Why AI is only as good as your data

Dubai: Data is the foundation of modern business, but its value depends on one critical factor: trust. As AI becomes more capable and autonomous, organisations face new questions around accuracy, governance, accountability and risk. Reece Clifford, Public Sector Pre-Sales Manager for the Middle East, Turkey and Africa at SAS, shares his views on the evolving relationship between data, AI and business decision-making, and why strong governance is becoming a competitive advantage rather than a compliance exercise. Data accuracy, trust and reliability How accurate is the data organisations rely on for critical business decisions today? What are the biggest threats to data quality and reliability? How do you define data accuracy in a modern enterprise? Reece Clifford: Before answering how accurate data is, I want to emphasise how important and fragile trust in data and AI is within an organisation. Trust is the reason we worry about data accuracy and is what we try to preserve through our Data Quality and Data Governance programmes. With the introduction of Agentic AI, trust becomes even more critical because it lowers the barrier to entry for many specialist tasks or areas of knowledge that users did not previously possess. When using different providers, a single incorrect answer can quickly erode trust. To answer your question, data accuracy depends on the specific use case. Does a credit decisioning model have accurate, complete, timely, relevant and traceable data? Most probably, yes. However, with the introduction of Agentic AI and its ability to contextualise vast amounts of data, overall data accuracy across the enterprise is not as high. Many organisations have improved their ability to collect vast amounts of data, but the core challenge is ensuring it remains reliable. The major threats to data quality are data silos, inconsistent definitions, incomplete records and, increasingly, poor-quality AI-generated content entering business workflows. What misconceptions do business leaders have about data quality and reliability? Reece Clifford: Analytics and AI only amplify the quality of the data on which they are built. It is a principle known since the inception of computer science: garbage in, garbage out. The primary misconception is that data quality is purely an IT issue. It is not. Data quality is a business-led issue because it directly affects customer experience, risk management, forecasting and strategic planning. It requires collaboration between business teams responsible for definitions and usage, and IT teams responsible for data values and storage. Leaders also frequently overestimate the maturity of their data foundations. A SAS report developed with research insights from IDC found that many organisations are still developing the infrastructure needed to support explainability, validation and governance at scale, particularly as AI becomes more autonomous. How easy is it to manipulate data, intentionally or unintentionally? To what extent does human bias influence data? Reece Clifford : Humans influence every stage of the data lifecycle: what data to collect, which variables matter, how results are interpreted and what actions are taken. Most data issues are not malicious; they are usually the result of inconsistent processes, sampling errors or unconscious bias, compounded by the increasing volume and complexity of data. Because decision-making requires explainability and transparency to maintain trust by reducing and ultimately removing manipulation or bias, investing in specialists remains a priority for organisations. The concept of complete automation is closer through the introduction of Agentic AI, but we are not there yet. Have you seen organisations make major decisions based on flawed data? What lessons were learned? Reece Clifford: Every organisation has experienced some form of a data quality issue, and numerous examples are publicly documented. Overall, given the rapid pace of innovation in data insights, the most important lesson is not to skip guardrails and security measures. This is especially vital when a system or output is externally facing. Data quality checks and governance policies are essential components of these protective measures. Data verification and governance What frameworks and controls should organisations implement to verify data authenticity and maintain trust? Reece Clifford: The best results are achieved when people, processes and technology are combined into a single framework. At a practical level, that requires strong data lineage, quality monitoring, audit trails, access controls, clear business definitions and defined ownership. Governance works best when embedded into day-to-day decision-making rather than managed as a standalone compliance exercise. The most advanced approaches allow in-production issues and errors to be identified and rectified before becoming problems because the underlying processes are well defined. What warning signs sugg