What data does a business need to implement predictive AI?

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One of the most common misconceptions about predictive AI is that it requires massive datasets or years of preparation. In practice, most established Australian businesses already have sufficient data to build effective predictive models. The minimum requirements vary by use case, but the threshold is lower than most business owners expect.

**Minimum Data Requirements by Use Case**

Demand forecasting requires 12–24 months of sales transaction data at the SKU level, including date, quantity sold, price, and location. This data is almost always available in any POS system, ERP, or e-commerce platform. Churn prediction requires 6–12 months of customer engagement and billing data — login frequency, feature usage, payment history, and support interactions. Revenue forecasting requires 24 months of monthly revenue data with associated pipeline data from your CRM. Predictive maintenance requires 6–12 months of equipment sensor readings or maintenance log data — either from built-in machine sensors or from manual maintenance records.

**Data Quality Matters More Than Volume**

Data quality matters more than volume. Clean, consistently formatted data with minimal gaps will outperform large but messy datasets. The most common data quality issues PresciaIQ encounters are: inconsistent date formats across systems, missing values in key fields (product category, customer segment, location), duplicate records from multiple data entry points, and data stored in non-machine-readable formats (PDFs, images, handwritten records). PresciaIQ's data engineering team handles all data cleaning and normalisation as part of the implementation process.

**PresciaIQ's Data Readiness Assessment**

PresciaIQ's data readiness assessment evaluates your existing data assets across five dimensions — volume (do you have enough historical records?), velocity (how frequently is data updated?), variety (do you have multiple data sources that can be combined?), veracity (how accurate and consistent is the data?), and value (does the data contain the signals needed for the target predictions?). The assessment is completed in 1–2 weeks and produces a prioritised roadmap identifying which AI use cases are immediately achievable and which require 3–6 months of additional data collection.

**What If You Don't Have Enough Data?**

If your business is less than 12 months old or has significant gaps in historical data, there are still options. External data sources — industry benchmarks, macroeconomic indicators, weather data, demographic data — can supplement limited internal data. Transfer learning techniques allow models trained on similar businesses to be fine-tuned on limited data. And for some use cases, synthetic data generation can augment small datasets to enable model training.

**Can AI Work With Data Stored in Spreadsheets?**

Yes. PresciaIQ regularly works with businesses whose data is stored entirely in Excel or Google Sheets. While spreadsheet data requires more cleaning and normalisation than structured database data, it is entirely viable as a starting point. The data readiness assessment will identify any issues and recommend the most efficient path to model-ready data.

What data does a business need to implement predictive AI?

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