Mastering QuickSight Q: Setting the Stage for Semantic Insights

Author: Manasa K, Head Data Practice | Co-Author: Priyanka Gopalakrishnan

Without question, companies leveraging data insights thrive with improved operational efficiency and profitability. As such, the increasing need for enterprises to effectively leverage data, coupled with paradigm shifts in technology is reshaping the business intelligence landscape more so in recent times. This evolution is marked by a move toward a semantic understanding of data and intelligence, enabling deeper insights and more informed decision-making.

For instance, the emergence of Generative AI has revolutionized business intelligence tools, offering capabilities far beyond traditional interactive dashboards. These tools now enable processing complex natural language queries, allowing users to “chat” with their data to uncover insights effortlessly. AWS is at the forefront of this transformation with Amazon QuickSight, introducing innovative features like Amazon QuickSight in Q, Data Stories, Data Scenarios, and integration with Amazon Q Business. These advancements empower users to engage with their data more intuitively.

As we move towards a new approach in data analytics, this marks the beginning of a transformation in how businesses interact with their data. While we will be delving into all things new about QuickSight in the upcoming blogs, in this first edition of a three-part blog series, we will explore more about QuickSight in Q and the steps to enhance and tune the output from QuickSight Q.

What is Amazon Q in QuickSight?

Amazon QuickSight is a powerful, cloud-native BI service offered by AWS. It allows businesses to create interactive dashboards and visualize data from multiple sources. The latest version, Amazon Q in QuickSight, integrates generative AI capabilities to significantly enhance the user experience by enabling natural language queries. This integration transforms how users interact with their data, making it easier to access, analyze, and derive insights without needing advanced technical skills. With Amazon Q in Quicksight, businesses can now explore their data through natural language, bringing a new level of accessibility and efficiency to business intelligence.

Getting Started with QuickSight Q

Now that we’ve introduced the transformative potential of Amazon Q in QuickSight, let’s dive deeper into how you can start leveraging this tool to maximize your data analysis capabilities. One of the fundamental concepts in QuickSight Q is topics, which is the backbone of how the system understands and processes natural language queries. It includes metadata, user-friendly field names, synonyms, and filters to make the data more intuitive and relevant for specific business use cases.

Creating and managing these topics effectively is crucial to making the most out of QuickSight Q and having a better user experience. A topic can be mapped to one dataset or multiple datasets with relevant data, so business users can get relevant insights.

• When working with multiple datasets in QuickSight Q, you might typically create separate topics for each dataset and switch between topics every time you need to ask a question specific to a dataset.
• However, this can be time-consuming and inconvenient. Instead, you can simplify this process by creating a single topic that includes all the datasets that are relevant and needed.
• Add multiple datasets by clicking on “ADD DATASETS“ in the DATASETS section.

• With this functionality, we can seamlessly ask queries without switching topics, allowing for more efficient analysis across datasets.

Tailoring QuickSight Q to Fit your Data and Business needs

Data holds greater value when it is not just available, but accurate, accessible, and actionable. Tailoring Amazon Q in QuickSight to suit your business needs plays a key role in unlocking its value by delivering the most relevant insights — like a genie by your side, giving the right data at the right time, just the way you want to see it.

Now let us explore how to finetune QuickSight Q effectively.

The Role of Context in Simplifying Data Retrieval and Analysis

Sifting through data without any context can be inefficient and overwhelming. Context, however, provides clarity by offering additional information about the data, like its nature and the type of information it contains. This organized approach enables QuickSight Q to gain a deeper understanding of your data, allowing it to better interpret and respond to queries with accuracy.

Synonyms

Adding synonyms for columns makes the data more accessible and lets users make queries using alternative words or phrases.

For instance, the column Employee ID could have synonyms like ID or Code, allowing users to ask questions in multiple ways and still get accurate results. This flexibility ensures a smoother and more intuitive user experience.

Semantics

Assigning semantic types to fields is one of the key ways to improve the quality of answers in QuickSight Q. Doing so helps QuickSight Q better understand how to interpret and utilize your data effectively, ensuring more accurate and contextually relevant responses.

Semantic types define critical aspects of a field, including field role, data type, default aggregation and additional context. By regularly updating and refining the semantic types for your fields, you empower QuickSight Q to deliver precise, high-quality answers, creating a smoother and more intuitive user experience.

Description

We can enhance QuickSight Q’s ability to understand and retrieve accurate data by providing a description of each field. Serving as a bridge between the dataset and user queries, these descriptions, without doubt, improve both the accuracy of results and the overall user experience.

Enhancing Q’s Understanding with Business Terms

Business-specific terms can sometimes, if not always, be confusing, or complex to interpret if not properly defined. Defining these terms allows QuickSight Q to better interpret queries and provide results tailored to your business context. It effectively bridges the gap between technical data and business terms, improving the precision of your reports and making the analysis more intuitive.

Named Entity

Group relevant columns together using named entities in QuickSight Q for better query performance and accuracy. This approach simplifies querying by providing a structured way to reference and organize related data fields, ensuring more efficient and precise results.

Without adding a named entity:

Adding named entity:

For example, under a Product Named Entity, you can configure the following columns:

• Category
• Discount
• Price
• Rating

• By leveraging Named Entities, whenever you ask a question related to Category, QuickSight Q will directly pull the configured columns instead of searching randomly through all 100 columns.

Improving the Reliability of Insights Generated by QuickSight Q

Sure, you can simplify data retrieval by adding context and enable Q to better understand your queries using named entities to get the results you want. But how can you ensure optimised query performance, allowing QuickSight Q to refine how it processes similar questions in the future and ensuring the reliability of responses? Well, there are not one, but two ways to do that too.

Feedback

• QuickSight Q allows users to provide feedback so it can learn and improve over time, refining how it processes similar questions in the future. If the results do not meet the expectations, users can send feedback with suggestions on how the system could better match the query intent.
• Positive feedback reinforces the system’s understanding of user preferences and ensures consistent performance across similar queries.
• By actively providing feedback — whether for improvements or validations — you contribute to optimising query responses, making the tool more effective and reliable for all users.

Mark as Verified

As you go about making queries and getting accurate results, also make sure to verify answers to the questions asked. Verified answers are prioritized in search results, helping users quickly find reliable information. Additionally, the Mark as verified option also plays a key role in driving constant improvements by helping users track how often questions are asked, and if users find the answers helpful.

Conclusion

As outlined in this blog, following these best practices optimizes the output from QuickSight Q, and enhances the value of your data, driving better decision-making and, ultimately, better business outcomes. QuickSight Q continues to evolve, becoming smarter and all the more powerful, not only addressing the limitations of conventional BI tools but also paving the way for a deeper understanding of data.

To continue your journey toward a deeper understanding of your data, stay tuned for the upcoming blogs, where we will explore the latest advancements in QuickSight Q, including Data stories and Data Scenarios and how they can further elevate your business intelligence capabilities.