Purchasing business intelligence requires confidence in three dimensions: structure, quality, and integration readiness. Our samples demonstrate exactly how MediumAxis delivers across these dimensions—whether you’re loading mortgage leads into Salesforce, analyzing consumer households in PostgreSQL, or mapping corporate ownership for due diligence.
Every dataset ships in multiple formats because your infrastructure shouldn’t dictate your data strategy. CSV for universal compatibility. Excel for stakeholder review. SQL dumps for direct warehouse loading. JSON for API pipelines. Parquet for high-performance analytics.
We cut our teeth on this data ourselves. MediumAxis operates under The Omega Project (established 2008), and these samples reflect the same structures we use for internal demand generation, corporate intelligence research, and client delivery. Each file contains real field mappings, actual verification standards, and tested load instructions—not sanitized marketing mocks.
Common Use Cases & Software Integration
| Use Case | Business Software | Recommended Format |
|---|---|---|
| Mortgage & Financial Services Marketing Refinancing campaigns, home equity offers, lender prospecting, loan origination | Salesforce, HubSpot, Zoho CRM, Microsoft Dynamics, Excel (financial analysis) | CSV (CRM import), Excel (stakeholder review), PostgreSQL (portfolio analytics) |
| Consumer Demographics & Household Targeting Direct mail, lifestyle segmentation, purchasing behavior analysis, credit marketing | Klaviyo, Mailchimp, USPS NCOA, Tableau, Snowflake, BigQuery, Python/R | CSV (marketing lists), Parquet (columnar analytics), PostgreSQL (data warehouse) |
| Geographic & Spatial Analysis Territory planning, location-based marketing, census tract analysis | ArcGIS, QGIS, PostGIS, Snowflake, BigQuery Geo, Tableau, Power BI | PostgreSQL/PostGIS (spatial queries), CSV (GIS import), Parquet (big data) |
| B2B Sales & Account-Based Marketing Lead generation, territory planning, executive outreach, account scoring | Salesforce, HubSpot, Pipedrive, LinkedIn Sales Navigator, Apollo.io, Outreach | CSV (CRM import), JSON (API integration), PostgreSQL (account database) |
| Corporate Intelligence & M&A Ownership mapping, due diligence, compliance screening, supply chain analysis | Orbis, PitchBook, Capital IQ, CB Insights, Neo4j (graph analysis), PostgreSQL | PostgreSQL/MySQL (relational), JSON (hierarchical), CSV (spreadsheet analysis) |
| Data Science & Analytics Machine learning, predictive modeling, big data processing, statistical analysis | Snowflake, BigQuery, Databricks, Python (pandas), R, Apache Spark, Jupyter | Parquet (columnar storage), CSV (universal compatibility), JSON (nested structures) |
Sample Files: Specifications & Downloads
| Sample | Description & Key Fields | Technical Specs | Download |
|---|---|---|---|
US Mortgage & Property Records 500 records | Homeowner records with mortgage details, property valuations, lender information, and loan characteristics. Includes property purchase year, construction date, estimated value ranges, mortgage amounts, interest types (fixed/adjustable), loan-to-value ratios, and lender names. Ideal for refinancing campaigns, home equity marketing, and financial services prospecting. Key Fields: First_Name, Last_Name, Address, City, County, State, Zip, Property_Type, Phone, Gender, Age, Property_Purchased_Year, Property_Built, Property_Value_Range, Mortgage_Amount_Thousands, Lender_Name, Interest_Type, Loan_Type, Loan_To_Value, Email | Structure: 21 columns, standard CSV format Load tested: Salesforce, HubSpot, Zoho CRM, Microsoft Dynamics, PostgreSQL, Excel | |
Consumer Demographics & Lifestyle 500 records | Comprehensive household-level consumer data with 400+ attributes including demographics, purchasing behavior, lifestyle interests, credit capacity, property ownership, and charitable giving. Features detailed age/gender breakdowns, children presence by age brackets (0-2, 3-5, 6-10, 11-15, 16-17), income estimates, net worth indicators, credit ratings, and 100+ lifestyle flags (travel, hobbies, reading, sports, crafts, gardening, etc.). Key Fields: personfirstname, personlastname, primaryaddress, cityname, state, ZipCode, latitude, longitude, personexactage, estimatedincomecode, homeownerprobabilitymodel, lengthofresidence, presenceofchildren, NumberOfChildren, personmaritalstatus, Networth, CreditRating, plus 100+ lifestyle and behavioral indicators | Structure: 400+ columns (household and individual level demographics) Load tested: PostgreSQL, Snowflake, BigQuery, Python pandas, R, Tableau | |
