Empowering Analytics with Amazon Quicksight Q – NLP for BI

Posted on by By admin, in Amazon QuickSight, Business Intelligence, Data Processing | 0

For BI tools, generally require users to have technical skills and knowledge of data structures and query languages to transform data into insights. This can limit the accessibility and usability of BI for many users.
NLP can be applied to BI to create natural language query tools;this will allow users to ask questions in natural language about data and receive answers with relevant visualizations.

In this blog, we will get to know about Amazon Quicksight Q and how Amazon it uses NLP and machine learning (ML) to provide accurate and relevant answers to natural language questions. We will also compare Amazon Quicksight Q with other NLQ tools in the market, such as Microsoft Power BI Q&A, Tableau Ask Data, and ThoughtSpot.

Amazon Quicksight Q:

Amazon Quicksight Q is a new innovative feature in Amazon Quicksight Enterprise Edition from which users can type or speaktheir questions about data in natural language i.e., in plain English and use the ML-powered engine to receive answers with meaning visualizations and analytics in seconds. Q can be integrated with Quicksight dashboards from which it can answer questions about datasource or visualization in their dashboard. It can also be embed into different applications using APIs or SDKs.

Amazon Quicksight Q uses NLP and ML to automatically understand the meaning and relationships among business data. It also provides suggestions for phrases and business terms and performs spell checking so that users do not have to concern about spelling mistakes or remember the exact terms in their data. Users directly can ask questions like “what are the top selling products?” or “Which products have highest profit?”

The main advanced analytical questionslike “why” and “forecast” are supported in Amazon Quicksight Q which differentiate AWS Quicksight Q with other NLQ tools. For Example, users can ask question like “why did sales got increased in may?” or “Forecast sales for next year”.

How does Amazon Quicksight Q Use NLP?

1. Tokenization: Breaking the user query into individual tokens.
Quicksight Q tokenizes the query into small tokens to understand the specific words or phrases.
Ex. ‘what, ‘are, ‘the’, ‘top, ‘selling’, ‘products’
2. Parsing: Analyzing the grammatical structure of the query i.e., toidentify the subject, verb, object, modifiers, and clauses in a question.
Q Parses query and identify the intent and entities in a question.
For the above Example it will find the top products based on Net Sales, and the entityhere is product
3. Semantic Analysis: Understanding the meaning and intent of the query.
It performs semantic analysis to grasp the contextual meaning and user intent behind the query.
For above example, it can map the term “products” to a column name or a table name in a data source.
4. Query Mapping: Translating the natural language query to SQL-like queries.
It maps the NL query to structured SQL-like query with that dataset.
For above Example itselects the product name and net sales from a table and groups them by product

5. Data Retrieval: Interacting with the data sources to retrieve the relevant data.
Q interacts with the connected data sources, executing the SQL-like queries to retrieve the necessary data.
6. And it Generates the most suitable visualizations based on the retrieved data to the users.
7. Also finally, it learns from user interactions and feedback, continuously improving its NLP models and query interpretation accuracy.

Amazon Quicksight Q comparison with other NLQ tools:

There are several NLQ tools such as Microsoft Power BI Q&A, Tableau Ask Data, ThoughtSpot etc., in the market that offers similar functionality as Amazon Quicksight Q. Some of the factors which differentiate the Q are:

1. Machine learning: Q uses ML to provide advanced analytical questions such as “why” and “forecast”.
2. Cost
3. Scalability
4. Data sources: Q can easily connect to various AWS data sources like Amazon S3, Amazon Redshift, etc., as well as third-party data sources via JDBC/ODBC connectors.

By evaluating the strengths and weaknesses, you can make a decision about which NLQ tool best suits your analytical needs and goals.


In this blog, we get to know about Amazon Quicksight Q, also explained how Q uses NLP and ML to provide accurate and relevant answers to natural language questions. We also comparedQ with other NLQ tools in the market and highlighted some of its advantages.

Thank You
Soni Sammani
Helical IT Solutions


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