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PlayerZero AI Chat: Data Analyst Guide

As a data analyst, PlayerZero AI Chat revolutionizes how you understand data systems, discover data sources, and analyze business logic through code exploration.

  • Data Schema Discovery: Understand database structures, relationships, and data models across your entire system.
  • Business Logic Analysis: Trace how business rules are implemented in code and how they affect data quality and meaning.
  • Data Pipeline Understanding: Map data flows from collection through transformation to storage and reporting.
  • Data Quality Insights: Identify validation rules, constraints, and data processing logic that impact data integrity.
  • Data Integration Mapping: Discover how different systems exchange data and maintain consistency.

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Prompting Best Practices and Examples

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Effective Prompting Strategies

  1. Focus on Data Flows, Not Individual Tables

    • GOOD: “Show me the user table schema.”
    • BETTER: “Trace how user data flows from registration through profile updates to account deletion, including all transformations and validations.”

  2. Connect Business Logic to Data Implementation

    • GOOD: “Find revenue calculation code.”
    • BETTER: “Show me how revenue metrics are calculated in the code, including any business rules, discounts, refunds, and how this data is stored and aggregated.”

  3. Understand Data Quality Through Code

    • GOOD: “Show me data validation.”
    • BETTER: “What validation rules and data‑quality checks are implemented for customer data, and how do they ensure data consistency across the system?”

  4. Map Data Sources and Destinations

    • GOOD: “Find database connections.”
    • BETTER: “Show me all the places where user‑behavior data is collected, how it’s processed, and where it’s ultimately stored or exported.”

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Unsatisfactory Prompting Examples

  1. Schema‑Only Requests

    • POOR: “Show me the database schema.”
    • WHY: Misses business logic and data‑processing context.
    • BETTER: “Show me the customer data model including how it’s validated, processed, and used in business calculations.”

  2. Generic Data Requests

    • POOR: “Find all data tables.”
    • WHY: Too broad and doesn’t specify analytical purpose.
    • BETTER: “Show me all data models related to user engagement and how they connect to calculate retention metrics.”

  3. Tool‑Specific Without Context

    • POOR: “Show me SQL queries.”
    • WHY: Doesn’t explain what you’re trying to understand about the data.
    • BETTER: “Show me how product‑performance metrics are calculated, including the database queries and business logic involved.”

  4. Missing Business Context

    • POOR: “Find API endpoints.”
    • WHY: Doesn’t connect to data‑analysis needs.
    • BETTER: “Show me API endpoints that expose customer data and how they transform or filter information for different use cases.”

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Data Analyst Use Cases

Before you get started, double‑check which repositories and datasets PlayerZero has access to. You can find this in your settings at go.playerzero.app/settings

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Data Schema Discovery & Modeling

Database Schema Exploration
  • “Show me the complete data model for e‑commerce orders, including relationships with customers, products, payments, and shipping.”
  • “Map out the user‑engagement data structure and how different user actions are tracked and stored.”
  • “Find all the database tables and models related to financial transactions and revenue tracking.”
Data Relationship Analysis
  • “Show me how customer data relates to order history, payment information, and support tickets across different tables.”
  • “Map out the product‑catalog structure, including categories, attributes, pricing, and inventory relationships.”
  • “Find all the foreign‑key relationships in the user‑management system and how they maintain data integrity.”
Data Model Evolution
  • “Show me the database‑migration history for the order system and how the schema has changed over time.”
  • “Find all recent changes to the user data model and how they affect existing analytics.”
  • “Trace the evolution of the product‑catalog schema and identify any breaking changes.”
Cross‑System Data Mapping
  • “Show me how customer data is structured differently across the main application, CRM, and billing systems.”
  • “Map out data‑model differences between the operational database and the analytics warehouse.”
  • “Find all the data‑transformation rules when moving data between systems.”

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Business Logic & Calculations

Metric Calculation Discovery
  • “Show me how customer lifetime value is calculated in the code, including all the business rules and data sources involved.”
  • “Find how ‘active users’ is defined in the codebase and show me all the different ways this metric might be calculated across different features.”
  • “Trace how conversion rate is calculated in the code and what events and data points contribute to this metric.”
Business Rule Implementation
  • “Show me the business logic behind customer segmentation and how users are categorized for marketing campaigns.”
  • “Find the discount‑calculation logic and show me how different discount types, combinations, and business rules are applied.”
  • “Trace how subscription billing is calculated, including upgrades, downgrades, prorations, and cancellation logic.”
Data Processing Logic
  • “Show me how product‑recommendation algorithms work and what data they use to generate suggestions.”
  • “Find the logic for determining user subscription status and how it affects feature access and billing.”
  • “Trace how inventory levels are calculated and updated across different systems.”
Financial and Revenue Calculations
  • “Show me how monthly recurring revenue is calculated, including all the business rules for subscriptions, upgrades, and cancellations.”
  • “Find the tax‑calculation logic and show me how different tax rates and rules are applied based on location.”
  • “Trace how commission calculations work for sales teams, including all the business rules and data sources.”

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Data Pipeline Understanding

Data Collection and Ingestion
  • “Show me how user‑activity data flows from collection through processing to storage and reporting.”
  • “Trace the data pipeline for inventory management from product updates to stock‑level reporting.”
  • “Map out how customer‑support ticket data is processed and aggregated for reporting dashboards.”
Data Transformation Processes
  • “Show me how raw user events are transformed into meaningful analytics metrics.”
  • “Find all the data‑transformation rules applied when moving data from operational systems to the data warehouse.”
  • “Trace how product‑sales data is aggregated and transformed for executive reporting.”
ETL Process Analysis
  • “Show me how daily sales data is aggregated and transformed for the data warehouse, including any business rules or calculations.”
  • “Find the ETL processes that prepare customer data for analytics and what transformations are applied.”
  • “Trace how real‑time events are batched and processed for historical reporting.”
Data Synchronization Patterns
  • “Show me how customer data is synchronized between the main application and reporting systems, including all validation rules and consistency checks.”
  • “Find all the data‑sync processes between different databases and how they handle conflicts.”
  • “Trace how changes in the operational database are propagated to analytics systems.”

