INDUSTRY VIEW
WHY AI FAILS WITHOUT CLEAN DATA
ANDY MACMILLAN, CEO, ALTERYX
As Artificial Intelligence continues to reshape the business landscape, data quality has emerged as the deciding factor between success and failure for enterprise AI initiatives. Andy MacMillan, CEO, Alteryx, tells us how channel partners can help organisations unlock AI’ s full potential by prioritising trusted, transparent and well-governed data foundations.
AI has taken centre stage as one of the most powerful tools in business, enabling enterprises to streamline processes, gain valuable insights and innovate like never before. In a recent Alteryx study, 94 % of data analysts said their role now directly impacts strategic decisions, and 87 % noted increased influence over business outcomes.
Moreover, 90 % of data professionals in the Middle East report that AI has already transformed their work. Yet, despite its enormous potential, AI frequently underperforms or outright fails. The culprit often lies in one overlooked but foundational element – data quality. AI, no matter how advanced, can only be as good as the data it’ s fed. Imagine building a skyscraper on a crumbling foundation. The result is predictable. Poor data quality undermines AI efforts, making trust, governance and transparency non-negotiable pillars for success.
Let’ s dive into how bad data sabotages AI, explain the critical role of governance, and introduce a revolutionary approach to solving these challenges with trusted, AIready data.
The Data Quality Crisis in AI
AI relies on massive quantities of data to learn, predict outcomes and make decisions. However, Gartner has reported a significant obstacle in the AI adoption wave: 60 % of AI initiatives will be abandoned through 2026 due to poor data quality.
Organisations can struggle with data quality due to incomplete datasets, biases, duplicate records and outdated information. These problems trickle down into AI models, leading to poor predictions, incorrect insights, and ultimately, lost trust in AI systems.
For example, bias in AI predictions occurs when training data favours certain demographics, skewing outcomes in critical areas like hiring, lending or healthcare. Incomplete information causes AI systems to compensate for gaps, often drawing inaccurate conclusions. Legal and regulatory risks also arise when personal data is mishandled, potentially violating GDPR or other data protection laws.
Considering that 65 % of UAE IT leaders accelerated AI implementation over the past two years, the consequences are more than just technical failures; they translate into reputational damage, wasted resources and critical missed opportunities.
Why Trust, Governance and Transparency Matter
AI systems thrive on trust. For AI to yield actionable outcomes, stakeholders must have confidence in the data that feeds the system. This is where governance and transparency become essential.
Trust Built on Governance
Governance ensures that every stage of the data pipeline, from collection to processing, adheres to policies and best practices. This includes anonymising personally identifiable information( PII), securing compliance with regulations like GDPR, and monitoring for potential ethical concerns. Without governance, businesses risk turning AI systems into liabilities rather than assets.
Data governance solutions enable auditable workflows, empowering compliance and legal teams to oversee data before it goes into AI systems. This ensures data integrity without paralysing innovation.
Transparency Across Workflows
Transparency is not a bonus feature; it’ s a necessity. When organisations can track every step a dataset has taken, they gain the insight required to fine-tune AI systems and address errors quickly. By implementing AI Data Clearinghouses like those powered by the
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