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| test_cases: | |
| - name: "thoughtspot_legal_ops_spend" | |
| company: "thoughtspot.com" | |
| use_case: "Legal Operations and Spend Management" | |
| model: "claude-sonnet-4.5" | |
| tags: [legal, operations, spend, generic] | |
| context: | | |
| Target persona: General Counsel / VP of Legal Operations. | |
| Build a schema with exactly 2 fact tables and 4 dimension tables. | |
| Fact table 1: LEGAL_MATTERS (workload/matter management) | |
| Columns: | |
| - matter_id | |
| - matter_key (example: LGL-6691) | |
| - summary | |
| - request_type (Sales, NDA, Procurement, Alliance, Marketing, Other) | |
| - priority (P1, P2, P3) | |
| - status (New, In Progress, Done) | |
| - assigned_attorney_id (FK) | |
| - reporter_name | |
| - created_date | |
| - updated_date | |
| - resolved_date | |
| - matter_type_id (FK to MATTER_TYPES) | |
| - region (US East, US West, EMEA, APAC) | |
| - is_customer_deal (boolean) | |
| - sfdc_opportunity_value | |
| Fact table 2: OUTSIDE_COUNSEL_INVOICES (spend management) | |
| Columns: | |
| - invoice_id | |
| - outside_counsel_id (FK) | |
| - matter_id (FK to LEGAL_MATTERS) | |
| - matter_number (business reference only; do not use as the relationship key) | |
| - description | |
| - services_amount | |
| - costs_amount | |
| - total_amount | |
| - invoice_date | |
| - invoice_month | |
| - payment_status (Paid, Pending, Overdue) | |
| IMPORTANT RELATIONSHIP RULE: | |
| - MATTER_TYPE belongs to LEGAL_MATTERS through matter_type_id. | |
| - OUTSIDE_COUNSEL_INVOICES must link to LEGAL_MATTERS through matter_id. | |
| - Do not make invoices carry an independent matter_type truth. | |
| Dimension table 1: ATTORNEYS | |
| - 100 in-house attorneys | |
| - attorney_id, attorney_name, title (Associate GC, Senior Counsel, Paralegal, VP Legal, GC), | |
| department (Commercial, IP, Employment, Corporate, Compliance), hire_date, manager_name | |
| Dimension table 2: OUTSIDE_COUNSEL | |
| - 5 firms: Cooley LLP, Young Basile, Baker McKenzie, McDermott Will & Emery, Newman Du Wors | |
| - firm_id, firm_name, specialty (IP, Corporate, Employment, Commercial, Regulatory), | |
| billing_model (Hourly, Flat Fee, Blended), avg_hourly_rate | |
| Dimension table 3: MATTER_TYPES | |
| - NDA, Sales Agreement, Procurement, Alliance, Marketing, Patent Filing, Trademark Application, | |
| Litigation, Employment, Corporate Governance | |
| Dimension table 4: DATE_DIM | |
| - Standard date dimension | |
| Outlier patterns to include: | |
| - November spend spike about 40% higher than other months, driven by 4 new patent filings and litigation surge | |
| - Travis Guerre spends disproportionate time on NDA reviews versus peers | |
| - One attorney should have significantly longer completion times on Sales matters | |
| - Cooley LLP should be highest-spend firm, followed by Baker McKenzie | |
| - Q4 should show higher volume across all request types | |
| Data volume: | |
| - About 2,000 legal matters over 24 months | |
| - About 500 invoices across the 5 firms | |
| Key Spotter questions to support: | |
| - Why was spend so high in November? | |
| - What were the top 3 outside counsels by spend? | |
| - What does Travis spend most of his time on? | |
| - Average time to close NDA requests | |
| - Which attorneys have the longest completion times for Sales matters? | |
| QUALITY GUARDRAILS (required): | |
| - Keep month-over-month change for core spend and volume measures within +/-20% in normal months. | |
| - Allow large changes only for named events (November spend spike and Q4 lift), with at most 2 outlier periods. | |
| - Enforce arithmetic consistency: total_amount = services_amount + costs_amount. | |
| - Keep payment_status and request_type values strictly within listed domains. | |
| - Keep trend lines smooth and realistic, not random jagged spikes. | |
| - name: "thoughtspot_saas_finance_defined" | |
| company: "thoughtspot.com" | |
| use_case: "Financial Analytics" | |
| model: "claude-sonnet-4.5" | |
| tags: [saas, finance, arr, defined] | |
| context: | | |
| Build a SaaS finance story for a CFO/VP Finance persona. | |
| Focus metrics: | |
| - ARR, MRR, NRR, Gross Revenue Retention, churn_rate | |
| - expansion_arr, contraction_arr, new_logo_arr | |
| - CAC, LTV, payback_months | |
