data-source-audit

# Data Source Audit for Construction

## Overview

Perform comprehensive audits of construction data sources to identify silos, map data flows, assess quality, and plan integration strategies. Essential for digital transformation and data-driven construction initiatives.

## Business Case

Construction organizations typically have 10-50+ data sources:
- Project management systems
- Estimating software
- Scheduling tools
- Accounting/ERP systems
- BIM platforms
- Document management systems
- Field apps
- Spreadsheets

> **Note:** This skill is vendor-agnostic and works with any data source. Product names mentioned elsewhere in examples are trademarks of their respective owners.

This skill helps:
- Discover all data sources
- Map data flows and dependencies
- Identify integration opportunities
- Prioritize data improvement efforts

## Technical Implementation

```python
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Set
from enum import Enum
from datetime import datetime
import pandas as pd
import json

class DataSourceType(Enum):
    DATABASE = "database"
    API = "api"
    FILE_SHARE = "file_share"
    CLOUD_APP = "cloud_app"
    SPREADSHEET = "spreadsheet"
    LEGACY_SYSTEM = "legacy_system"
    IOT_SENSOR = "iot_sensor"
    MANUAL_ENTRY = "manual_entry"

class DataDomain(Enum):
    COST = "cost"
    SCHEDULE = "schedule"
    BIM = "bim"
    DOCUMENT = "document"
    FIELD = "field"
    SAFETY = "safety"
    QUALITY = "quality"
    HR = "hr"
    ACCOUNTING = "accounting"
    PROCUREMENT = "procurement"

@dataclass
class DataSource:
    name: str
    source_type: DataSourceType
    domains: List[DataDomain]
    owner: str
    department: str
    description: str
    # Technical details
    technology: str
    location: str  # cloud, on-prem, hybrid
    access_method: str  # API, ODBC, file export, manual
    # Data characteristics
    update_frequency: s...