Forecast demand, spot anomalies and plan what comes next.

DataNova Predict brings machine learning into your reporting and business applications. Scope a model around your data, business question and success criteria, then evaluate it through an assisted trial.

THE PRACTICAL STARTING POINT

Which decision needs a better forecast?

Start with a defined decision, such as how much stock to buy, where to add capacity or which customers need attention. A useful predictive model needs relevant historical data, a baseline comparison and a clear way to use its results.

Explore custom models and evaluation

Define the decision and forecast horizon.
Evaluate the model against a practical baseline.
Show uncertainty alongside the prediction.

Custom models and data preparation are scoped with you. An assisted trial begins after onboarding and environment setup.

Make forecasts part of the decisions you take every day.

Compare likely demand with available inventory. Explore revenue and cash-flow scenarios. Identify unusual activity or customers at risk of leaving. We build and validate models around the question you need to answer, then put the results where your team can use them.

Key Capabilities

Choose a business question with an action behind it. We assess data readiness, agree how to validate the model and define how predictions will be used.

Forecast Business Performance

Generate forecasts for key metrics such as revenue, demand, expenses, and operational performance. Machine learning models analyse historical patterns to estimate future trends. Machine learning models analyse historical patterns to estimate future trends and provide early signals of change. Common forecasting scenarios include: Revenue forecasting Demand and sales forecasting Expense and cost projections Operational KPI forecasting

Detect Anomalies Automatically

DataNova Predict continuously analyses data patterns to identify unusual activity or unexpected behaviour, such as financial anomalies or cost spikes. This helps organisations detect issues early and investigate potential risks. Examples include: Unusual transactions or financial anomalies Unexpected cost spikes Abnormal operational patterns Irregular customer behaviour

Predict Emerging Risks

Machine learning models can identify signals of potential future problems by analysing patterns in enterprise data. This enables teams to move from reactive reporting to proactive decision making. Examples include: Customer churn risk Declining performance trends Operational risk indicators Early warning signals across business metrics

Monitor and refine your models

Track model performance as new data becomes available. Agree when to retrain and validate changes before relying on new predictions. More data does not automatically mean a more accurate model.

Built On The DataNova Platform

Machine learning on your connected data.

DataNova Predict uses prepared data in your Fabric lakehouse. Model results can feed the reporting and semantic models used across DataNova, so teams can compare predictions with actuals and inspect the same business definitions.

The workflow includes:

Data ingestion and preparation within the DataNova Lakehouse

Machine learning models trained on governed enterprise data

Predictions written back into the platform

Microsoft Fabric

Explore predictions with Nova AI Analyst.

Once the model results are available in your governed data, explore questions such as:

From Reporting to Prediction

Traditional analytics answers the question:

The workflow includes:

What happened?

DataNova Predict helps organisations answer the next question:

What will happen next?

By combining machine learning with governed enterprise data, organisations can move beyond static reporting and towards predictive decision intelligence.

Ask Predictive Questions Using AI

Choose the prediction that matters to your business.

Apply predictive analytics to the business question that matters. Agree the data, validation and action for each use case.

FP&A IN UNDER A MINUTE

A predictive forecast and an evidence-based AI briefing

Compare a machine learning outlook with the business plan, then explore an executive briefing linked to supporting figures.

A DataNova FP&A demonstration using illustrative figures. Your application, data connections and setup are agreed with you.

Discuss an assisted trial

Watch the full FP&A walkthrough

Read the transcript (automatic)

What does the historical pattern suggest? Independently of management's assumptions. Predictive forecast adds a machine learning outlook, tested against past results in a seasonal baseline. Review its coverage and measured error alongside the business plan. It's another perspective to evaluate with your team keeping control of the decision. The CFO briefing turns calculated evidence into a focused executive discussion. AI-generated commentary groups the findings into attention required, positive performance, and monitor with supporting figures and links to the evidence. Leaders can see what deserves attention, then investigate further.

Open video and transcript

The workflow includes:

Predictions built on trusted, governed data

AI-assisted exploration of predictive insights

Reduced complexity compared to standalone ML platforms

Models monitored against agreed success criteria

Why DataNova Predict

Organisations choose DataNova Predict because it combines machine learning with a governed enterprise analytics platform.

Put a real business question to the test.

Tell us what you want to forecast or detect, which data you hold and what decision the result will support. We will scope the model, validation and assisted trial with you.