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Data Quality & Validation

Validation Rule Discovery
  • “Show me all the data‑validation rules for user profiles and how they prevent data‑quality issues.”
  • “Find the data‑consistency checks between the order system and inventory system.”
  • “Show me how duplicate detection works for customer records and email addresses.”
Data Integrity Mechanisms
  • “Show me all the data‑validation rules that ensure customer email addresses are properly formatted and unique.”
  • “Find the business logic that determines valid product categories and how hierarchy is maintained.”
  • “Trace how date ranges are validated in reporting interfaces and what constraints are enforced.”
Data Completeness Analysis
  • “Show me how required fields are enforced for customer profiles and what happens when data is missing.”
  • “Find the logic that determines when a user record is considered complete for analysis purposes.”
  • “Trace how missing data is handled in reporting and what default values or exclusions are applied.”
Data Consistency Verification
  • “Show me how the system ensures inventory counts match between the warehouse system and the e‑commerce platform.”
  • “Find all the places where user account balances are calculated and verify they use consistent business rules.”
  • “Trace how order totals are validated across different systems and what discrepancy‑handling exists.”

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Reporting & Analytics Implementation

Dashboard and Report Generation
  • “Show me how the executive dashboard calculates key business metrics and where the underlying data comes from.”
  • “Find the code that generates automated reports and how it ensures data accuracy and completeness.”
  • “Trace how custom report builders work and what data sources and calculations are available to users.”
KPI and Metric Calculation
  • “Show me how conversion rate is calculated in the code and what events and data points contribute to this metric.”
  • “Find the business logic behind customer segmentation and how users are categorized for marketing campaigns.”
  • “Trace how churn‑prediction models use customer data and what features are most important.”
Data Aggregation Patterns
  • “Show me how user‑engagement metrics are calculated and aggregated for different time periods.”
  • “Find all the roll‑up calculations used in financial reporting and how they handle different fiscal periods.”
  • “Trace how product‑performance metrics are aggregated across different categories and regions.”
Analytics Data Access
  • “Show me how analytics queries are structured and what indexes are used for performance.”
  • “Find all the data‑access patterns used by reporting tools and how they’re optimized.”
  • “Trace how user permissions affect data access in analytics and reporting systems.”

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Data Integration & External Sources

API Data Integration
  • “Show me how external marketing data is integrated into our customer‑analytics platform.”
  • “Find all the third‑party data sources and how they’re validated and processed.”
  • “Trace how payment‑processor data is integrated with our internal transaction records.”
Data Import and Export Processes
  • “Show me how customer data is exported for marketing campaigns and what transformations are applied.”
  • “Find all the data‑import processes and how they handle data validation and error recovery.”
  • “Trace how product‑catalog data is synchronized with external systems.”
Cross‑Platform Data Consistency
  • “Show me how customer data is kept consistent between the e‑commerce platform and the CRM system.”
  • “Find all the data‑synchronization mechanisms between different business systems.”
  • “Trace how changes in one system propagate to other systems and maintain data integrity.”
External Data Validation
  • “Show me how external data feeds are validated before being integrated into our analytics.”
  • “Find all the data‑quality checks applied to imported customer data.”
  • “Trace how address validation works with external services and how errors are handled.”

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Performance & Optimization

Query Performance Analysis
  • “Analyze the database queries used in customer analytics and identify optimization opportunities.”
  • “Show me the most expensive queries in the reporting system and suggest indexing strategies.”
  • “Find all the queries that might not scale well with increased data volume.”
Data Processing Optimization
  • “Show me how large data‑processing jobs are optimized and what caching strategies are used.”
  • “Find all the batch‑processing jobs and analyze their performance characteristics.”
  • “Trace how data‑aggregation processes are optimized for different reporting needs.”
Storage and Archiving Strategies
  • “Show me how historical data is archived and what retention policies are implemented.”
  • “Find all the data compression and storage‑optimization techniques used.”
  • “Trace how old data is moved between different storage tiers.”
Analytics Performance Tuning
  • “Show me how analytics queries are optimized for different user‑access patterns.”
  • “Find all the materialized views and summary tables used to improve reporting performance.”
  • “Trace how real‑time analytics are balanced with batch processing for optimal performance.”

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Data Governance & Compliance

Data Privacy Implementation
  • “Show me how personally identifiable information is handled throughout the data pipeline.”
  • “Find all the data masking and anonymization processes used in analytics.”
  • “Trace how user consent preferences affect data collection and processing.”
Data Access Control
  • “Show me how data‑access permissions are implemented across different analytics tools.”
  • “Find all the role‑based access controls for sensitive customer data.”
  • “Trace how data‑sharing agreements are enforced in the codebase.”
Audit Trail and Compliance
  • “Show me how data changes are tracked and logged for compliance purposes.”
  • “Find all the audit mechanisms for data access and modifications.”
  • “Trace how data retention policies are implemented and enforced.”
Data Lineage Tracking
  • “Trace how product‑pricing data flows from the admin interface through validation, storage, API exposure, and eventually to customer‑facing displays.”
  • “Show me how customer data moves through the system and all the transformation points.”
  • “Find all the data sources that contribute to executive reporting and how they’re combined.”