DTS (Data Transform System), Your Business Data Solution: The Ultimate Key to Success (11/03)
Table of Contents
Business Data
Introduction
Data has been important since before the Fourth Industrial Revolution. However, its significance has grown even more pronounced since the onset of this revolution around the early 2010s. As information across various industries has become digitalized, the volume of data has increased exponentially, providing more opportunities for analysis and utilization. This enables businesses to analyze consumer behavior and market trends, improving decision-making and offering personalized services, thereby enhancing their competitiveness. Additionally, advancements in artificial intelligence and machine learning have further underscored the importance of data.
Barriers to Effective Data Use: What Prevents Companies from Harnessing Its Power?
However, many companies cannot share high-utility data externally due to internal regulations and legal restrictions related to the risk of sensitive information leaks. In sectors like finance and defense, even data movement between affiliates or departments can be challenging. Data protection laws are becoming increasingly stringent across the United States, Europe, and Asia, making it difficult to leverage business data without legal or security concerns.
CUBIG offers solutions to address these issues!
One of CUBIG’s core solutions, the DTS(Data Transform System), generates private synthetic data that retains the statistical properties of the original data while excluding sensitive information. This enables safe sharing of necessary information and data with external parties. Furthermore, synthetic data can be generated on the company’s local servers, eliminating the need to share original data during the creation process. This allows for the creation of unlimited business data that is free from legal and security concerns.
Overview of CUBIG’s Solutions
CUBIG’s private synthetic data generation solution, DTS, can be accessed through a subscription-based SaaS model or an on-premise deployment. Depending on the service delivery method, customers can enjoy different benefits and target use cases:
SaaS Model (Build Your Own Business Data, All Subscription Long)
Customers pay a monthly subscription fee to access unlimited usage of DTS’s generation capabilities during the subscription period. By utilizing data access technologies for synthetic data generation, customers do not need to worry about the export of original data. However, they will need to receive portions of the securely processed synthetic data. This model is primarily anticipated for use by healthcare institutions, IT companies, and businesses utilizing AI.
On-Premise Model
Leveraging a server policy that prevents the transmission of the actual data, this service allows companies or institutions that cannot share even securely processed data to generate synthetic data on their local servers. This model is expected to be used mainly by military organizations, banks, and securities firms.
Conclusion
CUBIG is comprised of a workforce with over half holding advanced degrees, including PhDs. Based on their academic knowledge and expertise, CUBIG has accumulated significant expertise in AI and data security. This has led to successful development outcomes and continuous validation of the CUBIG AI engine’s reliability through various PoC references with partners. The DTS solution enables governments, financial institutions, and businesses to utilize high-value data more securely. Additionally, it provides business data tailored for you, no matter your industry, meeting specific requirements across sectors such as finance and healthcare. This ensures the acquisition of high-quality data without the risk of security breaches or legal disputes.
If you’re interested in leveraging the DTS secure synthetic data generation solution mentioned in this post for your business data, or if you simply find DTS intriguing, you can visit the homepage and solution page of CUBIG, the developer of DTS, through the link below.