Capability

Analytics & Machine Learning

Turn data into decisions - dashboards and KPI frameworks for now, machine learning for what is next.

RUBIX Analytics & Machine Learning illustration
What it is

From what happened to what happens next.

Analytics and machine learning turn your data into decisions. Analytics - dashboards, KPI frameworks and reporting - shows leaders what is happening now and why. Machine learning adds prediction: forecasting demand, scoring risk, spotting anomalies and anticipating what is likely to happen next.

Together they move a business from hindsight to foresight. RUBIX starts from the decisions you need to make, builds trusted analytics on a governed foundation, and adds predictive models only where they earn their place.

In short: Analytics and machine learning turn data into decisions: dashboards and KPIs for what is happening now, and predictive models for what is likely next. RUBIX builds both on a governed foundation, starting from the decisions that matter.

Why it matters

Why analytics and ML together.

Hindsight to foresight

Analytics explains what happened; machine learning anticipates what is next. Together they change how you plan.

Decisions, not dashboards

We start from the decisions leaders need to make, so the output drives action rather than decoration.

Value where it pays

We add machine learning only where it earns its place, so effort goes to models that move the business.

How RUBIX does this

How RUBIX delivers insight.

1

Define the decisions

We agree the decisions and KPIs that matter, so analytics is built around real questions.

2

Build trusted analytics

We build dashboards and reporting on a governed foundation, with agreed definitions people trust.

3

Add predictive ML

Where it pays, we build and validate machine learning models - forecasting, scoring, anomaly detection.

4

Operationalise

We put models and dashboards into daily use and monitor them, so insight stays accurate over time.

Case study

Forecasting demand for a logistics company.

Case study

A logistics company

Transport & logistics · Australia

The challenge

Operations were reactive with no reliable demand forecasting, leading to stockouts, wasted capacity and firefighting.

What RUBIX did

RUBIX built executive and operational dashboards on a governed data foundation and a demand-forecasting machine learning model integrated into planning.

92%Forecast accuracy
28%Fewer stockouts
1Trusted view for ops
FAQ

Frequently asked questions.

What is the difference between analytics and machine learning?

Analytics describes what has happened and why, through dashboards and KPIs. Machine learning predicts what is likely to happen next - forecasting, scoring and anomaly detection. RUBIX uses both together.

Do we need machine learning, or just good dashboards?

Often good, trusted analytics is the highest-value first step. RUBIX adds machine learning only where prediction clearly pays off, so you do not over-engineer.

What do we need in place before ML?

Reliable, governed data. Models are only as good as the data behind them, so RUBIX ensures the foundation is trustworthy before building predictive models on top.

How do you keep models accurate over time?

We operationalise and monitor them, watching for drift and data-quality issues, and retune as the business changes - so insight stays reliable rather than going stale.

Explore more

See what is happening now - and what is next.

Tell us the decisions you wish you could make with confidence. We will build the analytics and models that get you there.

Talk to us today