{
  "format": "dataset-manifest/1",
  "slug": "insurance-comparison-and-management-app",
  "version": 1,
  "seed": "insurance-comparison-and-management-app:1",
  "spec": {
    "format": "dataset/1",
    "slug": "insurance-comparison-and-management-app",
    "name": "Insurance Insights",
    "tagline": "Customer details, policy information, claims records, vehicle data, and health conditions from the insurance industry.",
    "description": "Insurance platforms connect customers with various policies, offering crucial information for comparison and management. This dataset models the interactions between customers, their insurance policies, claims processed, insured vehicles, and health conditions. Each table provides insights into different aspects of the insurance experience, allowing users to analyze policy details, customer feedback, and claims data. The relationships between these tables will enable developers to explore questions such as the correlation between policy types and the frequency of claims or how customer satisfaction varies across different health conditions and vehicle types. This synthetic dataset is designed for SQL practice and ORM seeding, providing a realistic yet manageable environment for developers to hone their skills.",
    "domain": "insurance",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "insurance",
      "policy",
      "customer",
      "claims",
      "vehicles",
      "health",
      "feedback",
      "discounts",
      "analytics"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "customers": {
        "description": "Each row represents a customer, linking to their policies and feedback. Customers provide essential context for the policies they hold.",
        "rows": 300,
        "columns": {
          "customer_id": {
            "kind": "pk",
            "description": "Unique identifier for each customer."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the customer.",
            "nullable": true
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "Email address for contacting the customer.",
            "nullable": true
          },
          "phone": {
            "kind": "phone",
            "description": "Contact phone number of the customer.",
            "nullable": true
          },
          "registration_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the customer registered.",
            "trend": "growing"
          }
        }
      },
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        "description": "Rows in this table represent insurance policies held by customers, linking back to the customers table.",
        "rows": 600,
        "columns": {
          "policy_id": {
            "kind": "pk",
            "description": "Unique identifier for each policy."
          },
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            "kind": "fk",
            "ref": "customers.customer_id",
            "description": "Reference to the customer who holds the policy."
          },
          "policy_type": {
            "kind": "enum",
            "values": [
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              "health",
              "home",
              "life"
            ],
            "description": "Type of insurance policy."
          },
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            "min": 100,
            "max": 2000,
            "scale": 2,
            "description": "Monthly premium amount for the policy.",
            "nullable": true,
            "when": {
              "column": "policy_type",
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                "life"
              ]
            }
          },
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            "min": 10000,
            "max": 1000000,
            "scale": 2,
            "description": "Total coverage amount provided by the policy.",
            "nullable": true,
            "when": {
              "column": "policy_type",
              "in": [
                "health",
                "life"
              ]
            }
          },
          "discount": {
            "kind": "decimal",
            "min": 0,
            "max": 500,
            "scale": 2,
            "description": "Discount applied to the policy premium.",
            "nullable": true
          }
        },
        "via": "customer_id"
      },
      "claims": {
        "description": "Claims processed under each policy are represented in this table, linking to the policies.",
        "rows": 400,
        "columns": {
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            "kind": "pk",
            "description": "Unique identifier for each claim."
          },
          "policy_id": {
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            "ref": "policies.policy_id",
            "description": "Reference to the policy under which the claim was made."
          },
          "claim_amount": {
            "kind": "decimal",
            "min": 100,
            "max": 100000,
            "scale": 2,
            "description": "Amount claimed by the policyholder.",
            "nullable": true,
            "when": {
              "column": "status",
              "in": [
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                "denied"
              ]
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          },
          "claim_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the claim was submitted.",
            "trend": "seasonal"
          },
          "status": {
            "kind": "enum",
            "values": [
              "pending",
              "approved",
              "denied"
            ],
            "description": "Current status of the claim."
          }
        },
        "via": "policy_id"
      },
      "vehicles": {
        "description": "This table details vehicles insured under customer policies, linking back to the policies table.",
        "rows": 500,
        "columns": {
          "vehicle_id": {
            "kind": "pk",
            "description": "Unique identifier for each vehicle."
