{
  "format": "dataset-manifest/1",
  "slug": "fractional-ownership-investment-platform",
  "version": 4,
  "seed": "fractional-ownership-investment-platform:1",
  "engine": 1,
  "engineVersion": "1.0.0",
  "spec": {
    "format": "dataset/1",
    "slug": "fractional-ownership-investment-platform",
    "name": "Fractional Investment Insights",
    "tagline": "User engagement, asset valuations, transactions and support interactions for fractional investment in high-value assets.",
    "description": "Investors engage with fractional ownership of high-value assets such as real estate, fine art, and collectibles through an innovative investment platform. Users navigate this marketplace, exploring diverse asset offerings and making decisions on their investments. The data captures user engagement metrics, asset valuations, financial transactions, and support interactions, providing insights into investor behavior and trends over time. The marketplace experiences heightened activity, particularly during opening hours, with significant spikes in asset listings and transaction volumes at the start of each month as investors flock to newly available opportunities.\n\nIn this dataset, the 'users' table represents individuals participating in the investment platform, detailing their profiles and activity levels. The 'assets' table lists the high-value items available for fractional ownership, providing key details such as valuations and asset types. Each row in the 'transactions' table records an investment action taken by users, linking them to specific assets and indicating the amount invested. The 'support_interactions' table captures user inquiries and resolutions, reflecting the level of customer service engagement within the platform. Finally, the 'valuations' table provides periodic assessments of asset worth, allowing for insights into market trends and fluctuations.\n\nBy exploring these interrelated tables, developers can answer questions about user trends, asset performance, and transaction behavior. This dataset is particularly useful for practicing data analysis skills, such as querying relationships between user engagement and asset valuations or examining the impact of support interactions on user satisfaction. Note that all data is synthetic, designed to mirror real-world scenarios while ensuring privacy and compliance.",
    "domain": "fintech",
    "shape": "time-series",
    "useCases": [
      "analytics-demo",
      "bi-dashboards"
    ],
    "keywords": [
      "investment",
      "fractional ownership",
      "assets",
      "user engagement",
      "financial transactions",
      "support interactions",
      "valuation trends"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "users": {
        "description": "This table captures user details and their engagement metrics on the investment platform. Each row represents a unique user and links to their transactions and support interactions.",
        "rows": 150,
        "columns": {
          "user_id": {
            "kind": "pk",
            "description": "Unique identifier for each user."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The user's full name."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "The user's email address."
          },
          "signup_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date the user signed up for the platform."
          },
          "engagement_score": {
            "kind": "int",
            "min": 0,
            "max": 100,
            "description": "A score representing the user's engagement level with the platform.",
            "distribution": "high"
          }
        }
      },
      "assets": {
        "description": "This table lists the various assets available for fractional investment. Each asset links to its valuations and transaction records.",
        "rows": 100,
        "columns": {
          "asset_id": {
            "kind": "pk",
            "description": "Unique identifier for each asset."
          },
          "asset_name": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "The name or description of the asset.",
            "pool": [
              "equity",
              "bond",
              "fund",
              "realestate",
              "commodity",
              "index",
              "stock",
              "cryptocurrency",
              "portfolio",
              "option",
              "derivative",
              "savings",
              "yield",
              "investment",
              "asset",
              "capital",
              "dividend",
              "share",
              "risk",
              "return",
              "market"
            ]
          },
          "category": {
            "kind": "enum",
            "values": [
              "real estate",
              "fine art",
              "collectibles"
            ],
            "description": "Category of the asset.",
            "weights": [
              8,
              2,
              1
            ]
          },
          "initial_value": {
            "kind": "decimal",
            "min": 1000,
            "max": 1000000,
            "scale": 2,
            "description": "The initial valuation of the asset.",
            "distribution": "long-tail"
          },
          "listing_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date the asset was listed for investment."
          }
        }
      },
      "transactions": {
        "description": "This table records financial transactions made by users on the platform, linking users to the assets they invested in.",
        "rows": 700,
        "columns": {
          "transaction_id": {
            "kind": "pk",
            "description": "Unique identifier for each transaction."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.user_id",
            "description": "The user who made the transaction."
          },
          "asset_id": {
            "kind": "fk",
            "ref": "assets.asset_id",
            "description": "The asset involved in the transaction."
          },
          "transaction_date": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "dayProfile": "business",
            "description": "The date and time of the transaction."
          },
          "amount": {
            "kind": "decimal",
            "min": 100,
            "max": 100000,
            "scale": 2,
            "description": "The amount of money involved in the transaction.",
            "distribution": "long-tail"
          },
          "transaction_type": {
            "kind": "enum",
            "values": [
              "buy",
              "sell",
              "dividend"
            ],
            "weights": [
              70,
              20,
              10
            ],
            "description": "Type of transaction performed by the user."
          },
          "transaction_fee": {
            "kind": "decimal",
            "min": 0.5,
            "max": 100,
            "scale": 2,
            "distribution": "long-tail",
            "description": "Fee charged for the transaction."
