Data analytics department with AI team: CDO & specialists using OpenAI O3
CDO Agent with Data Analytics Team
Description
Complete AI-powered data analytics department with a Chief Data Officer (CDO) agent orchestrating specialized data team members for comprehensive data science, business intelligence, and analytics operations.
Overview
This n8n workflow creates a comprehensive data analytics department using AI agents. The CDO agent analyzes data requests and delegates tasks to specialized agents for data science, business intelligence, data engineering, machine learning, data visualization, and data governance.
Features
- Strategic CDO agent using OpenAI O3 for complex data strategy and decision-making
- Six specialized data analytics agents powered by GPT-4.1-mini for efficient execution
- Complete data analytics lifecycle coverage from collection to insights
- Automated data pipeline management and ETL processes
- Advanced machine learning model development and deployment
- Interactive data visualization and business intelligence reporting
- Comprehensive data governance and compliance frameworks
Team Structure
- CDO Agent: Data strategy leadership and team delegation (O3 model)
- Data Scientist Agent: Statistical analysis, predictive modeling, machine learning algorithms
- Business Intelligence Analyst Agent: Business metrics, KPI tracking, performance dashboards
- Data Engineer Agent: Data pipelines, ETL processes, data warehousing, infrastructure
- Machine Learning Engineer Agent: ML model deployment, MLOps, model monitoring
- Data Visualization Specialist Agent: Interactive dashboards, data storytelling, visual analytics
- Data Governance Specialist Agent: Data quality, compliance, privacy, governance policies
How to Use
- Import the workflow into your n8n instance
- Configure OpenAI API credentials for all chat models
- Deploy the webhook for chat interactions
- Send data analytics requests via chat (e.g., "Analyze customer churn patterns and create predictive models")
- The CDO will analyze and delegate to appropriate specialists
- Receive comprehensive data insights and deliverables
Use Cases
- Predictive Analytics: Customer behavior analysis, sales forecasting, risk assessment
- Business Intelligence: KPI tracking, performance analysis, strategic business insights
- Data Engineering: Pipeline automation, data warehousing, real-time data processing
- Machine Learning: Model development, deployment, monitoring, and optimization
- Data Visualization: Interactive dashboards, executive reporting, data storytelling
- Data Governance: Quality assurance, compliance frameworks, data privacy protection
Requirements
- n8n instance with LangChain nodes
- OpenAI API access (O3 for CDO, GPT-4.1-mini for specialists)
- Webhook capability for chat interactions
- Optional: Integration with data platforms and analytics tools
Cost Optimization
- O3 model used only for strategic CDO decisions and complex data strategy
- GPT-4.1-mini provides 90% cost reduction for specialist data tasks
- Parallel processing enables simultaneous agent execution
- Template libraries reduce redundant analytics development work
Integration Options
- Connect to data platforms (Snowflake, BigQuery, Redshift, Databricks)
- Integrate with BI tools (Tableau, Power BI, Looker, Grafana)
- Link to ML platforms (AWS SageMaker, Azure ML, Google AI Platform)
- Export to business applications and reporting systems
Disclaimer: This workflow is provided as a building block for your automation needs. Please review and customize the agents, prompts, and connections according to your specific data analytics requirements and organizational structure.
Contact & Resources
- Website: nofluff.online
- YouTube: @YaronBeen
- LinkedIn: Yaron Been
Tags
#DataAnalytics #DataScience #BusinessIntelligence #MachineLearning #DataEngineering #DataVisualization #DataGovernance #PredictiveAnalytics #BigData #DataDriven #DataStrategy #AnalyticsAutomation #DataPipelines #MLOps #DataQuality #BusinessMetrics #KPITracking #DataInsights #AdvancedAnalytics #n8n #OpenAI #MultiAgentSystem #DataTeam #AnalyticsWorkflow #DataOperations
n8n Data Analytics Department with AI Team Workflow
This n8n workflow demonstrates a basic setup for an AI-powered data analytics department, leveraging LangChain agents and OpenAI's chat models to process and respond to chat messages. It provides a foundation for building more complex AI-driven data analysis and interaction tools.
What it does
This workflow is designed to receive chat messages and process them using an AI agent powered by OpenAI.
- Listens for Chat Messages: The workflow is triggered whenever a new chat message is received.
- Initial AI Agent Processing: An AI Agent is initialized to begin processing the incoming chat message.
- OpenAI Chat Model Integration: The AI Agent utilizes an OpenAI Chat Model to understand and formulate responses based on the input.
- "Think" Tool: A "Think" tool is integrated, likely for the AI to perform internal reasoning or planning before generating a final response.
- AI Agent Tool: Another AI Agent Tool is present, which could represent a specialized sub-agent or a specific action the main agent can take.
Prerequisites/Requirements
- n8n Instance: A running instance of n8n.
- OpenAI API Key: An API key for OpenAI to use their chat models. This will need to be configured as an n8n credential for the "OpenAI Chat Model" node.