| - billings, collections, deferred_revenue | |
| Data expectations: | |
| - 24 months of monthly history | |
| - 1,500-2,500 customer-account records | |
| - Enterprise, Mid-Market, SMB segments | |
| - Regions: North America, EMEA, APAC | |
| - Required dimensions: DATES, CUSTOMERS, PRODUCTS, LOCATIONS | |
| - Required facts: SAAS_CUSTOMER_MONTHLY, SALES_MARKETING_SPEND_MONTHLY | |
| - SAAS_CUSTOMER_MONTHLY grain must be one row per customer per month | |
| - SALES_MARKETING_SPEND_MONTHLY grain must be one row per month per segment per region | |
| - SALES_MARKETING_SPEND_MONTHLY must carry NRR_PCT, GRR_PCT, NET_NEW_ARR_USD, and CAC_USD | |
| Outlier patterns to include: | |
| - Q4 ARR uplift from enterprise expansion | |
| - July SMB churn spike and temporary NRR dip | |
| - EMEA collections lag increases DSO | |
| - One segment with unusually strong upsell conversion | |
| Key Spotter questions: | |
| - Why did ARR jump in Q4? | |
| - Which segment is driving the most churn? | |
| - What are the top drivers of NRR change month over month? | |
| - Where are collections delays impacting cash flow? | |
| QUALITY GUARDRAILS (required): | |
| - Keep ARR/MRR/NRR time series smooth with normal month-over-month movement within +/-20%. | |
| - Restrict major variance to named events only (Q4 ARR uplift and July SMB churn spike), max 2 outlier periods. | |
| - Preserve metric relationships: ending_arr approximately prior_arr + expansion_arr + new_logo_arr - churned_arr - contraction_arr. | |
| - Preserve metric relationships: mrr approximately ending_arr / 12. | |
| - Preserve spend relationships: total_s_and_m_spend = sales_spend + marketing_spend. | |
| - Preserve derived KPI relationships: nrr and grr should reconcile to the monthly ARR movement components for each segment/region. | |
| - Keep categorical fields in-domain only (segment and region lists above). | |
| - Avoid synthetic or nonsense category values. | |
| - name: "datadog_saas_finance_generic" | |
| company: "datadog.com" | |
| use_case: "SaaS Finance and Unit Economics" | |
| model: "claude-sonnet-4.5" | |
| tags: [saas, finance, unit_economics, generic] | |
| context: | | |
| Create a generic SaaS finance and unit economics demo for FP&A and Finance Ops. | |
| Include realistic SaaS business entities: | |
| - Customers/accounts, subscriptions, invoices, payments, usage | |
| - Product lines (core platform, security, observability, add-ons) | |
| - Segments, geo regions, and contract terms | |
| - Required dimensions: DATES, CUSTOMERS, PRODUCTS, LOCATIONS | |
| - Required facts: SAAS_CUSTOMER_MONTHLY, SALES_MARKETING_SPEND_MONTHLY | |
| - SAAS_CUSTOMER_MONTHLY grain must be one row per customer per month | |
| - SALES_MARKETING_SPEND_MONTHLY grain must be one row per month per segment per region | |
| - SALES_MARKETING_SPEND_MONTHLY must carry NRR_PCT, GRR_PCT, NET_NEW_ARR_USD, and CAC_USD | |
| Core metrics: | |
| - ARR, MRR, NRR, GRR | |
| - churned_arr, expansion_arr, net_new_arr | |
| - CAC, LTV, gross_margin, payback_period | |
| - billed_vs_collected, deferred_revenue | |
| Outlier patterns to include: | |
| - November billings spike due to annual prepay renewals | |
| - One product line has margin compression for two quarters | |
| - APAC has stronger net new ARR growth than other regions | |
| - SMB churn rises while enterprise expansion offsets total ARR | |
| Key Spotter questions: | |
| - Why were billings unusually high in November? | |
| - Which product line has the weakest margin trend? | |
| - What is driving NRR by segment? | |
| - Which region contributes most to net new ARR? | |
| QUALITY GUARDRAILS (required): | |
| - Keep monthly finance series smooth with normal month-over-month movement within +/-20%. | |
| - Allow major movement only for named events (November billings spike, two-quarter margin compression), max 2 outlier periods. | |
| - Use strict arithmetic consistency where relevant (for example billed_vs_collected and deferred revenue behavior over time). | |
| - Preserve metric relationships: ending_arr approximately prior_arr + expansion_arr + new_logo_arr - churned_arr - contraction_arr. | |
| - Preserve metric relationships: mrr approximately ending_arr / 12. | |
| - Preserve spend relationships: total_s_and_m_spend = sales_spend + marketing_spend. | |
