Data is the fuel of generative AI. Vast amounts of data and the cloud’s crucial ability to store and process it at scale have led to the rapid rise of powerful core models. If companies can bring together their scattered data and make it all available, they can easily refine these models or use retrieval augmented generation (RAG) to tailor them to their business needs.
However, the relationship between data and AI goes both ways. AI can also be used to improve and enhance your data and make it available for analysis.
Even though businesses have invested heavily in data in recent years, they often find that it is not enough. The rise of AI has drawn attention to the gaps in their data and the difficulties of access or interpretation. Data can be isolated in organizational silos; it may be incomplete or of poor quality, making it difficult to work with.
Below are three examples of using AI to power your data rather than the other way around. Use cases like these can bring you quick wins while generating value from your data assets.
Reduce extremely tedious work (ETL!)
One of the most resource-intensive tasks of any data project, often consuming as much as 60-70% of the effort, is preparing and moving data to be used for analysis, also known as processes extraction, transformation and loading (ETL). . This is why AWS is working on a zero-ETL future.
Fortunately, generative AI can be used to automatically analyze source and target data structures and then map them into each other. AWS AI Generative Coding Assistant, Amazon Q Developercan build data integration pipelines using natural language. This not only reduces the time and effort required, but also helps maintain consistency across different ETL processes, making ongoing support and maintenance easier.
Businesses often have both structured (e.g., customer profiles and sales orders) and unstructured (e.g., social media or customer reviews) data maintained in a variety of data sources, formats, schemas, and types. THE Integrating Amazon Q Data into AWS Glue can generate ETL jobs for more than 20 common data sources, including PostgreSQL, MySQL, Oracle, Amazon Redshift, Snowflake, Google BigQuery, DynamoDB, MongoDB, and OpenSearch.
With generative AI for ETL and data pipelines, data engineers, analysts, and scientists can spend more time solving business problems and learning from data, and less time defining plumbing. This is a generative AI use case that most businesses can get started on right away.
Generative BI: better information, faster
We often talk about democratizing data within an organization, that is, removing it from the hands of specialists and making it accessible to everyone. Data analysts and data scientists often find themselves faced with large and complex projects, which limits their ability to provide daily, actionable insights to everyone. However, a barrier to democratization is that not everyone has the skills to work rigorously and creatively with data.
With generative AI, you can interact with your data using conversational queries and natural language without having to wait for someone to build reports and dashboards to find insights, reducing time to value . For example, a retail manager might ask, “What were our best-performing product categories last quarter and what factors contributed to their success?”
Regional supply chain specialists at BMW Group, a global manufacturer of premium automobiles and motorcycles, used the generative AI assistant Amazon Q in QuickSight to quickly respond to requests for supply chain visibility from key stakeholders, such as board members.
Data has the power to influence change, but it requires compelling storytelling. Generative AI can make data easy to use and enjoyable to use by creating visually appealing documents and presentations that bring data to life. Another benefit is that it can help people in the organization become more familiar with the data and its interpretation, making the data useful for more complex AI applications.
Synthetic data: get the data you want
As companies advance in analytics and AI, many find they lack the data needed to support the new use cases envisioned. And acquiring third-party data can be extremely expensive. Additionally, in regulated industries like healthcare and financial services, where data privacy and security are paramount, using real customer data may not be feasible. The data required to test edge cases in business processes is often limited.
This is where AI-generated high fidelity synthetic data can be used for testing, training and innovation. It mimics the statistical properties and patterns of real datasets while preserving privacy and eliminating sensitive information. It can also be used to augment data for training AI models when data is sparse or sensitive. Additionally, executives can use synthetic data for scenario planning to model various business situations and test strategies to mitigate and reduce risks.
Merck, a global pharmaceutical company, uses synthetic data and AWS services to reduce false rejection rates in its drug inspection process.. The company reduced its false reject rate by 50% by developing synthetic defect image data with tools such as generative adversarial networks (deep learning models that pit two neural networks against each other to generate new synthetic data) and variational autoencoders (generative neural networks that compress data into a compact representation and then reconstruct it, thereby learning to generate new data).
Synthetic data generated by AI can unleash innovation and help create delightful customer experiences. Amazon One is a fast and convenient service that allows customers to make payments, present the loyalty card, verify their age and enter the venue using only their palm.
AWS needed a large dataset of palm images to train the system, including variations in lighting, hand poses, and conditions such as the presence of a bandage. The team even trained the system to detect highly detailed replicas of silicone hands using AI-generated synthetic data. Customers have already used Amazon One more than three million times with 99.9999% accuracy.
AI and data are symbiotic
These three examples demonstrate how generative AI can be leveraged to unlock the potential of data, extract value faster, and demonstrate tangible gains through generative AI. Whether it’s automating tedious data integration tasks or providing business users with conversational analytics, generative AI can help teams work smarter, nothing more. And by generating synthetic data for testing and innovation, we can power new ideas and capabilities that were previously out of reach. The key is not only to view your data as the fuel for generative AI, but also to view generative AI as a powerful new tool that you can apply to your data.
