‘Data Driven’ is probably a word you have heard a thousand times within the last business quarter and rightly so. The world is all about data now. With advancements in technology, intense product personalization, and extreme market competitiveness, every company is now placing their bets on data to guide them on what, when, and where to make the next big business move—making data a tradeable commodity on its own. The data monetization market in 2024 is valued at 4.17 billion $ and is predicted to grow up to 10.35 billion $ by 2030.
This presents a unique opportunity for organizations with excess data to create alternate revenue streams through data monetization that is if they can bypass stringent data privacy and protection laws, and maintain data utility, value, and security by applying extensive data anonymization techniques which in recent time have not been proven to be as effective as they once were.
But we are here to discuss solutions, not problems. And the solution is the much-talked-about synthetic data.
Synthetic data is created through advanced AI models that use deep learning methods to mirror the statistical characteristics of real-world data, all while avoiding any replication of personal information. Techniques like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers are used to analyze the statistical patterns of original datasets that recreate the complex multivariate relationships present in the original data, ensuring no direct connection to individual data points protecting the anonymity of the data sources while preserving its statistical integrity which is why synthetic data can be used effectively for data monetization.
Offering synthetic data as a service allows businesses to provide clients with customized datasets for various purposes such as testing, machine learning model training, or research.
Use Case: Financial Institutions can use synthetic data to test a new fraud detection algorithm, ensuring it performs well under various scenarios without risking exposure to real client data.
Companies can utilize synthetic data to enhance their products and services. In the software industry, synthetic data is invaluable for testing applications under diverse conditions. They can acquire this data through data marketplaces which allows other organizations to monetize their data.
Use Case: A software company uses synthetic user interaction data to test the performance of a new feature across various devices and operating systems, leading to a more reliable and user-friendly application.
Synthetic data facilitates safe data sharing between organizations, enabling collaborative research and development without compromising sensitive information.
Use Case: Two pharmaceutical companies collaborate on drug development by sharing synthetic patient data, accelerating research without violating patient confidentiality.
Organizations can monetize synthetic datasets by selling them for market insights and analytics. These datasets can help businesses identify trends, preferences, and patterns.
Use Case: A marketing firm sells synthetic consumer data to retail companies, helping them to tailor their marketing strategies and product offerings based on identified trends and preferences.
The demand for high-quality data to train AI and machine learning models is ever-growing. Synthetic data can meet this need, offering diverse and extensive datasets that enhance model accuracy and performance.
Use Case: An AI startup uses synthetic data to train a machine learning model for image recognition, achieving high accuracy without the need for large volumes of labelled real-world data.
Read Also: Use Cases for Synthetic Data Across Industries
Scenario:
An autonomous vehicle (AV) company is developing new software to improve the safety and efficiency of its self-driving cars. Real-world data collection from sensors and cameras is time-consuming, expensive, and can involve privacy concerns.
Problem:
The AV company needs large volumes of diverse and high-quality data to train and validate its machine learning models, but using real-world data presents challenges related to data privacy, collection costs, and coverage of all possible driving scenarios.
Solution: Synthetic Data as a Service:
Benefits:
Implementation Example:
The AV company subscribes to a synthetic data service, specifying the types of scenarios and conditions needed. The service generates and delivers the synthetic datasets, which the company uses to train and validate its machine learning models. Over time, as the AV software improves, the company can request new datasets to test and refine its algorithms further, ensuring continuous improvement and safety of its autonomous vehicles.
Scenario:
A retail fashion brand wants to understand the latest trends in consumer preferences to adjust its product lines and marketing campaigns accordingly.
Problem:
The brand struggles with acquiring detailed consumer data due to privacy laws and the high cost of real data collection.
Solution: Synthetic Consumer Data:
Benefits:
Implementation Example:
The retail fashion brand subscribes to the marketing firm's synthetic data service. The firm generates and delivers synthetic consumer datasets, which the brand analyzes to identify key trends and preferences. Based on these insights, the brand adjusts its product offerings and marketing strategies, increasing sales and customer engagement. Over time, the brand continues to use synthetic data to stay ahead of market trends and maintain a competitive edge.
And there you have it—your ticket to creating new revenue streams for your business through data monetization with data synthetic data. Synthetic data’s versatility combined with its privacy-protecting quality makes it a front contender for any company looking to add value to its business.
