For many businesses, the success of AI initiatives hinges on one critical factor: data readiness. AI thrives on clean, structured, and accessible data. Without it, even the most advanced algorithms will falter. Before diving into a large AI implementation, it is important to carefully consider your data infrastructure to ensure it aligns with your business aims. Poor data quality can sabotage your efforts, leading to inaccurate insights and wasted resources. Let’s break down how to get your data house in order.

First, assess your data availability. Do you have enough historical data to use within your models effectively? For instance, if your business aims to predict client project timelines using AI, you’ll need comprehensive records of past projects—dates, durations, and outcomes. Check whether this data is centralized or scattered across systems like CRMs, ERPs, or random spreadsheets. Fragmented data creates bottlenecks, forcing your team to spend more time gathering than analysing. A centralised data repository is can help to streamline access and ensuring your AI can draw from a complete dataset.

Next, tackle data quality. Issues like duplicates, missing values, or inconsistencies can derail AI models, leading to unreliable outputs. Imagine training an AI to optimize client onboarding, but your dataset has duplicate entries for the same client or missing contact details. The result? Flawed predictions and frustrated customers. Quality matters as much as quantity. AI isn’t magic – it amplifies what you feed it. If your data is messy, your outcomes will be too. A thorough audit can reveal these gaps, setting the stage for meaningful improvements.

In order to help structure your activities think about utilising an industry model like the Data Management Capability Assessment Model (DCAM) to produce a current state analysis. DCAM helps you evaluate your data maturity across dimensions like governance, quality, and architecture. From there, define your desired future state—say, a fully centralised, compliant data system—and build a roadmap to get there. Audit your data sources (CRM, ERP, spreadsheets) to identify gaps. Clean your data using tools some of the available toolsets or hire a data specialist to tackle more complex issues. Centralise your data with a platform such as Snowflake. Finally, implement data governance policies to ensure ongoing compliance and security, such as access controls and regular audits.

Don’t overlook privacy and compliance, especially with your EU clients. Regulations like GDPR (for EU clients) and CCPA (for California) impose strict rules on data handling. Non-compliance can lead to hefty fines—GDPR violations can cost up to €20 million or 4% of annual global turnover, whichever is higher. Ensure your data practices meet these standards by anonymizing sensitive information, securing consent, and documenting processes. This isn’t just about avoiding penalties; it builds trust with clients who expect responsible data stewardship.

Here’s the critical takeaway: poor data quality leads to “garbage in, garbage out.” A 2023 IBM study found that 80% of AI project delays stem from data issues, underscoring the need for preparation. Investing time upfront to assess and improve your data readiness isn’t just a precaution—it’s a competitive edge. For many businesses, getting this right means your AI solutions will deliver real value, not just empty promises.