My company Baker Tilly Digital has leveraged Amazon’s QuickSight solution for a variety of internal and external analytics use cases. With continuous advancements in technology, Amazon recently made their Amazon Q product, a Generative AI solution, generally available in Amazon QuickSight.
Amazon Q in QuickSight brings together the generative AI strengths of large language models (LLMs) from Amazon Bedrock with the proven models from QuickSight to create Generative BI experiences, reducing time to insights and accelerating data analysis.
Some of the capabilities include
1) contextual answers with multi-visual Q&A
2) insights with Executive Summaries
3) ability to build visuals and calculations quickly using natural language
4) ability to build simple-to-share documents or presentations to articulate key insights.
Research findings that provide information regarding how to create an analytics solution leveraging Amazon QuickSight and Amazon Q’s Generative AI capabilities, including technologies used, benefits/value, potential solution design and approaches and use cases.
Functioning proof of concept analytics solution leveraging AWS QuickSight and Amazon Q.
Final presentation that highlights key research findings, overview/demo of solution and how my team can leverage the technologies and solutions across the organization.
Develop an understanding of Amazon QuickSight and Amazon Q technologies and solutions, their potential benefits/value and use cases.
Understand how Generative AI technologies and solutions can be leveraged and benefit my comapny internally and provide value for our clients.’
Project approach
1.Conduct research and discovery for how to create an Amazon QuickSight solution leveraging Amazon Q, including technologies used, potential solution design and approaches and potential use cases.
2.Define requirements and approach to create the Amazon QuickSight solution leveraging Amazon Q and design a high-level solution architecture.
3.Implement and test the QuickSight solution including configuring/building the solution with Amazon Q, loading data into the solution, and testing/validating the solution.
4.Demo QuickSight and Amazon Q solution to Baker Tilly employees and articulate the value of the solution, both internally and externally and present research findings.
Objectives
Conduct research, define and implement a functioning proof of concept analytics solution leveraging Amazon QuickSight, Amazon Q’s Generative AI capabilities and data from our proprietary models to better understand how these technologies and capabilities can be leveraged.
Expected outcomes
1.Understand how to create an analytics solution leveraging Amazon QuickSight and Amazon Q’s Generative AI capabilities, including technologies used, benefits/value, potential solution design and approaches and use cases.
2.Develop a functioning proof of concept analytics solution leveraging Amazon QuickSight and Amazon Q.
Data overview
Generative AI overview
Amazon Q Overview
Technologies used
There were the different AWS services that we used to build this solution.
High-level Solution architecture
Here is the architecture flow of our entire solution.
Recommendations
The data needs to be preprocessed prior to working on the data as it could improve your insights
Increased usage of Q will lead to development of best practices techniques for more efficient generation
Next Steps
These are the next steps that we think we should be focusing on next.
Source link
lol
