---
license: other
language:
  - en
tags:
  - oceania
  - australia
  - new-zealand
  - efficiency
  - software-development
  - productivity
  - saas
  - fintech
  - e-commerce
  - edtech
size_categories:
  - 1K<n<10K
pretty_name: Oceania Tech Monthly Efficiency Dataset
---

# 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

```python
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.