In this article, we explore three compelling use cases where generative AI can be leveraged to fuel your data and drive tangible business outcomes.
Reducing extremely tedious labour (ETL)
One of the most resource-intensive tasks in any data project is the extract, transform, and load (ETL) process, often consuming up to 70% of the effort. Generative AI, such as AWS’s Amazon Q Developer, can automate this process by analysing your source and target data structures and mapping them seamlessly. This not only reduces the time and effort required but also helps maintain consistency across different ETL processes, making ongoing support and maintenance easier. Enterprises often find that they have both structured (e.g., customer profiles and sales orders) and unstructured (e.g., social media or customer feedback) data, and that it is held in a variety of data sources, formats, schemas, and data types. The Amazon Q data integration in AWS Glue can generate ETL jobs for over 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 deriving insights from your data and less time laying out the plumbing.
Generative BI: better insights, faster
Democratising data across an organisation is a key priority, but not everyone has the skills to work rigorously with data. Generative AI can bridge this gap by enabling users to interact with data using conversational queries and natural language. This empowers business users to quickly find the insights they need, reducing time to value and fostering a data-driven culture. A retail executive can ask, “What were our top-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, have been using the generative AI assistant Amazon Q in QuickSight to quickly respond to supply chain visibility requests from senior stakeholders like board members. Data has the power to influence change, but that requires compelling storytelling. Generative AI can make data easy to work with and enjoyable to use by creating visually appealing documents and presentations that bring the data to life.
Synthetic data: get the data you want
As enterprises become more mature with analytics and AI, they often find that they lack the data required for new use cases. Acquiring third-party data can be prohibitively expensive, and in regulated industries, using actual customer data may not be possible. Generative AI can create high-fidelity synthetic data that mimics the statistical properties and patterns of real datasets, while preserving privacy and eliminating sensitive information. This synthetic data can be used for testing, training, and innovation, unlocking new possibilities for your organisation. Merck, a global pharmaceutical company, uses synthetic data and AWS services to reduce false reject rates in their drug inspection process, achieving a 50% reduction in false rejects by developing synthetic defect image data with tools like generative adversarial networks (GANs) and variational autoencoders (VAEs). Amazon One, a fast and convenient service that allows customers to make payments or access venues using their palm, used AI-generated synthetic data to train the system, including variations in lighting, hand poses, and conditions like the presence of a bandage. Customers have already used Amazon One more than three million times with 99.9999% accuracy.
By leveraging these generative AI use cases, you can unlock the true potential of your data, extract value more quickly, and demonstrate tangible wins. The key is to view your data and generative AI as symbiotic, where AI can be a powerful tool to enhance, enrich, and democratise your data assets. Embrace the power of generative AI and fuel your data-driven transformation.