--- schema_version: "1" id: 5b36b0db4aef77613681009228191872b602a0fa421465780043563bb89bb967 title: Enhancing Data Analysis with Vector Search in Tableau collection: admin version: current locale: en-us portal_url: https://docs.salesforce.com/admin/current/en-us/data/264-0-0/c360_a_search_index_vector_search_analytics.md canonical_url: https://help.salesforce.com/s/articleView?id=data.c360_a_search_index_vector_search_analytics.htm&release=264.0.0&type=5 source_site: help.salesforce.com content_type: documentation content_hash: e6d785f530ab00020700d9c37cea6632a8dec76c05e955b886a4ae1261c790f9 description: Find and analyze data based on meanings, not just keywords, by using vector search in a Tableau query. Vector search broadens the scope of data exploration as well as enhances the accuracy and relevance of the insights by identifying semantic similarities rather than relying solely on exact keyword matches. products: - Data 360 release: "264" --- # Enhancing Data Analysis with Vector Search in Tableau Find and analyze data based on meanings, not just keywords, by using vector search in a Tableau query. Vector search broadens the scope of data exploration as well as enhances the accuracy and relevance of the insights by identifying semantic similarities rather than relying solely on exact keyword matches. For example, a business analyst creates a Voice of the Customer analysis dashboard. The analyst sourced the data from a file in an Amazon S3 bucket, created a Data 360 data stream from that content, and mapped the custom fields to a custom DMO they named `dc_voc`. The analyst then created a search index configuration from the `dc_voc` object and chunked and vectorized the Description field from the object. The analyst can now use a search filter in Tableau specifically tailored for Voice of the Customer descriptions. The Tableau dashboard displays both structured and unstructured data analytics. The left side uses a category filter, and the right side uses semantic search to enhance data exploration. ![A VOC dashboard with vector search](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-data-data_cloud-264-0-0-production-enus/f3569962-3c9b-4f83-9d0c-b6a574e785da/data_cloud/images/c360_a_search_index_vector_search_analytics.png) For instance, when you enter the term “real time” into the semantic search on the right side, the records show entries that include related terms, such as real-time activation, near-time data ingestion, and real-time data stream. The matching score ranks results based on the similarity of their descriptions to the search term. This scoring system highlights the advantage of semantic search, because it captures varied expressions of similar concepts, providing richer insights than a simple keyword search. To implement this functionality, the analyst created a custom SQL query in Tableau that uses the `vector_search( )` function. (Note that the query contains non-standard or custom objects that are examples and may not correspond to objects in customer orgs.) ``` SELECT distinct "dc_voc__dlm"."Accounts_Impacted__c" AS "# Accounts Impacted", "dc_voc__dlm"."Opportunities_Impacted__c" AS "# Opportunities Impacted", "dc_voc__dlm"."Created_By_Role_Name__c" AS "Created By: Role: Name", "dc_voc__dlm"."Created_Date__c" AS "Created Date", "dc_voc__dlm"."Description__c" AS "Description", "dc_voc__dlm"."Input_Name__c" AS "Input Name", "dc_voc__dlm"."Product_Area__c" AS "Product Area", "dc_voc__dlm"."Total_ACV_Impacted_converted__c" AS "Total ACV Impacted (converted)", "dc_voc__dlm"."Total_AOV_Impacted__c" AS "Total AOV Impacted", "Chunk__c", "score__c" FROM vector_search(table("dc_voc_str_id_vector__dlm"), , '', 500) vs INNER JOIN "dc_voc_str_id_chunk__dlm" USING ("RecordId__c") INNER JOIN "dc_voc__dlm" USING("Str_id__c") ORDER BY "score__c" DESC LIMIT ``` Notice two specific parameters: - `Query String` for entering search terms. - `Limit VOC Matches` controls the number of VOC records displayed. This integration of vector search within Tableau not only broadens the scope of data exploration but also enhances the accuracy and relevance of the insights derived from customer feedback by identifying semantic similarities rather than relying solely on exact keyword matches. #### See Also - [_Salesforce Help_: Query Search Index](https://help.salesforce.com/s/articleView?id=data.c360_a_search_index_query.htm&release=264.0.0&type=5 "Use the Query API to run vector searches in Data 360. Use these examples as starting points.") - [_Salesforce Help_: Create Vector Search Index Configurations](https://help.salesforce.com/s/articleView?id=data.c360_a_search_index_create_vector_index_config.htm&release=264.0.0&type=5 "When you create a vector search index configuration for a data model object, Data 360 breaks up the referenced data into semantically related chunks and generates searchable vectors. Your RAG and agent workflows use these vectors to find and return semantically similar items when they run queries. To get more granular control over your search index configuration, use the advanced setup, which guides you through each step of the process.") - [_Salesforce Help_: Data 360 for Tableau](https://help.salesforce.com/s/articleView?id=data.c360_a_dc_for_tableau.htm&release=264.0.0&type=5 "After you've combined, unified, and harmonized your data in Data 360, it's now ready for you to analyze. Use Tableau to find useful and actionable insights that can drive business success and create a personalized experience for your customers. Data 360, Tableau, and Salesforce 360 apps work together to consolidate your data and create a single customer record. Unlock hidden insights, and make decisions based on all your customer data.")