Every AI project starts with data. And nearly every failed AI project I’ve seen in Australia failed because the data wasn’t ready. Not because the AI was wrong. Not because the team was incompetent. Because the data was messy, fragmented, or missing.

This is the step everyone wants to skip. Don’t.

Why Data Preparation Matters More Than AI Selection

The best AI model in the world can’t compensate for bad data. A mediocre model on excellent data will outperform a brilliant model on poor data every time. That’s not opinion. It’s demonstrated repeatedly in academic research and industry practice.

Yet Australian companies routinely spend weeks evaluating AI vendors and minutes evaluating their own data readiness. The result is predictable: expensive AI tools producing unreliable outputs because the inputs are garbage.

If you take one thing from this article, make it this: invest more time and money in data preparation than in AI tool selection. The ratio should be at least 3:1 in favour of data work for your first AI project.

Step 1: Find All Your Data

You probably have more data than you think, scattered across more systems than anyone knows.

The formal data lives in databases, CRMs, ERPs, and analytics platforms. But there’s informal data everywhere: spreadsheets on individual computers, email threads with important information, documents in shared drives, notes in project management tools.

For AI purposes, all of it matters. Conduct a data inventory that covers every system, formal and informal, where relevant business data lives. This is tedious work. It’s also essential.

For each data source, document: what data it contains, how it’s structured, how frequently it’s updated, who’s responsible for it, and how it connects to other data sources.

Step 2: Assess Data Quality

Once you know where your data is, assess its quality across five dimensions.

Completeness. What percentage of records have all required fields populated? If your customer database has 50,000 records but only 30,000 have email addresses and 20,000 have phone numbers, your AI model that needs contact information is working with less data than you thought.

Accuracy. How much of the data is correct? Old addresses, wrong phone numbers, misclassified products, and incorrect dates are common in Australian business databases. A sample audit of 200-300 records will give you a reasonable estimate of accuracy rates.

Consistency. Is the same information represented the same way across records and systems? “New South Wales,” “NSW,” “N.S.W.,” and “nsw” are all the same thing to a human. To an AI model, they might be four different values. Date formats, name formats, and category labels are common inconsistency culprits.

Timeliness. How recent is the data? If your AI model is trained on data that’s two years old and your business has changed significantly since then, the model’s predictions may not reflect current reality.

Relevance. Does the data you have actually relate to the problem you want AI to solve? If you want to predict customer churn but your data only captures transaction history and not service interactions, complaints, or engagement metrics, you’re missing critical signal.

Step 3: Clean and Standardise

This is where the real work happens, and it’s the step most people underestimate.

Deduplication. Remove duplicate records. This sounds simple but gets complicated quickly. Is “John Smith” at “123 Main St” the same person as “J. Smith” at “123 Main Street”? Probabilistic matching algorithms help, but human review is usually needed for edge cases.

Standardisation. Establish consistent formats for addresses, dates, names, categories, and other fields. Apply those formats across your entire dataset. This is boring, manual-intensive work that’s absolutely critical for AI.

Missing data handling. Decide how to handle missing values. Options include: exclude records with missing data (loses information), fill with averages (introduces bias), fill with predictions (adds complexity), or flag and handle separately (most robust). The right approach depends on your specific situation.

Error correction. Fix known errors identified during your quality assessment. Prioritise corrections that affect the most records or the most critical fields.

Step 4: Connect Your Data

AI works best when it can see relationships between different data sources. Your CRM data alone tells one story. Combined with transaction data, support ticket data, and engagement data, it tells a much richer story.

Building data connections requires mapping relationships between systems: which customer ID in your CRM corresponds to which account in your billing system? Which product codes in your inventory system match which items in your sales records?

Data integration platforms can help, but the mapping work requires human knowledge of your systems. This is another area where existing staff knowledge is invaluable and where documentation is often inadequate.

Step 5: Establish Ongoing Processes

Data preparation isn’t a one-time exercise. Data quality degrades over time as records age, people make errors, and systems evolve.

Establish processes for ongoing data quality management. Regular audits, automated validation rules, clear data entry standards, and assigned data ownership for each major dataset.

Companies that maintain data quality continuously find that AI initiatives are faster to implement and more reliable in production. Companies that do a one-time cleanup and then neglect ongoing quality find themselves back at square one within a year.

The Cost and Timeline

For a mid-market Australian business, a proper data preparation exercise takes four to eight weeks of focused effort and costs between $30,000 and $80,000 in internal and external resources.

That feels expensive until you compare it to the alternative: spending $200,000 on an AI implementation that fails because the data wasn’t ready, then spending $80,000 on data preparation anyway, then spending another $200,000 on a second AI implementation attempt.

Do it right the first time. Your future AI projects will thank you.