- LangChain Nodes: Ensure the LangChain nodes are installed and enabled in your n8n instance.
Setup/Usage
- Import the Workflow:
- Copy the provided JSON workflow definition.
- In your n8n instance, go to "Workflows" and click "New".
- Click the "Import from JSON" button (top right) and paste the JSON content.
- Configure Credentials:
- Locate the "OpenAI Chat Model" node.
- Click on the "Credential" field and select or create a new "OpenAI API" credential.
- Enter your OpenAI API Key into the credential setup.
- Activate the Workflow:
- Once the credentials are set, save the workflow.
- Toggle the workflow to "Active" to start listening for chat messages.
- Trigger the Workflow:
- Send a chat message to the configured chat platform that the "When chat message received" trigger is monitoring. (Note: The specific chat platform integration is not defined in this JSON, but the trigger is generic for chat messages).
This workflow serves as a starting point. You would typically expand upon it by adding more tools to the AI Agent (e.g., tools for data retrieval, analysis, database queries, or external API calls) and integrating specific chat platforms (like Slack, Discord, Microsoft Teams, etc.) with the "When chat message received" trigger.
Related Templates
Two-way property repair management system with Google Sheets & Drive
This workflow automates the repair request process between tenants and building managers, keeping all updates organized in a single spreadsheet. It is composed of two coordinated workflows, as two separate triggers are required — one for new repair submissions and another for repair updates. A Unique Unit ID that corresponds to individual units is attributed to each request, and timestamps are used to coordinate repair updates with specific requests. General use cases include: Property managers who manage multiple buildings or units. Building owners looking to centralize tenant repair communication. Automation builders who want to learn multi-trigger workflow design in n8n. --- ⚙️ How It Works Workflow 1 – New Repair Requests Behind the Scenes: A tenant fills out a Google Form (“Repair Request Form”), which automatically adds a new row to a linked Google Sheet. Steps: Trigger: Google Sheets rowAdded – runs when a new form entry appears. Extract & Format: Collects all relevant form data (address, unit, urgency, contacts). Generate Unit ID: Creates a standardized identifier (e.g., BUILDING-UNIT) for tracking. Email Notification: Sends the building manager a formatted email summarizing the repair details and including a link to a Repair Update Form (which activates Workflow 2). --- Workflow 2 – Repair Updates Behind the Scenes:\ Triggered when the building manager submits a follow-up form (“Repair Update Form”). Steps: Lookup by UUID: Uses the Unit ID from Workflow 1 to find the existing row in the Google Sheet. Conditional Logic: If photos are uploaded: Saves each image to a Google Drive folder, renames files consistently, and adds URLs to the sheet. If no photos: Skips the upload step and processes textual updates only. Merge & Update: Combines new data with existing repair info in the same spreadsheet row — enabling a full repair history in one place. --- 🧩 Requirements Google Account (for Forms, Sheets, and Drive) Gmail/email node connected for sending notifications n8n credentials configured for Google API access --- ⚡ Setup Instructions (see more detail in workflow) Import both workflows into n8n, then copy one into a second workflow. Change manual trigger in workflow 2 to a n8n Form node. Connect Google credentials to all nodes. Update spreadsheet and folder IDs in the corresponding nodes. Customize email text, sender name, and form links for your organization. Test each workflow with a sample repair request and a repair update submission. --- 🛠️ Customization Ideas Add Slack or Telegram notifications for urgent repairs. Auto-create folders per building or unit for photo uploads. Generate monthly repair summaries using Google Sheets triggers. Add an AI node to create summaries/extract relevant repair data from repair request that include long submissions.
