Gretel.ai | Making Data Work | Summary and Q&A

Transcript

Read and summarize the transcript of this video on Glasp Reader (beta).

Summary

In this video, Ali Gulshan, CEO and co-founder of Gretel AI, discusses the company's mission to remove the data bottleneck and provide fast and easy access to safe data through synthetic data. He explains the inspiration behind Gretel, the early conversations with investors and users, and the advantages of using synthetic data. He also touches on the use cases in healthcare, web 3.0, and other industries, as well as the future focus areas for the company.

Questions & Answers

Q: What does it mean when people say Gretel is the GitHub of data?

Gretel being called the GitHub of data refers to the company's aim to unlock and remove the bottleneck for data, similar to how GitHub unlocked coding and computing bottlenecks for engineers. Like GitHub, Gretel provides tools that allow organizations of any size to run experiments, collaborate, and build with data, democratizing the process of working with data.

Q: How did the founders of Gretel identify the data bottleneck issue and decide to work on it?

Ali Gulshan and his co-founders, Alex Watson and John Myers, came from the intelligence community and had firsthand experience of the challenges faced in working with limited access to data. They also experienced the advantages of having abundant data in larger companies. They noticed that larger companies were building walled gardens around their data, creating a major advantage for themselves. With the increasing importance of data in various industries, they saw the need to remove the data bottleneck and make advanced data tools accessible to everyone.

Q: Who were some of the early investors and users that helped Gretel build its product roadmap?

Sridhar Ramasamy from Greylock Partners joined Gretel as an investor during the seed round and played a key role in the early conversations. The conversations with early investors and users revolved around understanding the main questions users and customers needed to answer in order to remove the data bottleneck. Through these conversations, Gretel identified the need for tools that assess data quality, enable broader access to data, and ensure privacy. The insights gained from early users and customers helped shape the product roadmap.

Q: What are some advantages of using synthetic data?

Synthetic data can often yield better results than raw data, especially in cases where the raw data is missing columns, improperly classified, or labeled. Synthetic data goes through proper labeling and transformations, resulting in higher quality and better privacy. The tooling approach of Gretel, with better labeling and transforms, contributes to the superiority of synthetic data. By focusing on data quality, privacy, and use cases, Gretel aims to showcase the advantages of synthetic data and change the perception that raw data always yields better results.

Q: How does Gretel plan to make data engineering accessible to a broader set of users?

Gretel aims to make data engineering easy and safe to use for everyone, including individuals and smaller companies that may not have the resources to invest in specialized data teams. The platform provides a low-code, no-code approach to data engineering for tasks such as synthetics, labeling, and transforms. Users can sign up on the website, drag and drop their data files, and leverage the toolkit provided by Gretel. The focus on usability and user experience allows users to work with data without requiring extensive expertise.

Q: Are there any applications of Gretel in the field of healthcare?

Yes, healthcare and health sciences are areas where Gretel has found significant use cases. During the COVID-19 pandemic, the removal of red tape allowed health sciences, pharma, and hospitals to recognize the benefits of faster work with data. Gretel has been working with companies like Illumina, the world's largest genomics company, to build synthetic data sets for genotype and phenotype analysis. They have also collaborated with the University of California, Irvine on improving the detection of female heart disease. By combining synthetic data with other forms of data, Gretel enables more accurate predictions and research in healthcare.

Q: How does Gretel enable collaboration and data sharing in the healthcare field?

Gretel's platform allows users to generate synthetic data that can be shared and collaborated on, while ensuring privacy and providing a quantified view of differential privacy. This becomes especially valuable in healthcare, where regulations and privacy concerns are paramount. For example, researchers working on skin cancer can generate variations of skin cancer images and combine them with medical data, allowing for better context and decision-making. Synthetic data helps bridge the gap between different data types and enables collaboration among healthcare professionals and researchers.

Q: Are there any applications of Gretel in the web 3.0 space?

Gretel has started to see an uptake in web 3.0 companies, particularly in the areas of financial services and web3 gaming. These companies often face challenges with limited or sparse data and require better forecasting or testing capabilities. Synthetic data can help them generate high-quality data for better predictions. Gretel has been working with web3 gaming companies to provide synthetic data for their test networks, where the data needs to mimic the production data on the public blockchain. The unique data needs of web 3.0 companies create interesting use cases for synthetic data.

Q: What are the future focus areas for Gretel?

Gretel is focused on becoming a single platform for all types of synthetic data, allowing users to combine different data types, such as tabular, image, and visual data, for accurate predictions and training of ML and AI systems. They aim to provide deep visibility and usability for synthetic data, enabling users to make data operational quickly and easily. Additionally, Gretel is investing in usability and integration, aiming to become an easy-to-use tool that seamlessly integrates with other services and tools, such as storage systems, data lakes, and orchestration platforms. The goal is to automate complexity and make the platform a cohesive part of the larger data ecosystem.

Q: Who is Gretel looking to hire currently?

Gretel is rapidly expanding and looking to hire across various departments. They are particularly focused on hiring in engineering and applied research. The applied research team plays a vital role in the company, ensuring the development of advanced data tools and techniques. Gretel also places importance on building a people-friendly organization, and they are investing in their talent team to support this goal. As Gretel continues to grow, they aim to prioritize usability, integration, and scalability in their hires to meet the needs of their expanding user base.

Takeaways

Gretel's mission is to remove the data bottleneck and provide fast and easy access to safe data through synthetic data. Their platform, often referred to as the GitHub of data, democratizes the process of working with data and enables collaboration and sharing. Synthetic data offers advantages over raw data in terms of quality and privacy, and Gretel aims to educate the market about its potential. The company has seen success in healthcare, web 3.0, and other industries, and plans to become a single platform for all types of synthetic data. They are rapidly expanding and hiring across departments, with a focus on engineering and applied research. The goal is to make data engineering accessible to a broader set of users and build a cohesive tool that integrates with other services.