Data is one of the most important inputs for modern business systems, including AI.
You may already rely on data created or collected through daily tasks, such as:
AI can help you get more value from your data by turning it into insights that support better workflows and business growth.
AI systems can analyse large amounts of information quickly. This allows you to use data for tasks that would otherwise take a lot of time to do manually.
Data can be structured (such as records in a system or spreadsheet) or unstructured (such as emails and documents).
Regardless of format, good data management is important. It helps you:
Even with AI and good data, you still need to place people at the centre.
An AI system depends on the information you give it. If data is incomplete, outdated or inconsistent, the results will reflect those weaknesses.
Poor data quality can lead to:
The 1-10-100 rule, first developed by Labovitz et al. (1992), highlights why preventing data issues early costs far less than fixing problems later.
It costs relatively little to prevent data issues at the start. In practice, this means checking that data is accurate, complete and structured before you use it in an AI system.
It costs around 10 times more to fix problems once poor-quality data is already in the AI system. In practice, this means extra time spent on cleaning data, correcting mistakes or re-running processes.
It costs around 100 times more when an AI system is trained on bad data. In practice, this is when an AI tool produces biased or inaccurate outputs, or fails when it matters most. It often damages trust and can lead to serious consequences.
A small clothing retailer uses an AI tool to answer customer questions and manage stock across its online and in-store systems.
Product and inventory data aren’t consistent across these systems. Because of this, the AI gives wrong information about what's in stock and customers receive conflicting messages. Staff end up checking real stock in store and at their warehouse.
Over time, trust in the system and confidence from customers drop. Even though AI could save time and effort, poor data quality prevents it from providing reliable value and impacts business revenue. Depending on the information provided to the customer, the business may be at risk of misleading and deceptive conduct under Australian Consumer Law.
Organisations that treat data as a core asset are better positioned to use AI effectively.
You don’t need complex systems or large teams. Start by:
When you manage data with care, AI systems are more reliable, easier to govern and more likely to deliver useful outcomes.
Use our checklist and learn more about data security.
Labovitz, G., Chang, Y. S. & Rosansky, V. (1992). Making Quality Work: A Leadership Guide for the Results‑Driven Manager. New York: HarperBusiness.