{ "format": "dataset-manifest/1", "slug": "financial-planning-insights", "version": 1, "seed": "financial-planning-insights:1", "spec": { "format": "dataset/1", "slug": "financial-planning-insights", "name": "Financial Planning Insights", "tagline": "User financial interactions, including consultations and net worth progression, from a financial planning platform.", "description": "Users on the platform engage with expert financial planners to enhance their personal finance management. They track budgets, monitor investments, and assess tax implications while receiving tailored advice aimed at achieving specific financial milestones. Each interaction between users and planners is documented, providing a clear record of consultations and feedback on the advice provided. This allows for a comprehensive view of user experiences and outcomes in their financial journeys. Users generally exhibit steady growth in net worth over time, although some may face significant fluctuations due to life changes or impactful investment choices.\n\nThe primary table, user_financial_interactions, captures the data related to these user-planner interactions. Each row in this table represents a unique financial consultation, detailing user identification, planner engagement, specific topics discussed, user feedback, and observed changes in net worth following the sessions. This structure enables the exploration of correlations between user feedback and financial outcomes, revealing how effective the planners' advice has been in guiding users towards their financial goals.\n\nData from this dataset can answer various questions about user behavior and financial progression: How do users' financial situations improve after consultations? What types of advice lead to the most significant changes in net worth? Are there particular milestones that are commonly reached after specific planner interactions? The data is particularly useful for practicing data analysis techniques, such as trend analysis, correlation assessments, and user segmentation, allowing developers to simulate real-world financial advising scenarios.\n\nIt's important to note that the data in this dataset is synthetic, offering a realistic framework for testing and experimentation without the complications of real user information.", "domain": "saas", "shape": "transactional", "useCases": [ "sql-practice", "orm-seeding" ], "keywords": [ "personal finance", "financial planning", "user interactions", "net worth", "investment tracking", "budget management", "financial advice", "feedback", "milestones", "consultations" ], "locale": "en", "license": "CC0-1.0", "version": 1, "tables": { "user_financial_interactions": { "description": "Each row captures a distinct interaction between a user and a financial planner, detailing their financial status and feedback on advice received.", "rows": 500, "columns": { "user_id": { "kind": "pk", "description": "A unique identifier for each user." }, "interaction_date": { "kind": "datetime", "from": "2020-01-01", "to": "2023-12-31", "description": "The date and time the interaction occurred.", "dayProfile": "waking" }, "budget": { "kind": "decimal", "min": 1000, "max": 100000, "scale": 2, "description": "The user's current budget amount.", "nullable": true }, "net_worth": { "kind": "decimal", "min": 5000, "max": 5000000, "scale": 2, "description": "The user's total net worth at the time of interaction.", "nullable": true }, "investment_growth": { "kind": "decimal", "min": -100000, "max": 1000000, "scale": 2, "description": "The growth of the user's investments since the last interaction.", "nullable": true, "when": { "column": "life_event", "in": [ "promotion", "inheritance" ] } }, "milestones_achieved": { "kind": "int", "min": 0, "max": 10, "description": "The number of financial milestones the user has achieved.", "nullable": true, "when": { "column": "life_event", "in": [ "promotion", "inheritance" ] } }, "feedback_rating": { "kind": "enum", "values": [ "poor", "average", "good", "excellent" ], "weights": [ 0.15, 0.35, 0.25, 0.25 ], "description": "User's rating of the financial advice received, with weights for realistic distribution." }, "life_event": { "kind": "enum", "values": [ "none", "promotion", "inheritance", "purchase", "divorce" ], "weights": [ 0.5, 0.2, 0.1, 0.1, 0.1 ], "description": "Any significant life event influencing the user's financial decisions." } } } }, "structure": "single-table", "traits": [ "monetary", "timestamped", "nullable" ], "analysisQuestions": [ "How does user feedback correlate with their net worth progression?", "What are the common life events that lead to drastic changes in users' financial status?", "Which financial milestones are most frequently achieved by users over time?", "What trends can be observed in investment growth across different user demographics?", "How do budget amounts impact the effectiveness of the financial advice received?" ], "suggestedQueries": [ { "title": "List all user interactions with feedback ratings", "sql": "SELECT user_id, interaction_date, feedback_rating FROM user_financial_interactions;", "dialect": "postgres" }, { "title": "Calculate average net worth by feedback rating", "sql": "SELECT feedback_rating, AVG(net_worth) AS average_net_worth FROM user_financial_interactions GROUP BY feedback_rating;", "dialect": "postgres" }, { "title": "Count milestones achieved per user", "sql": "SELECT user_id, COUNT(milestones_achieved) FROM user_financial_interactions GROUP BY user_id;", "dialect": "postgres" }, { "title": "Find users with the highest investment growth", "sql": "SELECT user_id, investment_growth FROM user_financial_interactions ORDER BY investment_growth DESC LIMIT 10;", "dialect": "postgres" } ], "usageScenarios": [ { "title": "Financial Advisors", "body": "Financial advisors can utilize this dataset to analyze the effectiveness of their advice based on user feedback and net worth changes, allowing them to refine their strategies and understand client needs better." }, { "title": "Data Analysts", "body": "Data analysts can use the dataset to perform trend analysis on user financial behaviors, identifying correlations between life events and changes in financial status, which can inform future financial products." }, { "title": "Educators", "body": "Educators in finance can employ this dataset in classroom settings to teach students about personal finance management, investment strategies, and the impact of financial planning on overall wealth." }, { "title": "Developers and Data Scientists", "body": "Developers and data scientists can leverage this synthetic dataset for testing and training machine learning models aimed at predicting user financial behaviors based on historical interactions and outcomes." } ] }, "counts": { "user_financial_interactions": 500 }, "stats": { "user_financial_interactions": { "rows": 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