Data Extraction Specialist for Australian Business Statistics
Budget: $30 – $250 AUD
## Project Overview
We're building a market sizing tool that helps SaaS companies accurately identify and quantify their potential customer base in Australia. Your task is to collect, organize, and structure comprehensive business demographic data from the Australian Bureau of Statistics (ABS) into our Airtable database. This data will map out the entire landscape of potential SaaS buyers across all Australian industries, company sizes, and locations.
## Freelancer Requirements for ABS Data Collection:
### Technical Skills:
- Excel data manipulation (advanced)
- Experience with data extraction and transformation
- Airtable knowledge (at least basic)
- Understanding of database relationships
- Ability to work with statistical datasets
## Key Concept: Focus on SaaS Buyers
This project focuses on collecting data about potential buyers of SaaS products across ALL industries. Every business in Australia represents a potential customer for some type of SaaS solution, whether they're in retail, healthcare, financial services, or technology.
## Deliverables
1. Complete Airtable database with Australian business demographic data organized according to our structure (we will give edit access to the database on Airtable)
2. Detailed documentation of all data sources, collection methodology, and ABS reference codes
3. Notes on any data limitations, gaps, or estimation methods used
## Data Sources
Primary source: Australian Bureau of Statistics (ABS) - "Counts of Australian Businesses, including Entries and Exits"
- Latest release available at: https://www.abs.gov.au/statistics/economy/business-indicators/counts-australian-businesses-including-entries-and-exits
- Download ALL Excel data tables from the "Downloads" section
- Also reference the "Australian and New Zealand Standard Industrial Classification (ANZSIC)" documentation for code details
## Data Requirements
**IMPORTANT NOTE ON BUSINESS COUNT DATA:**
Focus ONLY on currently active businesses as of the latest available date (likely June 2023 or 2024). Do not include businesses that have exited/closed during the reporting period. The ABS report contains multiple tables - ensure you're using the ones that represent "actively trading businesses" or "counts of businesses" rather than the entry/exit statistics.
### 1. Industry Classification
Collect data at the most detailed ANZSIC level available:
- Division level (letters A-S) as the minimum baseline
- Subdivision level (2-digit codes) required for ALL divisions
- Group level (3-digit codes) required where available
- Class level (4-digit codes) required where available
Create a complete ANZSIC hierarchy from Division → Subdivision → Group → Class, even if business counts are only available at higher levels.
### 2. Company Size
Collect complete employee size breakdowns:
- 0 (”non-employing” - aka freelance / self-employed)
- 1-4 employees
- 5-19 employees
- 20-199 employees
- 200+ employees
**Important:** Check if any ABS tables provide further breakdowns of the 200+ category, such as:
- 200-499 employees
- 500-999 employees
- 1000+ employees
### 3. Geographic Location
Collect location data at multiple levels:
- National totals
- State/Territory breakdown
- Greater Capital City Statistical Areas where available
- Regional breakdowns if available
### 4. Annual Turnover
Include all available turnover band data:
- $0 to less than $50k
- $50k to less than $200k
- $200k to less than $2m
- $2m to less than $5m
- $5m to less than $10m
- $10m or more
### 5. Business Dynamics (if available)
Include data on:
- Business survival rates by industry and size
- Entry/exit rates by industry
## Database Structure (Airtable)
Set up the following tables with appropriate relationships:
1. **ANZSIC_Hierarchy**
- division_code (A-S)
- division_name
- subdivision_code (2-digit)
- subdivision_name
- group_code (3-digit)
- group_name
- class_code (4-digit)
- class_name
2. **Business_Counts**
- anzsic_code (linked to most granular level in ANZSIC_Hierarchy)
- state_territory (linked to Locations table)
- size_range (linked to Size_Ranges table)
- turnover_range (linked to Turnover_Ranges table)
- count (number of businesses)
- year (of data collection)
- data_source_table (reference to specific ABS table)
3. **Locations**
- location_code
- location_name
- location_type (State/Territory, Capital City, Region)
- parent_location (for hierarchical organization)
4. **Size_Ranges**
- range_id
- range_name
- min_employees
- max_employees
5. **Turnover_Ranges**
- range_id
- range_description
- min_turnover
- max_turnover
## Cross-Tabulation Requirements
Capture as many cross-tabulations as possible:
- Industry × Size
- Industry × Location
- Industry × Turnover
- Industry × Size × Location (if available)
If certain cross-tabulations aren't directly provided by ABS, document this limitation explicitly.
## All Industries Are Important
While technology sectors are obviously relevant, ALL industries represent potential SaaS buyers:
- Retail businesses (Division G) are potential customers for retail management SaaS
- Healthcare providers (Division Q) are potential customers for healthcare SaaS
- Construction companies (Division E) are potential customers for project management SaaS
- Financial institutions (Division K) are potential customers for fintech SaaS
Ensure comprehensive coverage across all industry divisions, as each represents distinct potential buyer markets for different types of SaaS products.
## Data Quality Assurance
For each data point, please:
1. Note the specific ABS table source
2. Record the publication date
3. Document any discrepancies between different ABS tables
4. Flag any instances where you had to estimate or interpolate data
5. Verify totals match across different breakdowns
## Timeline
- Deadline: Sunday 23rd Feb at 12:00pm AEST.
This comprehensive data collection will form the foundation of our Australian SaaS Market Sizing Tool, enabling SaaS companies to identify and quantify their specific target markets across the entire Australian business landscape.
