Data quality issues are among the leading causes of biased AI models and poor performance outcomes. For AI to deliver on its transformative promise, organisations must first build a solid data foundation. Clean, accurate, and reliable data is the fuel that powers effective, trustworthy AI.

      Neglecting data quality before model build undermines the entire analytics strategy. Poor-quality data erodes trust in insights, leading to flawed decisions and reputational risk. It inflates costs through rework, delays, and increased maintenance, reducing ROI on AI initiatives. 

      Strategically, it creates a fragile foundation-models become less scalable, harder to govern, and non-compliant with regulatory standards. Moreover, it limits the organisation’s ability to operationalise advanced analytics, as unreliable outputs discourage adoption by business stakeholders. Ensuring robust data quality upfront is not just a technical necessity; it’s a strategic enabler value-driven AI transformation.

      This perspective report addresses the persistent ‘garbage-in, garbage-out’ challenge by highlighting emerging trends in data quality across industries such as finance, life sciences, healthcare, and consumer markets. Drawing from experiential insights, it introduces a structured approach for AI enablement built around three key pillars:

      • Strategic anchors: Aligning with purpose and direction of data quality initiatives
      • Operational levers: Answers ‘How’ to operationalise/implement the DQ programs
      • Integration points: Defines ‘Where’ are my critical touchpoint for maximising DQ impact

      By shifting the focus from reactive data fixes to proactive, embracing process-driven quality improvements, organisations can unlock scalable, bias-free AI solutions that drive meaningful business outcomes.

      The ‘DQ Trifecta – Data Quality for AI Success’ cracks the permafrost of the old data quality methodologies to present a more holistic, complete approach to this challenge. The paper does more than suggest changes to existing data quality approaches. It suggests a cultural shift in how organisations should view the curation of arguably one of the most important corporate assets – their data. 


      Key trends on Data Quality imperatives for AI

      • Business-Led, Process-Fed

        Data quality ownership is shifting to business teams who best understand its strategic value and impact. By embedding quality controls into core processes, organisations move from reactive fixes to proactive governance, driving accountability, better decisions, and sustained compliance

      • 360° Integration within Data Teams

        Data quality is evolving into a shared responsibility across data engineering, science, and analytics teams, driving collaborative integration across people, process, and technology. This shift ensures reliable, timely, and trustworthy data, enabling faster, AI-driven decision-making and reducing operational risk

      • Observe, Detect, Act

        The Power of Observability - The shift to Data Quality Observability replaces fragmented checks with end-to-end visibility, enabling teams to monitor, trace, and resolve issues in real time. This integrated approach improves operational efficiency, strengthens AI/ML reliability, and ensures high-integrity data across the lifecycle

      • Semantic Intelligence through metadata quality

        As data ecosystems grow, focus is shifting to metadata quality as a key driver of semantic intelligence and intelligent data operations. By enriching metadata with context, lineage, and semantic tags, organisations enable traceability, automation, and trustworthy AI, turning data into a context-rich asset for innovation and growth

      • The Full Picture: beyond Masters

        Organisations are expanding data quality efforts beyond master data to include transactional and analytical data, recognising its critical role in AI-driven decisions. Ensuring high-quality data across the ecosystem is a strategic imperative for delivering trustworthy insights, resilient AI systems, and sustained competitive advantage

      • Agnostic by Principle, Flexible by Design

        Agile enterprises are adopting a tool-agnostic data quality approach that promotes decentralised ownership, aligns with data mesh and data fabric architectures, and embeds trust across the data ecosystem. This mindset ensures every data product is AI-ready, driving scalable adoption, faster innovation, and sustainable success



      The Data Quality Trifecta

      Emerging trends and data quality imperatives across industries drawn from experiential insights and introduces a structured approach for AI enablement

      The AI Outlook - The role of AI in shaping tomorrow’s world

      Key Contacts

      Sankara T Subramanian
      Sankara T Subramanian

      Partner and COO, Digital Platforms

      KPMG in India

      Ganapathy Subramanian

      Partner, Digital Platforms

      KPMG in India

      How can KPMG in India help

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