Your AI’s Weakest Link: Bad Data

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Businesses increasingly recognize data as their most vital asset, yet many struggle to translate information into reliable insights. A recent comprehensive study surveying thousands of data leaders reveals that while 63% of organizations now describe themselves as highly data-driven, a significant confidence gap persists. Nearly two-thirds of technical leaders admit their companies face difficulties using data to drive core business priorities effectively.

The swift ascent of artificial intelligence agents has injected a new sense of urgency across global industries. Companies are racing to transform into what experts call ‘agentic enterprises,’ leveraging AI to fuel accelerated growth. This transition demands a fundamental overhaul of existing data infrastructure, management protocols, and governance frameworks. For many organizations, this evolution is not merely about competitive advantage but about ensuring long-term survival in a rapidly shifting marketplace. Success hinges on democratizing access to data, analytics, and AI tools, making these powerful resources available to every employee.

Key findings from the research highlight several critical areas of focus and challenge.

The AI revolution is fundamentally reshaping data strategies. With the vast majority of companies now integrating at least one form of AI into daily operations, leaders are scrutinizing the readiness of their foundational data systems. As data volumes explode and complexity intensifies, the underlying culture and infrastructure are being tested. This acceleration brings both immense opportunity and significant pressure to adapt quickly.

A pervasive lack of trust in data quality severely hampers decision-making and action. As organizations lean more heavily on data, many business leaders feel bogged down by slow, technically complex processes for generating analytical insights. Compounding this issue, half of all business leaders express uncertainty about their ability to produce and deliver timely insights. The root cause often lies in doubts about the fundamental accuracy and reliability of the data itself.

Building a robust data foundation for advanced analytics and AI presents formidable technical hurdles. Data and analytics leaders are caught between rising demand from business units for data-driven capabilities and executive pressure for AI-driven innovation. Outdated data management practices, particularly around integration and harmonization, create major bottlenecks. A striking 70% of these leaders report that their organizations’ most valuable insights remain locked away within unstructured data formats like documents, emails, and images.

The proliferation of AI is exposing critical weaknesses in data governance and security. Long-standing gaps in compliance, security, and formal governance are coming to light. Alarmingly, only 43% of data and analytics leaders have established formal data governance frameworks. An overwhelming 88% agree that the age of AI demands entirely new approaches to safeguarding information.

Delving deeper into the first two insights reveals a landscape of both ambition and anxiety.

The AI-driven data paradigm is creating a new imperative for data fluency. An overwhelming 90% of business leaders now believe their career success is directly tied to their proficiency with data. Another 86% see being data-driven as essential for professional survival. However, a clear disconnect exists between aspiration and reality. While businesses report heavier data usage, 63% of technical leaders concede their organizations struggle to align data with business priorities. Data leaders estimate that over a quarter of their corporate data is considered “untrustworthy,” and 42% of business leaders admit their data strategies are not fully synchronized with business goals.

The fundamental truth is that all AI initiatives are, at their core, data projects. An overwhelming 93% of organizations have integrated AI into their technology ecosystems. This rapid adoption is forcing data leaders to accelerate their capabilities. AI is widely seen as a catalyst for improving overall data literacy, with 91% of business leaders convinced that AI makes a data-driven approach more critical than ever.

Investment patterns reflect this new reality. Chief Information Officers are allocating budgets accordingly, spending four times more on data infrastructure than on AI technology itself. This strategic focus is likely driven by the understanding that 80-90% of enterprise data exists in unstructured forms, and 70% of leaders believe the most valuable insights are trapped within it. We are swimming in an ocean of data yet remain thirsty for actionable intelligence.

The average enterprise manages a sprawling data environment, including 26 different spreadsheet applications, 21 cloud storage services, and 21 operational databases. With companies using an average of 897 applications, only 29% of which are interconnected, it’s no wonder that data trust is low. More than half of leaders (54%) lack full confidence that the data they need is even accessible, and data leaders estimate 19% of corporate data is completely trapped within silos.

Current data priorities reflect these challenges, with top focuses being building AI capabilities, enabling real-time data access, improving company-wide data fluency, enhancing data quality, and strengthening security. The corresponding top challenges include a lack of real-time data, insufficient data harmonization, security threats, ensuring accuracy, and dealing with siloed information. The demand for real-time data has surged dramatically, now ranking as the primary data challenge.

Limited confidence in data integrity remains a massive barrier to progress. Whether data powers an AI prediction, an automated customer interaction, or a strategic report, its value is contingent on being grounded in trustworthy business context. A full 93% of business leaders agree that insights are only relevant when they are contextually grounded. The primary obstacles preventing organizations from becoming truly data-driven include incomplete or poor-quality data, a lack of tools for analysis, insufficient staff expertise, slow insight generation, and restricted data access.

These challenges have direct consequences, with 49% of data leaders reporting their companies occasionally or frequently draw incorrect conclusions from data that lacks proper business context. The growing adoption of AI is changing how businesses interact with data. A vast majority of leaders (91%) say technical queries limit analytics at scale, and 92% cite a lack of data fluency among staff. In response, 64% of business leaders now use AI to find, analyze, and interpret data, surpassing the 54% who still rely on technical specialists.

As AI adoption grows, so do expectations. Buyers of analytics solutions now prioritize AI capabilities and real-time data access. A significant 88% of data leaders report that AI advancements are changing how they evaluate analytics software. The most sought-after features include real-time data, AI-assisted workflows, automated AI-driven actions, composable analytics, and scalable insights. An impressive 94% of business leaders believe they would perform better with direct data access within their primary work applications.

The future points toward conversational interaction with data through AI agents. These systems, which can understand, respond to, and take action based on natural language inquiries, hold tremendous potential. This “agentic analytics” approach makes data consumption intuitive and conversational. The demand is clear: 63% of data leaders say translating business questions into technical queries is error-prone, and 93% of business leaders believe they would perform better if they could simply ask data questions in plain language.

(Source: ZDNET)