10 ways to externally monetize your data and systems

This includes selling raw, aggregated, or processed data (cleansed, tagged, or even correlated data) that you own. It may also involve selling data of which you are not the original owner, but you must first ensure that you have the legal right to sell the data to a third party.

Telecom firms can sell user data that displays foot traffic to companies that want to open a physical store in a specific geographic location.

Selling individual analytical models for standalone use, integration into another system, or other appropriate applications.

Banks can sell the analytical models they use to identify fraudulent transactions to other financial institutions.

Sell machine-learning-based models that may be used as standalone products or incorporated into current software to improve its output.

A company can sell its NLP model to help other companies summarize large volumes of text into summaries or abstracts for easier consumption.

You can sell these by creating your own company, or you could sell the on AI model marketplaces like Gravity

Using data from multiple companies in a specific industry or business segment to compare features like market penetration, customer rating, or adherence to a specific applicable standard.

Deallogic analyses and organizes financial data from banks to provide insights. For example, it generates an investment scorecard that assesses global investment firms based on deals, competitor ranking, and industry breakdown.

Companies can spin off internal software solutions to external companies in the form of SaaS. For example, the firm Bootcamp began as a project management system that was utilized internally before being abstracted out to the public.

Spinoff your analytics practice as a separate consultancy.

This is especially common with larger organizations that have a lot of data and the in-house capability to process it but lack the focus or understanding of how to commercialize it. An example is Accenture's purchase of Slalom's Analytics Practice.

We've seen these companies, such as Amazon (Amazon Web Services) and Google (Google Cloud Platform). Why not break out their functional infrastructure and sell it to the public as a separate business?

A company can sell access to its data or application programming interface (API) to allow other companies to build off its platform. For example, Open API sells access to GPT-3 model using a tolled access (billed on token size)

Bringing various businesses together in multiple marketplaces for the exchange and sale of data products—in other words, providing a convenient place to meet and discuss.

An example is Amazon Data Exchange

Synthetic data is data that isn't obtained by seeing or measuring real-world processes. It can be created randomly, from real datasets or through mathematical simulations. Synthetic data should match real-world data in statistical and structural aspects. It has the same attributes and schema as real data. In some cases, synthetic data can outperform real-world data. You'll need synthetic data to train and test an AI or data science application. You can also build non-identifiable datasets via synthetic data generation.

If you're creating an AI or data science application, you'll almost certainly require synthetic data to train and test the system. Synthetic data marketplaces can give you test/training data to create an AML risk model.

For example, a bank can create synthetic data to sell or license to a synthetic data exchange that simulates AML data that you have in your data warehouse. That would be of value to other data scientists.