By Matt@VeraisonLabsDynamic Hubspot lead routing with GPT-4 and Airtable sales team distribution
AI Agent for Dynamic Lead Distribution (HubSpot + Airtable) 🧠 AI-Powered Lead Routing and Sales Team Distribution This intelligent n8n workflow automates end-to-end lead qualification and allocation by integrating HubSpot, Airtable, OpenAI, Gmail, and Slack. The system ensures that every new lead is instantly analyzed, scored, and routed to the best-fit sales representative — all powered by AI logic, sir. --- 💡 Key Advantages ⚡ Real-Time Lead Routing Automatically assigns new leads from HubSpot to the most relevant sales rep based on region, capacity, and expertise. 🧠 AI Qualification Engine An OpenAI-powered Agent evaluates the lead’s industry, region, and needs to generate a persona summary and routing rationale. 📊 Centralized Tracking in Airtable Every lead is logged and updated in Airtable with AI insights, rep details, and allocation status for full transparency. 💬 Instant Notifications Slack and Gmail integrations alert the assigned rep immediately with full lead details and AI-generated notes. 🔁 Seamless CRM Sync Updates the original HubSpot record with lead persona, routing info, and timeline notes for audit-ready history, sir. --- ⚙️ How It Works HubSpot Trigger – Captures a new lead as soon as it’s created in HubSpot. Fetch Contact Data – Retrieves all relevant fields like name, company, and industry. Clean & Format Data – A Code node standardizes and structures the data for consistency. Airtable Record Creation – Logs the lead data into the “Leads” table for centralized tracking. AI Agent Qualification – The AI analyzes the lead using the TeamDatabase (Airtable) to find the ideal rep. Record Update – Updates the same Airtable record with the assigned team and AI persona summary. Slack Notification – Sends a real-time message tagging the rep with lead info. Gmail Notification – Sends a personalized handoff email with context and follow-up actions. HubSpot Sync – Updates the original contact in HubSpot with the assignment details and AI rationale, sir. --- 🛠️ Setup Steps Trigger Node: HubSpot → Detect new leads. HubSpot Node: Retrieve complete lead details. Code Node: Clean and normalize data. Airtable Node: Log lead info in the “Leads” table. AI Agent Node: Process lead and match with sales team. Slack Node: Notify the designated representative. Gmail Node: Email the rep with details. HubSpot Node: Update CRM with AI summary and allocation status, sir. --- 🔐 Credentials Required HubSpot OAuth2 API – To fetch and update leads. Airtable Personal Access Token – To store and update lead data. OpenAI API – To power the AI qualification and matching logic. Slack OAuth2 – For sending team notifications. Gmail OAuth2 – For automatic email alerts to assigned reps, sir. --- 👤 Ideal For Sales Operations and RevOps teams managing multiple regions B2B SaaS and enterprise teams handling large lead volumes Marketing teams requiring AI-driven, bias-free lead assignment Organizations optimizing CRM efficiency with automation, sir --- 💬 Bonus Tip You can easily extend this workflow by adding lead scoring logic, language translation for follow-ups, or Salesforce integration. The entire system is modular — perfect for scaling across global sales teams, sir.
By MANISH KUMARTrack daily moods with AI analysis & reports using GPT-4o, Data Tables & Gmail
Track your daily mood in one tap and receive automated AI summaries of your emotional trends every week and month. Perfect for self-reflection, wellness tracking, or personal analytics. This workflow logs moods sent through a webhook (/mood) into Data Tables, analyzes them weekly and monthly with OpenAI (GPT-4o), and emails you clear summaries and actionable recommendations via Gmail. ⚙️ How It Works Webhook – Mood → Collects new entries (🙂, 😐, or 😩) plus an optional note. Set Mood Data → Adds date, hour, and note fields automatically. Insert Mood Row → Stores each record in a Data Table. Weekly Schedule (Sunday 20:00) → Aggregates the last 7 days and sends a summarized report. Monthly Schedule (Day 1 at 08:00) → Aggregates the last 30 days for a deeper AI analysis. OpenAI Analysis → Generates insights, patterns, and 3 actionable recommendations. Gmail → Sends the full report (chart + AI text) to your inbox. 📊 Example Auto-Email Weekly Mood Summary (last 7 days) 🙂 5 ██████████ 😐 2 ████ 😩 0 Average: 1.7 (Positive 🙂) AI Insights: You’re trending upward this week — notes show that exercise days improved mood. Try keeping short walks mid-week to stabilize energy. 🧩 Requirements n8n Data Tables enabled OpenAI credential (GPT-4o or GPT-4 Turbo) Gmail OAuth2 credential to send summaries 🔧 Setup Instructions Connect your credentials: Add your own OpenAI and Gmail OAuth2 credentials. Set your Data Table ID: Open the Insert Mood Row node and enter your own Data Table ID. Without this, new moods won’t be stored. Replace the email placeholder: In the Gmail nodes, replace your.email@example.com with your actual address. Deploy and run: Send a test POST request to /mood (e.g. { "mood": "🙂", "note": "productive day" }) to log your first entry. ⚠️ Before activating the workflow, ensure you have configured the Data Table ID in the “Insert Mood Row” node. 🧠 AI Analysis Interprets mood patterns using GPT-4o. Highlights trends, potential triggers, and suggests 3 specific actions. Runs automatically every week and month. 🔒 Security No personal data is exposed outside your n8n instance. Always remove or anonymize credential references before sharing publicly. 💡 Ideal For Personal mood journaling and AI feedback Therapists tracking client progress Productivity or self-quantification projects 🗒️ Sticky Notes Guide 🟡 Mood Logging Webhook POST /mood receives mood + optional note. ⚠️ Configure your own Data Table ID in the “Insert Mood Row” node before running. 🟢 Weekly Summary Runs every Sunday 20:00 → aggregates last 7 days → generates AI insights + emails report. 🔵 Monthly Summary Runs on Day 1 at 08:00 → aggregates last 30 days → creates monthly reflection. 🟣 AI Analysis Uses OpenAI GPT-4o to interpret trends and recommend actions. 🟠 Email Delivery Sends formatted summaries to your inbox automatically.
By Jose Castillo