AI Tools for Data Analysis and Intelligence
Most business data goes unanalysed — not because teams don't care, but because getting from raw data to a usable insight requires skills most people don't have. AI closes that gap, turning questions into answers without needing a data scientist.
Choose a specific task
Best AI tools to analyze data
The industry standard for serious data visualisation. Tableau AI adds natural language querying and automated insight generation to an already powerful platform.
The right choice for Microsoft-heavy organisations. Tight Excel and Azure integration, Copilot features, and enterprise-grade security. Significantly cheaper than Tableau at scale.
The friendliest BI tool for non-technical teams. Self-hosted or cloud, SQL optional, and AI-assisted querying that actually works. The best starting point for teams who just want answers.
Prompts to get started
Paste a CSV or table and get an executive summary with key findings and next steps.
I have a data export I need to make sense of. Here it is: [PASTE YOUR DATA — or describe the columns and paste a sample] Context: This is data from [describe the source — e.g. our CRM, Google Analytics, monthly P&L]. I'm trying to understand: [what question are you trying to answer?] Please: 1. Summarise the key findings in plain English (3–5 bullet points) 2. Flag any anomalies, outliers, or unexpected patterns 3. Give me 2–3 actionable recommendations based on what you see 4. Tell me what additional data would help me get a clearer picture
Describe your data structure and get a working query back.
Write a SQL query to answer this question. My tables and key columns: [DESCRIBE — e.g. 'orders: order_id, customer_id, created_at, total, status'] Question: [plain English] Please: 1. Write the query 2. Explain each major section 3. Note assumptions about the data structure 4. Suggest 2-3 related queries to run next
Define the right metrics before building dashboards or setting targets.
Design a metrics framework for [FUNCTION — sales / marketing / support / product]. Business: [describe] Stage: [early / growth / scaling / mature] Currently tracked: [list existing metrics] Decisions this should inform: [what business decisions need data?] Please design: 1. 3-5 primary KPIs (define success) 2. 5-10 supporting metrics (the levers) 3. For each: definition, how to calculate, what 'good' looks like, update frequency 4. Metrics to explicitly NOT track 5. The one metric to check every single day
Systematically explore unexpected results in your data.
Help me understand an unexpected result in our data. Expected: [describe] Actual: [describe the anomaly — include numbers] When it started: [timeframe] Data source: [system or report] Recent changes: [product, team, methodology, external factors] Please: 1. Most likely explanations, ordered by probability 2. What additional data would confirm or rule out each 3. Immediate actions while investigating 4. Whether this could be a data quality issue vs real business signal
Paste a CSV or table and get an executive summary with key findings and next steps.
I have a data export I need to make sense of. Here it is: [PASTE YOUR DATA — or describe the columns and paste a sample] Context: This is data from [describe the source — e.g. our CRM, Google Analytics, monthly P&L]. I'm trying to understand: [what question are you trying to answer?] Please: 1. Summarise the key findings in plain English (3–5 bullet points) 2. Flag any anomalies, outliers, or unexpected patterns 3. Give me 2–3 actionable recommendations based on what you see 4. Tell me what additional data would help me get a clearer picture
Describe your data structure and get a working query back.
Write a SQL query to answer this question. My tables and key columns: [DESCRIBE — e.g. 'orders: order_id, customer_id, created_at, total, status'] Question: [plain English] Please: 1. Write the query 2. Explain each major section 3. Note assumptions about the data structure 4. Suggest 2-3 related queries to run next
Define the right metrics before building dashboards or setting targets.
Design a metrics framework for [FUNCTION — sales / marketing / support / product]. Business: [describe] Stage: [early / growth / scaling / mature] Currently tracked: [list existing metrics] Decisions this should inform: [what business decisions need data?] Please design: 1. 3-5 primary KPIs (define success) 2. 5-10 supporting metrics (the levers) 3. For each: definition, how to calculate, what 'good' looks like, update frequency 4. Metrics to explicitly NOT track 5. The one metric to check every single day
Systematically explore unexpected results in your data.
Help me understand an unexpected result in our data. Expected: [describe] Actual: [describe the anomaly — include numbers] When it started: [timeframe] Data source: [system or report] Recent changes: [product, team, methodology, external factors] Please: 1. Most likely explanations, ordered by probability 2. What additional data would confirm or rule out each 3. Immediate actions while investigating 4. Whether this could be a data quality issue vs real business signal