t cht lk
t cht lk
For AI applications, governance extends to ensuring transparency in how data influences outcomes, enabling organisations to trace and explain AI decisions.
Addressing this challenge requires a multi-faceted approach – technical, cultural and strategic. But at the heart of any successful AI strategy lies a fundamental truth: if you want to trust what comes out of AI, you need to trust what goes into AI. In other words, there is no AI strategy without a robust data strategy.
The AI-data nexus
Put simply, AI systems, whether powered by Machine Learning, large language models, or other architectures, are only ever going to be as good as the data they process. Data is the raw material that fuels AI’ s decision-making, predictions and outputs. Poor-quality data – incomplete, inaccurate, biased, or outdated – leads to flawed results, no matter how sophisticated the algorithm. Conversely, high-quality, well-governed data enables AI to deliver reliable, actionable insights.
For CIOs, this means that building trust in AI has to begin with establishing trust in data. A comprehensive data strategy is the foundation for ensuring that AI systems operate on a bedrock of accuracy, consistency and transparency.
Key pillars of a data strategy for AI
A data strategy tailored for AI success rests on several critical pillars:
1. Data quality and integrity
High-quality data is non-negotiable. This means ensuring data is accurate, complete and consistent across sources. For example, if an AI system is used to predict customer behaviour, discrepancies in customer data, such as duplicate records or missing fields, can skew predictions. Implementing robust data cleansing, validation and enrichment processes is essential to maintain integrity at every stage of the data lifecycle.
2. Data governance and compliance
Trust in AI also hinges on trust in how data is managed. A strong governance framework ensures data is collected, stored and processed in compliance with
70 INTELLIGENTCIO EUROPE www. intelligentcio. com