Data Monetization: The Role of AI and Machine Learning in Creating New Revenue

data monetization market

Organizations are financially motivated to securely share their internal data through data monetization. It establishes the financial framework for an ecosystem of open data sharing, presenting new business prospects to companies in a range of industries. Companies can safely build rapidly expanding data-as-a-service (DaaS) businesses by sharing data by privacy and data protection regulations. In addition to maximizing the value of currently available resources, this approach safeguards the original data rights holder and fosters innovation and better decision-making. In the end, companies that carefully consider data monetization can strike a balance between financial gain and wider social advantages, enhancing their competitiveness and building long-term trust.

Organizations have begun to recognize data monetization as a crucial approach towards enhancing earnings, promoting innovation and staying ahead of competitors. Organizations can harness their data assets to produce rich products, services, and insights, and ultimately achieve tangible business results with the help of Machine Learning Models and Artificial Intelligence. However, strategic supplier selection, tools, and data management techniques are just as important to the success of data monetization initiatives as advanced analytics and AI technologies. Furthermore, seamless data accessibility and business continuity depend on having a strong data lake.

Moreover, in an organization, every department has requirements for data and data democratization ensures that every data requirement is met. On the other hand, Data monetization is more focused on making use of data for turning into cash. Data democratization refers to the provision of data to organizational employees regardless of the level of technical knowledge possessed by the employees, based on which decisions are made at all levels. For instance. research published by the Harvard Business Review reports that 91% of all the respondents considered it helpful to their companies to democratize data and analytic tools. When data is accessible to a larger segment of the organization, the rate of innovation is higher and the quality of the decisions made by teams is superior. Silos are broken down by this democratization, which guarantees that data is no longer solely the purview of IT departments or data scientists but rather that sales, marketing, product development, and even customer support teams can use it as a resource. Additionally, the increased use of online transactions and increased use of the internet for using all these services is also increasing the role of AI in generating revenue for many businesses.

Figure 1:  Number of Internet Users, Global, in Billions, 2022 to 2023

number of internet usres

Source: International Telecommunication Union

However, the role of AI and machine learning in data monetization for revenue generation are as follows:

  • Advanced data analytics
  • Predictive and prescriptive analysis
  • Personalization scale
  • AI-powered data products and services
  • Real-time decision making
  • Data security and privacy

Let’s discuss each one in detail.

1. Advanced data analytics

It is a central feature of AI and ML that allows them to contribute to the monetization of data through data analysis. It is often more beneficial to use AI and ML algorithms in processing large and complex datasets than conventional methods. Hence, they tend to find correlations, trends, and patterns that heavy volumes of data contain, which human analysts would not even dream possible.

Using historical sales data, consumer behaviour, and seasonal trends predictors can also help store owners ascertain customer purchasing habits in the future. This helps the companies to fine-tune their supply chains and make better projections on inventory which in the long run helps increase their sales revenue.

2. Predictive and prescriptive analysis

AI and ML not only enable just descriptive analytics but prescriptive and predictive analytics as well. By predictive analytics which studies the current observations and historical data to make forecasts about the future, businesses can know what their clients would like, understand the market landscape, and how to avoid crises.

Moreover, banks use predictive modelling to help control risky behaviours such as fraud before they occur, thus limiting overall losses. Predictive analysis deals with predictions and forecasts and prescriptive analysis provides a tangible explanation of why one should make a choice. For purposes of illustration, a pricing algorithm may suggest the most profitable price positions and strategies depending on the current state of the market.

3. Personalization scale

One of the most significant advantages of AI and ML in data monetization is the capability to create unique experiences for each consumer at scale. Personalized content, targeted advertisements, or tailored-driven product suggestions are only some of the services offered to consumers in today’s world.

Machine learning detection occurs, which causes them to provide extremely specific suggestions even when the user is simply looking for a book. For example, a service like Netflix will analyze you as a viewer to offer suggestions based on viewers like you, rather than suggesting a random collection of features.

Further, just as machine learning here analyzes user behaviour within an application, identifying and suggesting relevant content to the user, which in turn increases the time and engagement of the user within the application. In addition to increasing sales, this personalized approach also enhances satisfaction and loyalty towards the company.

4. AI-powered data products and services

The advent of artificial intelligence and machine learning has been fast-tracking the emergence of new data products and services for the market. An example would be the AI-as-a-Service ( AIaaS ) model, whereby companies can offer businesses with AI models, tools and other solutions. Typically, such platforms are built in such a way that enables the users to enjoy the machine learning technologies without the need to have an internal expert.

Furthermore, companies are also able to build data-centric offerings such as, but not limited to, automated trading systems, smart chatbots, and predictive maintenance solutions and sell them under a subscription model. In this case, the existing data assets gain enhanced value while new streams of revenue are also established.

5. Real time decision making

The new age of human computing powered by technologies like Artificial Intelligence and machine learning is making it possible for organizations to rely on live data feeds for almost instant decision-making. This is essential, especially in industries like finance where every single activity including trading or even spotting a fraud takes place in seconds.

AI models within this context can execute decision-making processes, reduce the incidence of human error and also take advantage of data-driven opportunities by performing on-the-spot analysis. For example, funds hedge via AI algorithms to monitor thousands of market data points per second before placing a trade and profiting from market movement. Moreover, real-time AI-powered insights are also applicable in dynamic pricing viability where organizations change their prices based on competitors’ prices, the stock or the level of demand at that instant. This approach enables firms to realize more profits by making sure that the appropriate product is available at the exact time needed.

6. Data security and privacy

Machine learning algorithms, for instance, can be employed to prevent and detect cyber threats in computer networks by monitoring and identifying reactive behaviours that may indicate a possible infiltration. Furthermore, companies can also leverage AI technologies to help protect sensitive data by enabling data masking without impeding the generation of critical information.

In conclusion, The strategies that businesses deploy to monetize data generated by their operations are changing significantly due to the emergence of adaptable and flexible Artificial Intelligence and Machine Learning technologies. Companies can extract more value from their data resources through the deployment of AI for advanced analytics, personalization, real time responses, and new data product developments among other aspects. However to successfully integrate this new strategy in business operations, within the overarching strategy of the organization, several challenges have to be addressed we can refer to these as barriers to data monetization, these include issues relating to data quality, data privacy, cost and lack of proficiency. As the field of AI and ML grows, the scope for development and growth in data monetization will widen, transforming the sectors as well as the economy in the process.