SBA · SBA-32 · record R3188

Data Analysis and Visualization AI

Small Business Administration · OCFO: Office of the Chief Financial Officer

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
Start date: 2026-01-11 (day precision)Source: 01/11/2026 00:00:00
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Information Technology
Operational functions: Chatbot or virtual assistant, Translation and transcription Derived by keyword rules; see methodology

Problem the AI is intended to solve

Data analysts spend significant time on repetitive tasks like data cleaning, semantic modeling, calculation creation, and generating data narratives. Non-technical business users struggle to explore data and create visualizations without extensive training. Translating complex datasets into actionable insights is time-consuming. Creating comprehensive documentation for data assets requires manual effort. Identifying trends, anomalies, and meaningful patterns in large datasets is labor-intensive.

Expected benefits

Accelerates data analysis with natural language queries that instantly generate visualizations and calculations. Reduces time spent on data preparation through AI-assisted cleaning and mapping automation. Simplifies complex analysis by converting natural language prompts into accurate calculations and visualizations. Democratizes data access for non-technical users through conversational data exploration. Automates data documentation generation for data sources, workbooks, and tables. Provides proactive context-aware insights by identifying patterns and anomalies. Generates natural language data stories that explain visualizations. Enhances decision-making speed and quality with AI-powered insights. Built on Einstein Trust Layer ensuring security, governance, and data privacy.

System outputs

Generative AI-powered analytics assistant integrated into Tableau that helps analysts explore data, create visualizations, and uncover insights through conversational natural language. Uses NLP and generative AI to transform natural language questions into calculations, visualizations, and data stories. Accelerates data preparation with AI-assisted cleaning and mapping. Automates semantic modeling with AI recommendations. Generates comprehensive data asset descriptions. Provides context-aware assistance, proactive anomaly detection, and guided calculation creation. Input: natural language queries, data sources, dashboards. Output: automated visualizations, calculated fields, data narratives, insight summaries, trend analysis, and interactive dashboards.

Technology

AI classification: Generative AISource: Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
System name(s): Tableau Cloud with Tableau Agent
Custom-developed code: No
Public source code: Not applicable - No custom code

Sourcing

How it was built: Purchased from vendorSource: a) Purchased from a vendor
Vendor (as reported): Salesforce, Inc. (Tableau)
Vendors (standardized): Salesforce

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not publicly available
Authorization to Operate: Yes
Demographic features: k) None of the above
Training and evaluation data: Commercial vendor-managed training data with customer data source integration
Federal Data Catalog: Not applicable - Proprietary vendor training data

Governance

8 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingReported in placeSource: a) Yes

AI impact assessmentReported in placeSource: a) Yes

Independent reviewReported in placeSource: a) Yes – by another appropriate agency office or reviewer not directly involved in the AI’s development

Ongoing monitoringReported in placeSource: a) Yes, sufficient monitoring protocols have been established

Operator trainingReported in placeSource: a) Yes, sufficient and periodic training has been established

Fail-safeNot applicableSource: b) Not applicable

Appeal processNot applicableSource: b) Not applicable

User and public consultationOther or ambiguous answerSource: Conducted pilot testing with data analysts and business users across multiple program offices, collected feedback on natural language query accuracy and visualization quality, monitored calculation correctness and insight relevance, established feedback mechanisms through user surveys and usage analytics, incorporated analyst input on AI suggestion improvements, validated AI-generated data stories against manual analysis, and continuously refined natural language understanding based on user interaction patterns and domain-specific terminology.Flag: free-text answer

Potential impacts and how they were identified

Potential impacts include quality and accuracy of data analysis, calculation correctness, visualization appropriateness, and automated insight generation affecting decision-making. Impacts identified through vendor documentation, analyst usability testing, accuracy validation of AI-generated calculations and insights, and pilot deployment with data teams. Mitigation includes human analyst oversight of all AI-generated outputs, validation of calculations before use, Einstein Trust Layer security and governance controls, transparency in how insights are derived, user training on appropriate AI assistance usage, and continuous monitoring of output accuracy.