Look forward to hearing how you can help.
We're building a market sizing tool that helps SaaS companies accurately identify and quantify their potential customer base in Australia. Your task is to collect, organize, and structure comprehensive business demographic data from the Australian Bureau of Statistics (ABS) into our Airtable database. This data will map out the entire landscape of potential SaaS buyers across all Australian industries, company sizes, and locations.
## Freelancer Requirements for ABS Data Collection:
### Technical Skills:
- Excel data manipulation (advanced)
- Experience with data extraction and transformation
- Airtable knowledge (at least basic)
- Understanding of database relationships
- Ability to work with statistical datasets
## Key Concept: Focus on SaaS Buyers
This project focuses on collecting data about potential buyers of SaaS products across ALL industries. Every business in Australia represents a potential customer for some type of SaaS solution, whether they're in retail, healthcare, financial services, or technology.
## Deliverables
1. Complete Airtable database with Australian business demographic data organized according to our structure (we will give edit access to the database on Airtable)
2. Detailed documentation of all data sources, collection methodology, and ABS reference codes
3. Notes on any data limitations, gaps, or estimation methods used
## Data Sources
Primary source: Australian Bureau of Statistics (ABS) - "Counts of Australian Businesses, including Entries and Exits"
- Latest release available at: https://www.abs.gov.au/statistics/economy/business-indicators/counts-australian-businesses-including-entries-and-exits
- Download ALL Excel data tables from the "Downloads" section
- Also reference the "Australian and New Zealand Standard Industrial Classification (ANZSIC)" documentation for code details
## Data Requirements
**IMPORTANT NOTE ON BUSINESS COUNT DATA:**
Focus ONLY on currently active businesses as of the latest available date (likely June 2023 or 2024). Do not include businesses that have exited/closed during the reporting period. The ABS report contains multiple tables - ensure you're using the ones that represent "actively trading businesses" or "counts of businesses" rather than the entry/exit statistics.
### 1. Industry Classification
Collect data at the most detailed ANZSIC level available:
- Division level (letters A-S) as the minimum baseline
- Subdivision level (2-digit codes) required for ALL divisions
- Group level (3-digit codes) required where available
- Class level (4-digit codes) required where available
Create a complete ANZSIC hierarchy from Division → Subdivision → Group → Class, even if business counts are only available at higher levels.
### 2. Company Size
Collect complete employee size breakdowns:
- 0 (”non-employing” - aka freelance / self-employed)
- 1-4 employees
- 5-19 employees
- 20-199 employees
- 200+ employees
**Important:** Check if any ABS tables provide further breakdowns of the 200+ category, such as:
- 200-499 employees
- 500-999 employees
- 1000+ employees
### 3. Geographic Location
Collect location data at multiple levels:
- National totals
- State/Territory breakdown
- Greater Capital City Statistical Areas where available
- Regional breakdowns if available
### 4. Annual Turnover
Include all available turnover band data:
- $0 to less than $50k
- $50k to less than $200k
- $200k to less than $2m
- $2m to less than $5m
- $5m to less than $10m
- $10m or more
### 5. Business Dynamics (if available)
Include data on:
- Business survival rates by industry and size
- Entry/exit rates by industry
## Database Structure (Airtable)
Set up the following tables with appropriate relationships:
1. **ANZSIC_Hierarchy**
- division_code (A-S)
- division_name
- subdivision_code (2-digit)
- subdivision_name
- group_code (3-digit)
- group_name
- class_code (4-digit)
- class_name
2. **Business_Counts**
- anzsic_code (linked to most granular level in ANZSIC_Hierarchy)
- state_territory (linked to Locations table)
- size_range (linked to Size_Ranges table)
- turnover_range (linked to Turnover_Ranges table)
- count (number of businesses)
- year (of data collection)
- data_source_table (reference to specific ABS table)
3. **Locations**
- location_code
- location_name
- location_type (State/Territory, Capital City, Region)
- parent_location (for hierarchical organization)
4. **Size_Ranges**
- range_id
- range_name
- min_employees
- max_employees
5. **Turnover_Ranges**
- range_id
- range_description
- min_turnover
- max_turnover
## Cross-Tabulation Requirements
Capture as many cross-tabulations as possible:
- Industry × Size
- Industry × Location
- Industry × Turnover
- Industry × Size × Location (if available)
If certain cross-tabulations aren't directly provided by ABS, document this limitation explicitly.
## All Industries Are Important
While technology sectors are obviously relevant, ALL industries represent potential SaaS buyers:
- Retail businesses (Division G) are potential customers for retail management SaaS
- Healthcare providers (Division Q) are potential customers for healthcare SaaS
- Construction companies (Division E) are potential customers for project management SaaS
- Financial institutions (Division K) are potential customers for fintech SaaS
Ensure comprehensive coverage across all industry divisions, as each represents distinct potential buyer markets for different types of SaaS products.
## Data Quality Assurance
For each data point, please:
1. Note the specific ABS table source
2. Record the publication date
3. Document any discrepancies between different ABS tables
4. Flag any instances where you had to estimate or interpolate data
5. Verify totals match across different breakdowns
## Timeline
- Deadline: Sunday 23rd Feb at 12:00pm AEST.
This comprehensive data collection will form the foundation of our Australian SaaS Market Sizing Tool, enabling SaaS companies to identify and quantify their specific target markets across the entire Australian business landscape.
Look forward to hearing how you can help.