication and trust between companies, companies can work together to achieve long-term benefits and mutual success.
Innovation Model
Similar to the partnerships and sharing model, the innovation model highlights how data sharing can lead to new ideas and developments in business and industry. The key benefits of this model include the following:
- Stimulating creativity and innovation: Sharing data can spark new ideas and perspectives, leading to fresh thinking and innovative solutions. By exposing companies to new data and insights, they can challenge their assumptions and create new solutions to problems.
- Encouraging collaboration and teamwork: Data sharing promotes collaboration between companies, enabling teams to work together to find new solutions. By sharing data and insights, companies can build a common understanding of a problem and collaborate to develop innovative solutions.
- Access to new data and information: Sharing data can provide companies with access to new information and insights they may not have otherwise had. This can lead to discoveries, new products, and new business opportunities.
- Improved decision-making: By sharing data, companies can make more informed decisions, reducing the risk of making decisions based on limited or inaccurate information. Data sharing can improve decision-making, better outcomes, and tremendous success.
Now that we have highlighted different models let’s look more into what you should know to choose the data monetization business model that fits best for your organization.
Which Data Monetization Model Should Your Business Choose?
The choice of which data monetization business model to choose will depend on your business’s specific needs and goals. Here is a brief overview of each model to help you decide.
| Data Monetization Business Model | Best Used For |
| Commercializing data | Selling data products directly to customers, either through a data marketplace, Data Web Store, or both. |
| Operational improvements | Indirect data monetization where businesses apply insights to facilitate operational, product, and service improvements. |
| Partner sharing and improving relationships | Data sharing network and relationship building with other organizations, partners, suppliers, distributors, and more. |
| Innovation | Stimulating creativity with data leads to novel inventions, solutions, and new growth opportunities. |
The suitable data monetization business model will ultimately depend on your business’s specific needs and goals. It is essential to carefully consider your business objectives and the value of your data assets before making a decision.
For instance, let’s say you’re an organization with steady and reliable revenue streams in place already, but you have the capital to start experimenting with new product and/or service development to keep your company top of mind for your target market(s), and also to contribute to continued growth. A data monetization business model focused on innovation would be a great choice.
On the other hand, perhaps you’re an organization looking for ways to establish a reliable revenue stream to support the business in times of economic uncertainty. Data commercialization would be a good way forward, especially taking advantage of data marketplaces that already cater to a set of data consumers interested in your company’s data products. Typically data mature organizations benefit the most from data commercialization, but any organization of any size can take advantage of selling its data.
For an organization that perhaps isn’t as data mature but still wants to sell or share its data, leveraging a partnership model is ideal. This allows the organization to develop positive relationships with others in their industry, align with a specific type of subject matter, and build their data sharing or monetization reputation through these partnerships. As the organization becomes more data mature, other avenues of data monetization business models can be explored and leveraged easier.
Revelate is a data marketplace solution that can meet the needs of organizations exploring any data monetization approach. For instance, typical data marketplaces limit display flexibility in an effort to maintain a consistent UI with all listings. This can hinder sales of data products that need more context or specific information display to understand fully. With Revelate, you can fully customize the look and feel of your marketplace to suit the data products that you sell, making it easier for your customers to find and understand your data product offerings.
Unlock Your Data's Potential with Revelate
Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!
Next, let’s explore the benefits of utilizing a data monetization platform.
Benefits of Utilizing a Data Monetization Platform for Business
Utilizing a data monetization platform can offer numerous benefits for businesses, including
- Revenue generation – A data monetization platform enables businesses to monetize their data assets by selling them to interested parties, thereby generating additional revenue. This can include selling data directly to other businesses or third-party data brokers. For example, a company that collects customer data through its online platform could use a data monetization platform to sell that data to marketers or other companies interested in analyzing consumer behavior.
- Increased efficiency – By automating the data sales and distribution process, a data monetization platform can increase the efficiency of the data monetization process. This includes streamlining the process of finding potential buyers, negotiating deals, and managing payments and delivery. Automation also reduces the risk of errors and improves the overall accuracy of the data sales process.
- Better data management – A data monetization platform encourages the use of a centralized system for managing and organizing data, making it easier for businesses to track and manage their data assets. This includes data catalog tools, which help to categorize and organize data, as well as the ability to track data usage and sales over time. With a data monetization platform, businesses can better understand their data assets and make more informed decisions about how to monetize them.
- Improved data security – Data monetization platforms often have robust security features in place to protect sensitive data and ensure data privacy. This includes secure data storage and transfer, as well as tools for managing access permissions and monitoring data usage. For instance, Revelate provides holistic security and access management through its partnership with Immuta. This way, adjustments can be made in real-time as datasets change.
- Insights and analytics – Data monetization can provide valuable insights and analytics on the performance of data sales and help businesses make informed decisions. Revelate, for instance, allows real-time tracking of data sales, insights into the demand for different types of data, and detailed reporting on revenue generation. With these insights, businesses can make more informed decisions about monetizing their data assets and optimizing their revenue potential.
- Access to new markets – A data monetization platform can provide businesses with access to new markets and potential customers for external data monetization expanding their reach and increasing their opportunities for revenue generation. By connecting with new buyers and data brokers, businesses can gain access to new markets and generate new revenue streams.
Conclusion
Data monetization is a powerful tool to help organizations generate significant revenue and gain a competitive advantage. Whether it’s through automating tasks, improving processes, making faster decisions, or partnering with others, there are many ways that businesses can monetize their data. Organizations can unlock their full potential and drive growth by leveraging data correctly.
So, what are you waiting for? Explore the data monetization models that best fit your business and take the first step toward unlocking its full potential. By doing so, you’ll be well on your way to a future filled with increased efficiency, improved operations, and, most importantly, increased revenue. Don’t let your data go to waste, contact us to monetize it today!
Unlock Your Data's Potential with Revelate
Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!
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