Today, industries are witnessing a massive data shortage caused by data privacy laws limiting the effectiveness of the development and training of AI/ML models. While data is anointed as fuel for innovation in the digital age, lack of real-time data hinders thorough training of artificial intelligence prototypes and systems. To combat this, enterprises are turning to vendors that can help in leveraging synthetic data to address the problem of sparse data. Datomize is one such company whose synthetic data revolutionizes the AI/ML and IT lifecycle by removing the major bottleneck that prevents the successful deployment of AI/ML models and continuous delivery of evolving applications.
In an interview with Analytics Insight, Sigal Shaked, Founding Partner and CTO of Datomize shared her valuable insights on how the company is enabling financial institutions to generate enterprise-grade synthetic data while protecting data privacy.
Analytics Insight: Kindly brief us about the company, its specialization, and the services that your company offers.
Sigal Shaked: Datomize generates fully compliant synthetic data to improve the quality of insights gleaned from analytics and AI/ML models and increase flexibility for agile and rapid product development. A patentable algorithm creates multiple table schemas that preserve the original data sets’ main behavioral features while ensuring compliance with privacy regulations. Preferable to encryption methods, there’s no function or mathematical operation that can reconstruct the original data.
AI: Brief us about yourself and your contributions towards the company and the industry
Sigal: While pursuing my Ph.D., I researched a better way to make data compliant for analytics, cloud migrations, and product development, and to enable data owners to publish their datasets for research. I used the knowledge I gained through these years to design Datomize’s technology.
AI: Kindly share your point of view on the current scenario of Big Data Analytics and its future.
Sigal: Not so long ago, the main challenges with big data analytics focused on data collection, storage, and integration. Now that we have solved these problems we have opportunities to extract insights from this new data, but we still face two obstacles; privacy regulations prevent us from using all the data we collected, and sometimes we are eager to generate insights before we have sufficient amounts of data. Despite all the promise of machine learning, extracting insights from small datasets is severely limited. For this reason, I believe that generating alternative or complementary synthetic data has an important place when it comes to the future of big data analytics.
AI: What is your biggest USP that differentiates the company from competitors?
Sigal: Designed based on real customer data from global banks, Datomize is uniquely able to automatically generate high-quality complex data that improve the accuracy and power of insights gleaned from AI/ML models. Datomize simplifies and streamlines the generation and management of synthetic data with visual tools so that highly trained and hard-to-find data scientists can focus on analysis and strategy. Datomize’s scalable solution extracts the behavior from big data sets, and it supports multi-table schemas while preserving cross-table relationships.
AI: What are your growth plans for the next 12 months?
Sigal: We are currently conducting product trials in Europe and the US with several financial institutions. Our goal is to provide a suite of data-generation solutions that includes data augmentation and assists with data collaboration.
AI: What is your vision for the future of data?
Sigal: I believe that obtaining the data enterprises need for analytics will be simple in the future. Data synthesis will become as easy as copy-paste. Data generation will be embedded in the development process, taking place behind the scenes. AI/ML models will have the input they need, and data generation will become a trusted process instead of a roadblock.
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