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Corporate Job Margin and Cost Structure Registry
Data Science and Analytics
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About
Evaluating the financial health of specific business tasks allows organisations to optimise resource allocation and maximise return on investment. Job profitability captures the essential intersection of revenue generation and cost structures, providing a quantitative basis for determining which activities contribute most effectively to a company's bottom line. By analysing these financial performance metrics, businesses can make informed decisions regarding job creation, process improvement, and strategic scaling.
Columns
- Job_Number: A unique identifier assigned to each individual job record.
- Jobs_Gross_Margin_Percentage: The calculated percentage representing the gross margin relative to total revenue.
- Jobs_Gross_Margin: The total gross margin value achieved for the specific job.
- Labor_Pay: The direct financial compensation paid for labour associated with the task.
- Labor_Burden: The additional costs associated with employment, such as taxes and insurance benefits.
- Material_Costs: The total expenditure on physical materials required to complete the work.
- PO_Costs: The expenses related to purchase orders generated for the job.
- Labor: The aggregated total of all labour-related expenses.
- Equipment_Costs: The total cost incurred for the utilisation of machinery and tools.
- Jobs_Total: The final aggregated cost or financial value associated with the completed job.
Distribution
The information is delivered in a CSV format titled
job_profitability.csv with a file size of 2.35 MB. It contains 14,500 valid records with zero missing or mismatched entries across a total of 31 columns. The resource maintains a perfect usability score of 10.00 and is provided as a static collection with no scheduled future updates.Usage
This collection is ideal for developing regression models to predict future job profitability based on historical cost features. Analysts can use the data to identify which specific factors—such as material costs or labour burden—have the most significant impact on gross margins. It also serves as an excellent training set for machine learning practitioners to practice feature importance ranking and cost-effectiveness analysis.
Coverage
The data represents 14,500 unique business jobs with high-integrity records across all primary financial fields. While specific geographic metadata is not included, the scope provides a detailed look into the operational finance of a modern enterprise. The demographic focus is centred on the financial outcomes of varied industrial or service-based tasks.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
Financial analysts can leverage these figures to refine profitability reports and resource management strategies. Business owners can use the cost structures to evaluate the return on investment for specific project types. Additionally, data science students can utilise the structured records to practice regression techniques and predictive analytics on real-world financial data.
Dataset Name Suggestions
- Predicting Job Profitability: Financial Performance Index
- Corporate Job Margin and Cost Structure Registry
- Business ROI and Operational Expense Statistics
- Job Profitability Metrics for Predictive Modelling
- Enterprise Financial Analysis: Cost and Margin Database
Attributes
Original Data Source: Corporate Job Margin and Cost Structure Registry
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