Data Con LA 2022 - Collaborative Data Exploration using Conversational AI
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The document discusses the limitations of current data consumption methods, particularly dashboards that provide limited interaction and can be misleading. It introduces conversational analytics as a solution, allowing users to ask natural language questions and receive actionable insights while bridging the gap between users and data. The presentation emphasizes the importance of collaboration in data exploration, enabling users with varying data skills to engage effectively with their data and each other.
Data Con LA 2022 - Collaborative Data Exploration using Conversational AI
- 1. Collaborative Data Exploration using Conversational AI Anand Ranganathan Co-Founder & Chief AI Officer [email protected]
- 2. How do people consume data & analytics today? – through charts & dashboards
- 3. What’s wrong with dashboards Limited or no drill-downs Don’t know how the data for the dashboards is produced Can’t ask a slightly different question Representative of an opinion; easy to cherry-pick stats Can be misleading
- 4. Analytics & BI tools have some ways to go In making organizations truly data-driven “Where can I find my data? I don’t know which database, table, query or tool to use” “The presentation is tomorrow and the BI team is busy. What should I do?” “Why are there so many dashboards?” 26.5% of organizations report being data driven in 2022, down from 37% in 2017.
- 6. Allow any user to ask text or voice questions of their data and receive back a natural language + visual analysis of statistically relevant and actionable insights for that user. What is Conversational Analytics? *Note that in this talk, we focus on structured data stored in relational format (e.g. SQL databases, Excel sheets, etc)
- 7. Qbo: Natural language conversations with data within collaboration platforms Hey QBO, how many policies are expiring in Oct 2022? Hey QBO, why were new acquisitions in Feb lower? Why have sales dropped in the North region this month? AI-Powered, Data Analyst
- 8. Direct Connection to data Sales Management Marketing Natural Language Queries Operations HR BUSINESS USERS Qbo sits between users and disparate, siloed datasets Support 20+ data connectors, and access via a web interface or Microsoft Teams
- 9. Unscrambl Qbo Demo : on Covid Data
- 10. Demo
- 11. A (very simplified) overview of NLU pipeline number of trips in winter 2017 by age and gender Entity Recognition & Construction SELECT anon_1."age group", anon_1.gender, count(*) AS "Count" FROM (SELECT "TRIP_ANALYSIS".end_station_id AS "end station id", "TRIP_ANALYSIS".program_id AS "program id", "TRIP_ANALYSIS".start_station_id AS "start station id", "TRIP_ANALYSIS".bikeid AS bikeid, CASE WHEN (:birth_year_1 - "TRIP_ANALYSIS".birth_year < :param_1) THEN :param_2 ELSE CASE WHEN (:birth_year_2 - "TRIP_ANALYSIS".birth_year < :param_3) THEN :param_4 ELSE …, "TRIP_ANALYSIS".gender AS gender FROM "TRIP_ANALYSIS" WHERE …. Identification of Query type and mapping to known concepts in DB Type: Aggregation Query on Trips table with a group-by and a filter; age -> derived from birth year attribute; gender -> gender attribute; in winter 2017 -> 2017-12-23 and 2018-03-19 (filter) Generate DB-specific SQL query Get results, decide on visualization and narratives, and present back to user
- 12. Key Challenge : Bridging the gap between users and data
- 13. ● Users don’t know what to ask ● Users don’t know how to ask ● Users may pose questions in an ambiguous manner ● Users may use terms not in the dataset Key Challenge : Bridging the gap between users and data
- 14. ● Users don’t know what to ask ● Users don’t know how to ask ● Users may pose questions in an ambiguous manner ● Users may use terms not in the dataset Key Challenge : Bridging the gap between users and data ● Data may be modeled in a variety of ways ● Hidden semantics and assumptions behind different tables and columns ● Data may be incomplete, unclean ● Data may be spread across silos
- 15. Common scenario we encounter in our deployments : Qbo doesn’t understand the user
- 16. Now, let’s assume Lila is part of a group channel
- 17. Collaborative data exploration to the rescue ● Many organizations and teams contain people with different levels of data skills ● In the past, the data-have-nots were dependent on the data-haves ● Conversational AI can help them collaborate. Allow the data-have-nots serve themselves sometimes, and the data-haves jump in when they can’t
- 18. Direct Connection to data Natural Language Queries Collaborate with internal and external users to explore & share data, insights and reports With the right access control restrictions in place Internal Business Users 3rd Party Partners Collaborate with Partners in the context of “Teams” or “Channels”
- 19. Interesting challenges around collaborative data access ● How should access control work? ○ Based on the user asking the question? ○ Based on the user in the group channel with the “least” privilege? ● How do we keep track of context around multiple parallel conversations in a group chat ● How do we keep track of versions and group edits in a collaborative dashboard? ○ Especially when the charts are interactive or can be refreshed