          }
        }
      },
      "support_interactions": {
        "description": "This table tracks support interactions users have with the platform, linking to the respective users and providing insights into support needs.",
        "rows": 300,
        "columns": {
          "interaction_id": {
            "kind": "pk",
            "description": "Unique identifier for each support interaction."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.user_id",
            "description": "The user who initiated the support interaction."
          },
          "interaction_date": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "dayProfile": "business",
            "description": "The date and time of the support interaction."
          },
          "issue_type": {
            "kind": "enum",
            "values": [
              "technical",
              "billing",
              "general inquiry"
            ],
            "description": "Type of issue the user is contacting support about.",
            "weights": [
              5,
              3,
              2
            ]
          },
          "resolution_status": {
            "kind": "enum",
            "values": [
              "resolved",
              "pending",
              "escalated"
            ],
            "description": "Current status of the support interaction.",
            "weights": [
              7,
              2,
              1
            ]
          }
        }
      },
      "valuations": {
        "description": "This table contains valuation updates for assets over time, allowing analysis of value trends and user investment behavior.",
        "rows": 400,
        "columns": {
          "valuation_id": {
            "kind": "pk",
            "description": "Unique identifier for each valuation record."
          },
          "asset_id": {
            "kind": "fk",
            "ref": "assets.asset_id",
            "description": "The asset being valued."
          },
          "valuation_date": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "dayProfile": "business",
            "description": "The date of the valuation."
          },
          "current_value": {
            "kind": "decimal",
            "min": 1000,
            "max": 1000000,
            "scale": 2,
            "description": "The current valuation of the asset.",
            "distribution": "long-tail"
          },
          "previous_value": {
            "kind": "decimal",
            "min": 1000,
            "max": 1000000,
            "scale": 2,
            "distribution": "normal",
            "description": "Valuation of the asset prior to the current valuation."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "timestamped",
      "monetary",
      "categorical",
      "nullable"
    ],
    "analysisQuestions": [
      "What is the trend of investment volumes over different months?",
      "How do support interaction types vary by user engagement levels?",
      "What categories of assets are most frequently listed and transacted?",
      "How have asset valuations changed over time for different asset types?",
      "Which users have the highest engagement scores and what are their transaction habits?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Users and Transactions",
        "sql": "SELECT u.full_name, t.amount FROM users u JOIN transactions t ON u.user_id = t.user_id;",
        "dialect": "postgres"
      },
      {
        "title": "Aggregate Transaction Amounts by Month",
        "sql": "SELECT DATE_TRUNC('month', transaction_date) AS month, SUM(amount) AS total_amount FROM transactions GROUP BY month ORDER BY month;",
        "dialect": "postgres"
      },
      {
        "title": "Valuation Trends Over Time",
        "sql": "SELECT v.valuation_date, AVG(v.current_value) AS average_value FROM valuations v GROUP BY v.valuation_date ORDER BY v.valuation_date;",
        "dialect": "postgres"
      },
      {
        "title": "User Engagement and Support Issues",
        "sql": "SELECT u.full_name, COUNT(si.interaction_id) AS support_issues FROM users u LEFT JOIN support_interactions si ON u.user_id = si.user_id GROUP BY u.full_name;",
        "dialect": "postgres"
      },
      {
        "title": "Monthly Asset Listings and Transactions",
        "sql": "SELECT DATE_TRUNC('month', a.listing_date) AS month, COUNT(a.asset_id) AS listings, COUNT(t.transaction_id) AS transactions FROM assets a LEFT JOIN transactions t ON a.asset_id = t.asset_id GROUP BY month;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Financial Analysts Exploring Investment Trends",
        "body": "Financial analysts can use this dataset to analyze trends in fractional investments, focusing on how user engagement correlates with asset valuations and transaction volumes. By running queries on the dataset, analysts can derive insights that inform investment strategies and platform improvements."
      },
      {
        "title": "Data Scientists Building Predictive Models",
        "body": "Data scientists can leverage this dataset to build predictive models that forecast future asset valuations or user engagement metrics. The time-series nature of the data allows for robust modeling techniques that can help enhance the platform's offerings and user experience."
      },
      {
        "title": "Educators Teaching Data Analysis",
        "body": "Educators can utilize this dataset in their courses to teach students about data analysis techniques, SQL querying, and BI dashboard creation. It provides a practical context for applying theoretical concepts in finance and investments, making learning more engaging."
      },
      {
        "title": "Business Intelligence Professionals Creating Dashboards",
        "body": "Business intelligence professionals can use this dataset to create interactive dashboards that visualize investment trends, user engagement, and asset performance. The insights from these dashboards can support strategic decision-making for the investment platform."
      }
    ]
  },
  "counts": {
    "assets": 100,
    "users": 150,
    "support_interactions": 300,
    "transactions": 700,
    "valuations": 400
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