| - Preserve derived KPI relationships: nrr and grr should reconcile to the monthly ARR movement components for each segment/region. | |
| - Use only realistic categorical values for product lines, segments, and regions. | |
| - For organization entities, prefer ACCOUNT_NAME or COMPANY_NAME style values; avoid person-like names in org columns. | |
| - name: "statestreet_private_equity_lp_reporting" | |
| company: "statestreet.com" | |
| use_case: "Private Equity Portfolio Analytics for LP Reporting" | |
| model: "claude-sonnet-4.5" | |
| tags: [private_equity, lp_reporting, portfolio_analytics, statestreet] | |
| context: | | |
| Schema Requirements - 1 fact table and 5 dimension tables: | |
| FACT TABLE: PORTFOLIO_PERFORMANCE (quarterly reporting data per portfolio company per quarter) | |
| - record_id | |
| - fund_id (FK) | |
| - company_id (FK) | |
| - quarter_date | |
| - vintage_year | |
| - invested_capital | |
| - reported_value (NAV) | |
| - distributions | |
| - total_value (reported_value + distributions) | |
| - gross_irr | |
| - net_irr | |
| - gross_irr_without_sub_line | |
| - irr_sub_line_impact_bps | |
| - total_return_multiple (TVPI) | |
| - dpi_multiple | |
| - rvpi_multiple | |
| - revenue | |
| - ebitda | |
| - net_debt | |
| - entry_ev_ebitda_multiple | |
| - current_ev_ebitda_multiple | |
| - revenue_growth_pct | |
| - ebitda_margin_pct | |
| - debt_to_ebitda_ratio | |
| - covenant_status (Compliant, Waived, Breached) | |
| - debt_performance_status (Performing, Watch List, Non-Performing) | |
| DIM TABLE 1: FUNDS | |
| - fund_id | |
| - fund_name ("Alpha Private Equity Fund III, L.P." and 2-3 others) | |
| - fund_manager | |
| - fund_size | |
| - fund_currency | |
| - fund_strategy (Buyout, Growth, Venture) | |
| - inception_date | |
| DIM TABLE 2: PORTFOLIO_COMPANIES | |
| - company_id | |
| - company_name (10 realistic PE-backed companies like "Apex Industrial Solutions", "Meridian Specialty Chemicals", "NovaTech Data Systems") | |
| - sector (Industrials, Healthcare, Technology, Consumer, Financial Services) | |
| - sub_sector | |
| - headquarters_state | |
| - investment_date | |
| - investment_stage (Active, Realized, Written Off) | |
| - board_seats_held | |
| DIM TABLE 3: SECTORS | |
| - sector_id | |
| - sector_name | |
| - sector_category (Cyclical, Defensive, Growth) | |
| DIM TABLE 4: LP_INVESTORS | |
| - investor_id | |
| - investor_name (pension funds, endowments, sovereign wealth) | |
| - investor_type (Pension Fund, Endowment, Family Office, Sovereign Wealth, Insurance) | |
| - commitment_amount | |
| - aum_total | |
| DIM TABLE 5: DATE_DIM | |
| - standard quarterly date dimension | |
| Outlier patterns to bake in (critical for demo narrative): | |
| - Subscription line impact: all companies should have gross_irr_without_sub_line that is 80-210 bps lower than gross_irr. Apex Industrial Solutions should have the highest impact at exactly 210 bps. | |
| - Meridian Specialty Chemicals should have covenant_status = 'Waived' as the only company with a non-compliant status. | |
| - Technology sector investments made at higher entry multiples (12-15x EV/EBITDA) should have the highest total return multiples (2.5-3.8x TVPI). | |
| - 2021 and 2022 vintage years should hold the most reported value. | |
| - Q4 2024 should show a performance dip in Healthcare sector. | |
| - One company should show revenue growing but EBITDA margin declining. | |
| Data volume: | |
| - 10 portfolio companies x 12 quarters (3 years) = ~120 rows in fact table | |
| - Keep it small and realistic (private equity reporting model) | |
| - 5 LP investors, 3 funds | |
| Target persona: | |
| - Portfolio Manager at a $50B Pension Fund (LP client using the embedded experience) | |
| Key Spotter questions the demo MUST support (in this order): | |
| - show me my total invested and reported value by sector | |
| - now by vintage year | |
| - compare gross irr with and without subscription facility for my active investments | |
| - which company has the highest irr impact from the sub line? | |
| - show me the trend of revenue vs ebitda for Apex Industrial Solutions since investment | |
| - what is the covenant status for my active investments with debt status performing? | |
| - show me the relationship between entry ev/ebitda multiple and total return multiple by sector | |