toolathon123/project_20260813_014231_4b7f50d9

Oceania Tech Monthly Efficiency Dataset

Monthly operational efficiency metrics for technology & internet companies across Australia and New Zealand, maintained by the APAC R&D data analytics team based in Sydney.

1. Business Background

The Australia & New Zealand (ANZ) region is home to a fast-growing technology and internet ecosystem spanning SaaS, e-commerce, online education (EdTech), fintech, healthtech, martech, data & AI, logistics tech, cybersecurity, HR tech, climate tech and gaming.

To improve cross-team data sharing and enable data-driven efficiency improvements, our team compiles a monthly operational efficiency dataset capturing how software and product teams convert time and people into delivered work. The dataset aggregates, at the team-month level, the effort invested (headcount, hours logged) and the output delivered (tasks completed, story points, deployments) plus quality signals (incidents).

Note: All company and team names in this dataset are fictional and generated for demonstration / analytics purposes. The metrics are simulated to be realistic in scale and distribution, but do not represent any real organisation.

2. Dataset Contents

Data Scale

• Total records: 6,591
• Companies: 90 (Australia: 54, New Zealand: 36)
• Teams: 507
• Time range: 2025-01 to 2026-01 (13 months)
• Update frequency: Monthly (a new snapshot is published on the 13th of each month, or on the first business day after)

Field Description

Field	Type	Description
record_id	string	Unique identifier of the record (e.g. OCE-000001).
month	string (YYYY-MM)	Calendar month the metrics were recorded for.
region	string	Geographic region: Australia or New Zealand.
company_name	string	Name of the company (fictional).
team_id	string	Unique identifier of the team within the company.
headcount	integer	Number of team members (headcount) during the month.
tasks_completed	integer	Number of tasks completed by the team in the month.
story_points_delivered	integer	Story points delivered by the team in the month (a normalised measure of delivered scope).
hours_logged	integer	Total hours logged by the team in the month.
deployments	integer	Number of production deployments performed by the team in the month.
incident_count	integer	Number of production incidents attributed to the team in the month.
efficiency_score	float (2 dp)	Efficiency score = tasks_completed / hours_logged, rounded to two decimal places. Higher is better (more tasks completed per hour logged).

Efficiency Score

efficiency_score = round(tasks_completed / hours_logged, 2)

This normalised ratio expresses output per unit of effort. It allows teams of different sizes to be compared on a like-for-like basis, and it can be complemented with story_points_delivered / hours_logged or deployments / incident_count for more nuanced analysis.

3. How This Dataset Helps Efficiency Analysis

The dataset is designed to support the following types of analysis for Oceania R&D teams:

• Benchmarking: Compare efficiency_score distributions across regions (Australia vs New Zealand), sectors, and team types to identify realistic target ranges.
• Trend & seasonality analysis: Track month-over-month changes in efficiency, and control for months with different numbers of business days (e.g. holidays in December/January).
• Sizing fairness: Because headcount and hours_logged are captured, analysts can compute per-capita and per-hour productivity rather than relying on raw output counts.
• Quality vs. velocity trade-offs: deployments, incident_count, and story_points_delivered let teams study whether higher delivery speed comes at the cost of reliability.
• Resource planning: Headcount and hours-logged data support capacity forecasting and workload balancing across the ANZ portfolio.
• Continuous improvement: Re-published monthly, the dataset is a reliable, versioned input for dashboards, A/B experiments on process changes, and quarterly efficiency reviews.

4. Files

File	Description
oceania_tech_efficiency_20260813.csv	Monthly efficiency records (UTF-8 encoded, comma-separated, with header row).
README.md	This documentation file.

5. Usage Example

import pandas as pd

df = pd.read_csv(
    "https://huggingface.co/datasets/toolathon123/project_20260813_014231_4b7f50d9/resolve/main/oceania_tech_efficiency_20260813.csv"
)

# Average efficiency by region
print(df.groupby("region")["efficiency_score"].mean())

# Efficiency trend over time
print(df.groupby("month")["efficiency_score"].mean())

6. License & Contact

• License: This simulated dataset is provided for demonstration and analytics purposes only.
• Maintained by: ANZ R&D Data Analytics Team (Sydney).
• Contact: Please raise issues or questions via the Hugging Face dataset discussion tab.