AI in Business: Data Quality, a Crucial Challenge

The Challenge of Trust in Data

The adoption of artificial intelligence (AI) in businesses faces a major obstacle: data quality. According to a recent report, only 12% of organizations believe their data is of sufficient quality and accessibility to effectively support their AI initiatives. This lack of trust is a significant barrier to leveraging AI technologies, despite a rapidly expanding market.

The market for AI products and services is booming, with forecasts reaching between $780 billion and $990 billion by 2027. This rapid growth, estimated at between 40% and 55% per year, does not, however, guarantee success for all companies, which struggle to realize their investments.

With the meteoric rise of generative AI over the past two years, companies are realizing the need to rethink their data strategies to extract real value from their investments. The market is evolving from predictive models to agentic AI systems capable of reasoning, planning, and acting autonomously to achieve a goal. In this context, data becomes the very environment in which these agents operate.

According to the "Data Integrity Trends and Insights" report, 60% of companies now cite AI as a key factor in their data programs, up from just 46% in 2023. This shift underscores the growing importance of AI in the overall strategy of businesses.

The Importance of Data Integrity

For AI to be effective, data integrity is essential. Reliable, consistent, and contextualized data is necessary to fuel high-performing AI models. Companies must integrate critical datasets, establish robust governance processes, and enrich their internal data with third-party sources to maximize their value.

Data integrity is a fundamental prerequisite for the effective use of AI. To achieve this, organizations must integrate critical datasets at the enterprise level, implement strong governance and quality processes, and enrich their internal data with third-party sources to maximize contextual value.

Breaking Down Silos and Governing Data

Access to reliable and accessible data is crucial. Companies must eliminate data silos and improve data quality to avoid biases and errors in analyses. A robust data integration strategy allows for the consolidation of heterogeneous sources, ensuring comprehensive and consistent data for analytical uses and AI models.

The lack of data governance is the main barrier to AI initiatives for 62% of organizations. This situation is explained by the central role of governance in managing data usage: location, traceability, access rights, presence of personally identifiable information (PII), etc. These are all critical elements to ensure data is truly ready for AI.

Thus, strong governance instills trust in an organization's data, ensuring that AI models have the necessary information and that it is used ethically and responsibly. In this framework, data governance naturally becomes the foundation of AI governance.

Improving Data Quality

The effectiveness of AI directly depends on the quality of the data used to train and feed its models. Accurate, consistent, and complete data enables AI to identify patterns, make predictions, and produce relevant insights. Conversely, poor-quality data can lead to biases, hallucinations, or unreliable results.

High-performing AI applications rely on continuous assessment of data quality. To act effectively, AI must rely on accurate and constantly monitored data. This is where data observability comes into play, becoming a fundamental pillar. Companies must prioritize automated monitoring, alerting, and diagnostic mechanisms to detect anomalies, schema drifts, or volume variations, and quickly trace back to the source of problems or trigger corrective actions. Data quality should no longer be a one-time check but a dynamic and ongoing capability.

Without rigorous quality management processes, AI initiatives, particularly autonomous agents, risk relying on incomplete, outdated, or erroneous data, leading to inaccurate and potentially costly strategic decisions.

Leveraging Third-Party Data to Enrich Context

Complete and reliable data is essential for producing quality or trustworthy AI results. However, without context, models remain susceptible to biases and may lack the nuance necessary to deliver reliable outcomes. Data enrichment involves complementing a company's internal data with carefully compiled third-party datasets: geographic data, demographic information, environmental risk factors, etc. This helps increase the diversity of data used to train AI models, highlighting hidden trends that might otherwise go unnoticed, and significantly improves the reliability of the results obtained by AI.

As organizations adopt generative AI, those that rely on robust data foundations (integration, quality, observability, governance, and enrichment) will gain a decisive advantage. By ensuring that AI systems rely on reliable, contextualized, and relevant data, they empower themselves to attract new customers, accelerate their time to market, and reduce compliance risks. In a future marked by autonomous decision-making, having reliable data will constitute a competitive advantage and a growth lever.