US Population Database 500 records | National consumer database with 280+ fields covering demographics, property characteristics, financial indicators, lifestyle preferences, and contact information. Includes geocoded addresses (Latitude/Longitude), Census tract data, CBSA/MSA codes (Core Based Statistical Areas), County descriptions, home values, income estimates, credit capacity, plus extensive hobby and interest categories for precision targeting. Key Fields: First_Name_01, Last_Name_01, Address, City, State, ZIP, Latitude, Longitude, County_Description, CBSA_Description, CBSA_Code, Ind_Age, Home_Value_Description, Income_Description, NetWorth_Code, Credit_Capacity, plus 200+ lifestyle, behavioral, and property attributes | Structure: 280+ columns with geospatial coordinates (lat/long) and Census/CBSA codes Load tested: PostgreSQL with PostGIS, Snowflake, BigQuery Geo, ArcGIS, QGIS, Tableau | |
Corporate Hierarchy & Ownership 500 records | Parent-subsidiary relationship data with shareholder information, ownership percentages, and complete company profiles for both parent and subsidiary entities. Includes contact details (email, phone, website), addresses, postal codes, regions, and relationship types (e.g., “Subsidiary of”). Essential for M&A research, compliance screening, supply chain mapping, and corporate structure analysis. Key Fields: shareholder_count, has_majority_shareholder, max_stake_percentage, immediate_owner_name, parent_company_name, parent_country, parent_email, parent_website, parent_address, parent_city, parent_phone, parent_postcode, parent_region, subsidiary_company_name, subsidiary_country, subsidiary_email, subsidiary_address, subsidiary_city, relationship_type | Structure: 27 columns with parent-subsidiary linkages and ownership data Load tested: PostgreSQL 14+, MySQL 8+, Neo4j (graph import), Excel, CSV analysis | |
B2B Decision-Maker Contacts 500 records | Executive and decision-maker contacts at mid-market to enterprise companies. Includes full name, business email, job title, seniority level (C-Level, Director-Level, Manager-Level, Staff), department, company revenue ranges, employee count estimates, industry classification, LinkedIn profile URLs, company descriptions, and domain information. Ready for CRM import and account-based marketing campaigns. Key Fields: full_name, business_name, email, business_revenue, mc_companySize, company_description, title, job_level, city, state, linkedin_url, department, industry, phone, domain, sector, country | Structure: 16 columns with contact, company, and professional networking data Load tested: Salesforce, HubSpot, Pipedrive, Microsoft Dynamics 365, Apollo.io, LinkedIn Sales Navigator | |
API Response Structure 100 records | Same records across multiple entity types (consumer demographic, mortgage, corporate, B2B contact) in nested JSON format. Demonstrates REST API schema, relationship nesting, and field enrichment depth for developer integration testing. Based on US Population data structure with full field nesting. Schema: Nested JSON with entity relationships (demographics embedded with property data, contact info nested) | Use case: Developer sandbox testing, schema validation, deserialization benchmarking Load tested: REST client testing (Postman, Insomnia), Python requests, Node.js fetch, curl |
Custom Format Requests
Standard not sufficient? We deliver tailored schemas:
| Field remapping | Rename fields to match your internal conventions | 24 hours |
| Derived calculations | Territory assignments, engagement scores, decile rankings | 48 hours |
| Deduplication merge | Match against your existing database, flag overlaps | 72 hours |
| Split deliveries | Separate files by region, industry, or segment | 24 hours |
| Encrypted delivery | PGP-encrypted files for sensitive datasets | Standard |
| Direct warehouse load | Managed import to Snowflake/BigQuery/Redshift | 5-7 days |
Product-Specific Samples
Every dataset offered by MediumAxis includes a CSV sample available for immediate download. Visit any product page to preview the exact structure, field completeness, and data quality before purchasing. These samples reflect the actual deliverable format for each specific dataset.
Contact for Custom Samples
Need a sample with specific criteria? Contact us with target industry, required fields, geographic scope, intended software platform, and compliance requirements (GDPR, CCPA, etc.).
Response time: 24 hours standard, 72 hours complex extractions