256: The Outlook of Q&A & Understanding Linguistic Schema In Power BI
Oct 10, 2023
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The podcast discusses major updates to Q&A tooling in Power BI, including the ability for organizations to integrate it into their workflow. They also talk about the Power BI contributor program, new updates in Power BI visuals, and the challenges and potential of language models in business outputs.
The September update of Power BI introduced an improved Q&A tooling feature with linguistic schema, allowing users to form more natural language questions and get relevant results.
With linguistic schema and improved Q&A tooling, organizations can build a comprehensive data glossary, fostering better communication and understanding of data across different teams and data models.
AI-powered recommendations for synonyms and associations in linguistic schema offer the potential for more accurate and intuitive querying, requiring organizations to invest in central data sets and effective collaboration.
Deep dives
Improved Q&A Tooling with Linguistic Schema
The September update of Power BI introduced an improved Q&A tooling feature with the addition of linguistic schema. This enhancement allows users to associate fields together, enabling more accurate and intuitive queries. With the ability to bind adjectives and verbs, users can form more natural language questions and get relevant results. The new tab in the Q&A tooling provides a convenient interface for managing these associations. However, while this feature shows promise, it remains to be seen how organizations will adapt and embrace the use of linguistic schema to enhance their data models.
Potential for Organization-Wide Data Glossary
With the introduction of linguistic schema and improved Q&A tooling, there is a potential for organizations to build a comprehensive data glossary. By sinking synonyms and associations across various data sets, users can establish consistent language and definitions within their organization. This could enable better communication and understanding of data, bridging the gap between different teams and data models. As users contribute appropriate synonyms and definitions, the glossary can serve as a valuable resource for promoting data literacy and ensuring coherent reporting practices.
Broader Applications and Microsoft's AI Recommendations
The inclusion of AI-powered recommendations for synonyms and associations opens up broader applications for linguistic schema. By leveraging the collective intelligence of Microsoft's data models and user-contributed synonyms, organizations can potentially benefit from more accurate and intuitive querying across various data sets. The ability to refine questions and receive feedback from users through the Q&A functionality provides a valuable feedback loop for improving reporting and data model design. To fully harness the potential of linguistic schema, organizations may need to invest time and effort into central data sets and collaborate effectively to achieve consistent language and definitions.
The Challenges of Building a Linguistic Schema for Large Language Models
Building a linguistic schema for large language models can be a challenging and time-consuming task. Without a proper schema, the interaction between the model and the end user may not be optimal. To streamline this process, Microsoft has introduced new tooling that allows users to add synonyms to their data models and share them with their organization. This feature aims to create a global glossary that improves the understanding of business terms across the organization. While this tool shows potential, the accuracy and usefulness of the generated visuals and responses still need further improvement.
The Potential of Large Language Models in Business Intelligence
Large language models, such as ChatGPT, have the potential to enhance business intelligence by allowing users to ask questions and get meaningful insights from data models. With the ability to interpret natural language prompts, these models can retrieve relevant information and present it in a comprehensible format. However, there are challenges in bridging the gap between the user's intent and the model's understanding. Providing clear and specific questions is crucial for accurate results. Despite the potential benefits, organizations need to carefully evaluate the investment required and consider factors such as data quality, user readiness, and the credibility of the generated outputs before implementing large language models in their business intelligence workflows.
Mike, Seth, & Tommy talk about the major updates to Q&A tooling in Power BI (September update) and how close we are to the ability to organizations to latch on to integrating into their workflow. Where are we still far away?
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