Gen AI for SMB data analysis: Unlocking insights from limited data (SMB305)
Simplifying the Journey to Generative AI Success
Key Takeaways
Generative AI is a powerful tool, but not every organization has the data or expertise to fully leverage it.
By changing the perspective and focusing on simplicity, smaller organizations can still benefit from generative AI capabilities.
A simplified data architecture and the use of modern services can help automate mundane tasks and get to a working pilot or MVP quickly.
Small data sets can be an advantage, allowing for faster iteration and experimentation.
Getting the data foundation right is the ultimate goal to start the journey in a proper way.
Rethinking Generative AI for Smaller Businesses
Larger organizations are grappling with questions on how to scale and integrate generative AI into their business.
Smaller organizations often lack the data and expertise required to fully leverage foundational models.
The goal is to change the perspective and find ways to maximize the return on investment for generative AI.
Simplified Data Architecture
Data Ingestion: Use tools like database migration service, Kinesis Streams, or third-party tools like Airbyte to move data from various sources to a staging area in Amazon S3.
Data Preparation: Use AWS Glue Data Brew to get insights into the data, perform transformations, and prepare the data for business use.
Apply deterministic encryption to sensitive data
Use imputation techniques, including leveraging generative AI, to fill in missing values
Standardize data formats
Data Consumption:
Publish the processed data to a data catalog using AWS Data Zone
Automatically generate documentation to improve discoverability and usability
Implement data governance and access controls
Visualization and Insights:
Use Amazon QuickSight to create visualizations and dashboards
Leverage generative AI to generate reports, forecasts, and actionable insights
Benefits of the Simplified Approach
Small data sets as a Competitive Edge: Smaller organizations can iterate quicker and experiment faster without complex architectures.
Automation of Mundane Tasks: Modern services can help automate data ingestion, preparation, and insights generation, allowing for faster time to value.
Centralized Data Foundation: Getting the data foundation right is crucial for starting the journey in a proper way.
Conclusion
Small data sets can be an advantage, allowing for faster iteration and experimentation.
Leveraging modern services and generative AI capabilities can help automate mundane tasks and get to a working pilot or MVP quickly.
Establishing a solid data foundation is the key to successful adoption of generative AI in smaller organizations.
These cookies are used to collect information about how you interact with this website and allow us to remember you. We use this information to improve and customize your browsing experience, as well as for analytics.
If you decline, your information won’t be tracked when you visit this website. A single cookie will be used in your browser to remember your preference.