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How to Build a Data-Driven CRM for the Oil and Gas Industry

Develop a powerful web-based CRM specifically designed for the oil and gas sector. This system leverages complex government datasets to prioritize users, featuring robust data cleaning capabilities to handle various formats. Streamline operations and enhance decision-making in the oil and gas industry with this innovative CRM solution.

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Resumo Simples

A web-based CRM tailored for the oil and gas industry, featuring advanced data cleaning capabilities to prioritize users based on diverse government datasets.

Documento de Requisitos do Produto (PRD)

Goals:

  1. Create a web-based CRM tailored for oil and gas companies
  2. Implement data cleaning capabilities for various government datasets
  3. Develop a user prioritization system based on processed data
  4. Provide an intuitive interface for managing customer relationships

Target Audience:

  • Oil and gas companies of various sizes
  • Sales and marketing teams in the oil and gas sector
  • Data analysts and decision-makers in the industry

Key Features:

  1. Data Import and Cleaning:

    • Support for multiple data formats (CSV, XML, JSON, etc.)
    • Automated data cleaning and normalization
    • Error detection and correction mechanisms

  2. User Prioritization:

    • Algorithm for ranking users based on cleaned government data
    • Customizable prioritization criteria
    • Visual representation of user priorities

  3. Customer Management:

    • Contact information storage and management
    • Interaction tracking and history
    • Task and follow-up management

  4. Reporting and Analytics:

    • Data visualization tools
    • Custom report generation
    • Key performance indicators (KPIs) tracking

  5. Integration Capabilities:

    • API for connecting with other industry-specific tools
    • Export functionality for cleaned and processed data

User Requirements:

  • Intuitive web interface accessible from various devices
  • Secure login and role-based access control
  • Ability to import and manage large datasets
  • Customizable dashboard for quick insights
  • Robust search and filtering capabilities

Fluxos de Usuário

  1. Data Import and Cleaning:

    • User uploads government dataset
    • System automatically detects file format
    • Data cleaning process is initiated
    • User reviews and approves cleaned data
    • Cleaned data is integrated into the CRM

  2. User Prioritization:

    • User selects prioritization criteria
    • System processes cleaned data based on criteria
    • Prioritized user list is generated
    • User reviews and can adjust priorities manually if needed

  3. Customer Interaction:

    • User selects a prioritized customer
    • System displays customer details and interaction history
    • User logs new interaction or schedules follow-up
    • System updates customer priority based on new information

Especificações Técnicas

Frontend:

  • React.js for building a responsive and interactive user interface
  • Redux for state management
  • Material-UI for consistent and professional design components

Backend:

  • Node.js with Express.js for API development
  • Python for data processing and cleaning scripts
  • MongoDB for flexible data storage

Data Processing:

  • Pandas and NumPy for data manipulation and cleaning
  • Apache Airflow for workflow management and scheduling

Authentication:

  • JWT (JSON Web Tokens) for secure user authentication

Hosting:

  • AWS EC2 for application hosting
  • AWS S3 for file storage

Version Control:

  • Git with GitHub for collaborative development

Endpoints da API

  1. /api/auth

    • POST /login: User authentication
    • POST /logout: User logout

  2. /api/data

    • POST /import: Upload new dataset
    • GET /status: Check data processing status
    • GET /cleaned: Retrieve cleaned data

  3. /api/users

    • GET /prioritized: Retrieve prioritized user list
    • PUT /:id: Update user information
    • GET /:id/history: Retrieve user interaction history

  4. /api/reports

    • GET /generate: Generate custom reports
    • GET /kpi: Retrieve KPI data

Esquema do Banco de Dados

  1. Users Collection:

    • _id: ObjectId
    • name: String
    • email: String
    • company: String
    • priority: Number
    • lastInteraction: Date

  2. Interactions Collection:

    • _id: ObjectId
    • userId: ObjectId (reference to Users)
    • type: String
    • date: Date
    • notes: String

  3. DataSets Collection:

    • _id: ObjectId
    • name: String
    • source: String
    • importDate: Date
    • cleanedDate: Date
    • status: String

  4. PrioritizationRules Collection:

    • _id: ObjectId
    • name: String
    • criteria: Object
    • weight: Number

Estrutura de Arquivos

/ ├── client/ │ ├── src/ │ │ ├── components/ │ │ ├── pages/ │ │ ├── redux/ │ │ ├── utils/ │ │ └── App.js │ ├── public/ │ └── package.json ├── server/ │ ├── src/ │ │ ├── routes/ │ │ ├── controllers/ │ │ ├── models/ │ │ ├── middleware/ │ │ └── app.js │ ├── data-processing/ │ │ ├── cleaning_scripts/ │ │ └── prioritization_scripts/ │ └── package.json ├── airflow/ │ └── dags/ ├── tests/ ├── docs/ └── docker-compose.yml

Plano de Implementação

  1. Project Setup (2 weeks):

    • Set up development environment
    • Initialize Git repository
    • Create basic project structure

  2. Backend Development (4 weeks):

    • Develop API endpoints
    • Implement data models
    • Set up authentication system

  3. Data Processing (3 weeks):

    • Develop data cleaning scripts
    • Implement user prioritization algorithm
    • Set up Airflow for data workflow management

  4. Frontend Development (5 weeks):

    • Create responsive UI components
    • Implement state management with Redux
    • Develop data visualization components

  5. Integration and Testing (3 weeks):

    • Integrate frontend with backend
    • Perform unit and integration testing
    • Conduct user acceptance testing

  6. Deployment and Documentation (2 weeks):

    • Set up production environment
    • Deploy application to AWS
    • Create user and technical documentation

  7. Final Testing and Launch (1 week):

    • Perform final QA
    • Soft launch and gather initial feedback
    • Official launch

Estratégia de Implantação

  1. Development Environment:

    • Use Docker for consistent development environments
    • Implement CI/CD pipeline with GitHub Actions

  2. Staging Environment:

    • Deploy to AWS EC2 staging instance
    • Use separate MongoDB instance for staging data

  3. Production Environment:

    • Deploy backend to AWS EC2 with load balancing
    • Host frontend on AWS S3 with CloudFront for CDN
    • Use MongoDB Atlas for production database

  4. Monitoring and Maintenance:

    • Implement AWS CloudWatch for application monitoring
    • Set up automated backups for database and file storage
    • Establish a regular update and maintenance schedule

Justificativa do Design

The chosen tech stack (React, Node.js, Python) provides a balance of performance and flexibility, crucial for handling complex data processing tasks. MongoDB was selected for its schema-less nature, allowing for easy adaptation to varying data structures from different government sources. The use of Airflow for data workflow management ensures scalability and reliability in processing large datasets. The modular architecture and use of containerization with Docker facilitate easier maintenance and potential future scaling of the application.