Unlocking Innovation with Synthetic Data: A Solution for Data-Driven Organizations

Thank you for reading my latest article Unlocking Innovation with Synthetic Data: A Solution for Data-Driven Organizations.?

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Introduction

In today's data-driven world, innovation and privacy are two sides of the same coin. We understand that harnessing the power of data is crucial for staying competitive, yet preserving data privacy is equally vital. In this blog post, we'll delve into the world of synthetic data, a powerful solution that bridges this gap, and how Snowflake, with its Generative AI capabilities, transforms data management.

Recently I was working on a project when the customer needed to conduct a performance test of the solution using the same volume and shape of production data. Typically zero copy cloning in Snowflake would be ideal to quickly create this environment. But not in this case, as, due to the sensitive nature of the customer’s data neither production or masked production data could be used. Instead I needed a different solution - which is when synthetic data comes in.

What is Synthetic Data?

Synthetic data is a revolutionary concept. It's not real data, but it looks and behaves just like it. It's generated by sophisticated algorithms, allowing organizations to train AI models, perform data analytics, and innovate without compromising sensitive or scarce real data.

Generating synthetic data in Snowflake is actually very straightforward and can be done using nothing more than SQL.

What Problem Does it Solve?

Consider a dataset consisting of thousands of human faces, such as those utilized in the training of facial recognition algorithms. In this scenario, you would need to identify and capture images of thousands of individuals while also obtaining their explicit consent for the collection and utilization of their data. Furthermore, a multitude of rigorous procedures and safeguards must be meticulously adhered to in order to prevent any potentially harmful biases from being introduced into the dataset.

Synthetic data offers a solution to the challenges of data availability and privacy. It eliminates the need to tap into sensitive or restricted datasets, making it easier to comply with data privacy regulations while accelerating innovation. It offers a safe environment for testing and experimentation.

By generating synthetic data, companies can craft customized information to fill voids within current records or establish entirely new and unique datasets. Importantly, this approach does not replace the necessity of real-world data, as it serves as the foundational basis for generating synthetic data. However, when employed skillfully, synthetic data can yield multiple advantages, including cost reduction, acceleration of machine learning model training, and facilitation of automation, ultimately leading to improved decision-making within businesses.

How Snowflake Data Marketplace and Generative AI Can Help

While Synthetic data existed before the rapid emergence of Generative AI, Snowflake allows you to take synthetic data to the next level with Generative AI. This new class of algorithms can leverage Generative AI and the elastic scalability of Snowflake to create huge datasets very quickly. This allows you to not only create synthetic datasets but also enables natural language queries, making data exploration more accessible and efficient. Snowflake's platform offers a secure and scalable environment to generate, store, and manage synthetic datasets, revolutionizing the way organizations use data.

A data marketplace is a digital platform or marketplace where organizations and individuals can buy, sell, exchange, or trade various types of data. These marketplaces facilitate the exchange of data assets, allowing data providers to monetize their data and data consumers to access valuable information for various purposes, such as research, analysis, marketing, and more. Snowflake Data Marketplace - one of the largest in the world - allows organizations to discover, access, and share third-party data sets and data services directly within the Snowflake platform.

On Snowflake’s Marketplace are a handful of companies looking to exploit these new feature such as Synthesis AI which provides a synthetic human faces dataset consists of 5,000 close-up images of diverse identities with detailed annotations such as semantic segmentation, facial landmarks, and surface normals. The images also contain a variety of backgrounds and lighting, and many different types of clothing, hair styles, and accessories. Because the dataset was developed using generative AI and cinematic CGI pipelines, there are no privacy or copyright issues.

Fraud detection in mortgage applications are also catered for with Clearbox AI who provide a synthetic dataset designed to simulate mortgage applications in a banking context, with the aim of identifying potentially fraudulent instances.

Or how about training your ML models to understand and read PDF invoices? Well Innodata provide synthetic invoices for just that purpose! Each data set is a compilation of handmade templates based on real-world examples (bank statements match recent versions from real banks, etc.), all sourced with ethical data practices. All files are representative of clean-scanned readable PDF documents for easy ingestion into annotation platforms.

Risks and Challenges

While synthetic data is a game-changer, it's not without its challenges. Ensuring that synthetic data accurately represents real-world scenarios and doesn't introduce biases is a critical concern. Therefore, it's essential to employ robust algorithms and rigorous validation processes. Additionally, maintaining data privacy and adhering to evolving regulations remains a challenge that requires constant vigilance.

Conclusion

In summary, synthetic data offers organizations a remarkable opportunity to drive innovation without compromising on data privacy and availability. By leveraging platforms like Snowflake with Generative AI, we can navigate the evolving data landscape with confidence. Let's continue the conversation on how synthetic data can empower your organization's growth and innovation. Your insights and leadership in this arena will shape the future of your industry.

To stay up to date with the latest business and tech trends in data and analytics, make sure to subscribe to my newsletter, follow me on LinkedIn , and YouTube , and, if you’re interested in taking a deeper dive into Snowflake check out my books ‘Mastering Snowflake Solutions ’ and ‘ SnowPro Core Certification Study Guide’ .

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About Adam Morton

Adam Morton is an experienced data leader and author in the field of data and analytics with a passion for delivering tangible business value. Over the past two decades Adam has accumulated a wealth of valuable, real-world experiences designing and implementing enterprise-wide data strategies, advanced data and analytics solutions as well as building high-performing data teams across the UK, Europe, and Australia.?

Adam’s continued commitment to the data and analytics community has seen him formally recognised as an international leader in his field when he was awarded a Global Talent Visa by the Australian Government in 2019.

Today, Adam works in partnership with Intelligen Group, a Snowflake pureplay data and analytics consultancy based in Sydney, Australia. He is dedicated to helping his clients to overcome challenges with data while extracting the most value from their data and analytics implementations.

He has also developed a signature training program that includes an intensive online curriculum, weekly live consulting Q&A calls with Adam, and an exclusive mastermind of supportive data and analytics professionals helping you to become an expert in Snowflake. If you’re interested in finding out more, visit www.masteringsnowflake.com .