Project Roadmap: A 6-Month Journey
This interactive roadmap outlines the 6-month plan to build a "Central Intelligence Hub" for automating business intelligence. The goal is to streamline data from sales and operations into an AI-powered system that delivers actionable insights. Use the timeline below to navigate through the project phases.
Total Task Distribution
This chart provides a high-level overview of the workload distribution between the Data Engineer and Full-Stack Developer across all three phases of the project.
Phase 1: Foundation & Data Integration
Duration: Months 1-2
The initial phase is dedicated to building the project's backbone. The primary objective is to establish the core cloud infrastructure and create robust data pipelines, culminating in a single source of truth. This foundational work is critical for all subsequent development and analysis.
Data Engineer Tasks
Tech Stack Selection
Set up cloud environment (AWS, GCP, Azure) and select data warehouse/database.
Data Source Identification
Map all data sources: POS, METRC, Inventory, Finance, and Marketing.
Build Data Pipelines (ETL)
Develop scripts to extract, transform, and load data into the data warehouse.
Initial Data Modeling
Structure raw data into clean, usable tables for analysis.
Full-Stack Developer Tasks
Application Scaffolding
Set up the basic web app with user authentication and roles.
API Development
Create secure internal APIs for front-end to back-end communication.
Basic UI/UX Wireframing
Design an intuitive dashboard interface with a clean layout.
First Dashboard
Build a "Single Pane of Glass" dashboard to validate data pipelines.
AI Integration Status:
The focus is entirely on data plumbing and infrastructure.
Phase 2: Core Features & BI Dashboards
Duration: Months 3-4
With the data foundation in place, this phase focuses on transforming that data into actionable insights. We will build interactive dashboards and reporting modules, turning the application into a valuable BI tool for daily use by sales and operations teams.
Data Engineer Tasks
Refine Data Models
Optimize the data warehouse for fast querying and create summary tables.
Develop Business Logic
Write complex SQL/Python scripts to calculate key performance indicators (KPIs).
Explore Predictive Model Data
Begin cleaning and preparing historical data for future sales forecasting models.
Full-Stack Developer Tasks
Sales Strategy Dashboard
Build dashboards for visualizing sales plans, territory mapping, and volume targets.
Reporting Module
Create a feature for generating standardized reports with filtering capabilities.
Cross-Functional Views
Develop dashboards showing inventory levels alongside sales data.
User Feedback Loop
Implement a simple method for users to provide feedback on the tool.
AI Integration Status:
The system analyzes historical data to answer "what happened?" and "why?".
Phase 3: AI Implementation & Automation
Duration: Months 5-6
The final phase transitions the hub from a reactive BI tool to a proactive intelligence system. We will build, train, and deploy predictive models and introduce AI Agents to automate reporting, monitor compliance, and provide strategic suggestions.
Data Engineer Tasks
Build & Train ML Models
Create sales and demand forecasting models using historical data.
Deploy Models
Deploy trained models and make them accessible via an API.
Develop AI Agent Logic
Define the triggers and actions for automated agents (e.g., restock alerts).
Full-Stack Developer Tasks
Integrate Forecasting
Display AI-driven forecasts in the dashboards and compare with actuals.
Build AI Agent Hub
Create a UI for managers to view alerts and suggestions from AI Agents.
Implement AI Agents
Roll out Reporting, Compliance, Inventory, and a beta Strategy Agent.
AI Integration Status:
The system provides forecasts and automated alerts via AI Agents.