INDUSTRY VIEW
Alteryx One platform, businesses achieve unparalleled visibility into what data is being used, how it’ s been processed and whether it complies with regulations.
Transparency also supports accountability. When an AI system makes a decision, enterprises can trace back and audit the data to understand if and how it contributed to the outcome, minimising risks and fostering trust.
How Poor Data Undermines AI Across Business Functions
The ripple effect of poor-quality data is felt across various business applications where AI is gaining traction. For instance, AI in customer service relies heavily on historical customer data to recommend solutions, respond to queries and predict needs. When datasets are corrupted, incomplete, or biased, AI-powered systems can misinterpret requests and deliver answers that are irrelevant or even offensive. This erodes customer trust and undermines the value of automated support tools.
AI’ s superpower is finding patterns in massive datasets. Poorquality data diminishes this capability, leading to spurious correlations, inaccurate forecasts, and ultimately, misinformed decisions. Business leaders who rely on these outcomes may steer strategy in the wrong direction, often without realising it until it’ s too late.
AI, no matter how advanced, can only be as good as the data it’ s fed.
In supply chain operations, AI is used to forecast demand, optimise inventory, and streamline logistics. However, with faulty data, companies may end up with excessive stock that ties up capital or shortages that damage service levels – both of which eat into margins and disrupt customer relationships.
How do businesses overcome these data challenges? Enter the AI Data Clearinghouse, a data-first approach that ensures
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