What data do I need to implement predictive AI in my Australian business?

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One of the most common concerns Australian businesses have about predictive AI is whether they have sufficient data. The good news is that most businesses have more than enough data for their primary use case — the challenge is typically data quality and accessibility rather than volume.

**Minimum data requirements by use case:**

*Demand forecasting:* 12–24 months of sales/order history at the SKU and location level. Additional signals that improve accuracy: promotional history, pricing history, weather data, local event calendars.

*Equipment failure prediction:* 12–36 months of maintenance records plus sensor data (vibration, temperature, pressure) if available. Failure events are the critical training signal — models need sufficient historical failures to learn from.

*Customer churn prediction:* 12–24 months of customer engagement data (transactions, logins, support contacts, usage patterns) plus churn events. The more granular the engagement data, the more accurate the model.

*Credit risk scoring:* 24–36 months of loan application data with outcomes (default/non-default). The model needs sufficient defaults to learn the patterns that precede them.

**Data quality requirements:**

Data quality matters more than data volume. A model trained on 12 months of clean, consistent data will outperform one trained on 36 months of inconsistent, incomplete data. The minimum quality thresholds are:
- Completeness: 80%+ of records should have values for key fields
- Consistency: field definitions and units should be consistent across the dataset
- Accuracy: the data should reflect actual business events, not data entry errors

**Data accessibility:**

The data needs to be extractable and integrable. Most Australian businesses store their operational data in ERP systems (SAP, Microsoft Dynamics, MYOB), CRM systems (Salesforce, HubSpot), or operational databases. PresciaIQ has pre-built connectors for the most common Australian business systems.

**What if my data isn't perfect?**

No business has perfect data. PresciaIQ's data preparation process includes data quality assessment, cleaning, and enrichment. In most cases, data quality issues can be resolved during the model build phase without delaying deployment.

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