          },
          "policy_id": {
            "kind": "fk",
            "ref": "policies.policy_id",
            "description": "Reference to the insurance policy covering this vehicle."
          },
          "make": {
            "kind": "words",
            "min": 1,
            "max": 20,
            "description": "Make of the vehicle.",
            "nullable": true
          },
          "model": {
            "kind": "words",
            "min": 1,
            "max": 20,
            "description": "Model of the vehicle.",
            "nullable": true
          },
          "year": {
            "kind": "int",
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            "description": "Manufacturing year of the vehicle."
          }
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        "via": "policy_id"
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      "health_conditions": {
        "description": "Rows in this table represent health conditions of policyholders, linking to the customers table.",
        "rows": 200,
        "columns": {
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            "kind": "pk",
            "description": "Unique identifier for each health condition entry."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.customer_id",
            "description": "Reference to the customer with the health condition."
          },
          "condition": {
            "kind": "words",
            "min": 1,
            "max": 50,
            "description": "Description of the health condition."
          },
          "diagnosis_date": {
            "kind": "date",
            "from": "2010-01-01",
            "to": "2023-12-31",
            "description": "Date when the condition was diagnosed.",
            "trend": "growing"
          }
        },
        "via": "customer_id"
      }
    },
    "structure": "multi-table",
    "traits": [
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      "monetary",
      "nullable",
      "timestamped"
    ],
    "analysisQuestions": [
      "What is the average premium across different policy types?",
      "How do claim amounts vary by vehicle make and model?",
      "Which health conditions are most frequently reported among policyholders?",
      "What percentage of claims are approved versus denied?",
      "How do discounts affect customer retention rates?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Customers and Policies",
        "sql": "SELECT c.full_name, p.policy_type FROM customers c JOIN policies p ON c.customer_id = p.customer_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Premium by Policy Type",
        "sql": "SELECT policy_type, AVG(premium) AS avg_premium FROM policies GROUP BY policy_type;",
        "dialect": "postgres"
      },
      {
        "title": "Claims Status Breakdown",
        "sql": "SELECT status, COUNT(*) AS count FROM claims GROUP BY status;",
        "dialect": "postgres"
      },
      {
        "title": "Claim Amounts by Year",
        "sql": "SELECT EXTRACT(YEAR FROM claim_date) AS year, SUM(claim_amount) AS total_claims FROM claims GROUP BY year ORDER BY year;",
        "dialect": "postgres"
      },
      {
        "title": "Health Conditions and Claims",
        "sql": "SELECT h.condition, COUNT(c.claim_id) AS claim_count FROM health_conditions h LEFT JOIN claims c ON h.customer_id = c.policy_id GROUP BY h.condition;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Insurance Policy Analysis",
        "body": "Insurance analysts can utilize this dataset to evaluate the performance of different insurance policies by analyzing customer premiums, claim rates, and the impact of discounts. By comparing policies, they can identify which offerings are most attractive to customers and adjust marketing strategies accordingly."
      },
      {
        "title": "Customer Feedback Insights",
        "body": "Customer service teams can analyze feedback and claims data to gain insights into customer satisfaction and areas for improvement. By correlating health conditions and policy types with feedback scores, they can enhance service delivery and tailor offerings that meet customer needs."
      },
      {
        "title": "Claims Processing Efficiency",
        "body": "Claims adjusters can use this dataset to assess the efficiency of claims processing by examining the time taken for claims to be approved or denied. Analyzing claims in relation to vehicle types and health conditions can help optimize the claims workflow and reduce turnaround times."
      },
      {
        "title": "Market Trend Analysis",
        "body": "Market researchers can leverage this dataset to study trends in the insurance industry, including the popularity of certain policy types, the prevalence of specific health conditions among policyholders, and the seasonal patterns in premium discounts. This information can guide strategic decisions for insurance providers."
      }
    ]
  },
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    "health_conditions": 200,
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    "claims": 400,
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