The Key to Reaching GenAI’s Full Potential: Data Quality

Generative AI (GenAI) promises to revolutionize industries—from automating business processes and improving decision-making to driving innovation at never-before-seen speeds. However, behind every successful GenAI model lies a foundational truth: GenAI is only as good as the data that fuels it.

When data is incomplete, inconsistent, or inaccurate, even the most advanced GenAI tools will deliver flawed results. As a result, businesses risk poor decisions, operational inefficiencies, compliance failures, and reputational damage.

For organizations looking to optimize and scale GenAI outcomes, ensuring high-quality data isn’t just a technical requirement—it’s a strategic imperative. Like other data-driven use cases, GenAI requires trusted data that gives you full confidence in the results.

Data Quality Matters

Organizations racing to adopt GenAI are realizing that data quality can make or break their investment. High-performing GenAI models rely on large volumes of clean, trusted data to train, predict, and deliver valuable outcomes. Poor data, on the other hand, introduces biases, amplifies errors, and undermines trust.

Consider the following cautionary examples:

• An online real estate company misused AI to predict housing prices, leading to a $300 million inventory write-down and mass layoffs.
• A major airline chatbot provided incorrect legal advice, exposing the company to costly liabilities.
• In hiring, AI recruiting tools introduced unintended biases, leading to legal and ethical scrutiny.

These cases demonstrate that flawed inputs—whether incomplete, inconsistent, or inaccurate data—produce unreliable AI outputs. In short, the “garbage in, garbage out” mantra applies to GenAI data.

According to Gartner, 30% of GenAI projects will fail by the end of 2025 due to poor data quality, unclear business value, and inadequate risk controls. These risks are real, but they are also avoidable. Ensuring high-quality data is the single most effective way to unlock the promise of GenAI.

5 Challenges in Ensuring Data Quality

Data professionals face several key challenges when preparing their data for GenAI. They include:

1. Disparate and Siloed Data Sources
   Many organizations struggle with fragmented data across systems, regions, or departments. Without unified, consistent data, GenAI models lack the full picture to deliver meaningful insights.
2. Data Volume and Complexity
   Businesses now manage massive, ever-growing data volumes coming from more sources than ever. Managing petabytes of sensor data, transactional records, time-series inputs, and other data requires modern solutions capable of real-time processing.
3. Outdated or Incomplete Data
   Stale or flawed data leads to inaccurate predictions. For GenAI to remain relevant, data must reflect real-time updates, evolving business needs, and shifting trends.
4. Lack of Governance and Transparency
   Without proper governance, organizations cannot ensure data accuracy, lineage, and compliance—critical elements for GenAI to meet regulatory and business standards.
5. Inconsistent Data Quality Processes
   Many organizations lack standardized processes for maintaining data quality across departments and systems. Inconsistencies in data validation, cleansing, and monitoring can result in discrepancies that undermine the accuracy and ultimately the confidence in GenAI outputs. Without a unified approach, organizations struggle to ensure data remains reliable, up-to-date, and aligned with business goals. 

The Solution: Building a Strong Foundation for Data

A unified data platform addresses these challenges, ensuring businesses can deliver clean, trusted, and integrated data to their GenAI models. This type of platform provides a comprehensive solution that supports:

• Data Integration. The platform should seamlessly integrate data from disparate systems, creating a single, unified source of truth. This eliminates silos and ensures GenAI has access to all relevant inputs.
• Real-Time Processing. A unified, scalable platform can handle massive data volumes, streaming extremely large data sets to power real-time predictions and insights.
• Data Quality Management. From data deduplication to error detection, the platform can ensure that data is accurate, complete, and reliable before feeding it into GenAI models.
• Governance and Compliance. Built-in governance tools in modern data platforms provide transparency and trust, allowing organizations to meet regulatory standards with confidence.
• Scalability. A platform’s ability to quickly scale supports modern data formats, enabling businesses to integrate all required data to scale their GenAI operations.

With the right platform, businesses can cleanse, connect, and prepare their data for GenAI use cases—whether automating workflows, delivering predictive analytics, optimizing operations, or achieving other business goals. A platform can be a driving force for GenAI success—or limit outcomes. That’s why organizations must understand their needs and implement a platform that meets their current and future requirements.

Achieving GenAI Excellence: The Benefits of Quality Data

When organizations address data quality challenges, they unlock the full potential of GenAI and other data-driven use cases. Ongoing benefits include:

• Accurate Insights. High-quality inputs result in reliable, actionable outputs, empowering smarter decision-making.
• Operational Efficiency. Automating tasks with GenAI reduces manual effort, increases productivity, and frees staff time for other tasks.
• Cost Savings. Clean data minimizes costly errors and accelerates the return on investment (ROI) for GenAI.
• Innovation. With trusted data, businesses can confidently deploy GenAI for complex use cases, like predictive maintenance, demand forecasting, and customer personalization.
• Competitive Advantage. Data-driven insights enabled by GenAI allow businesses to move faster, adapt to change quickly, and outperform competitors.

Take the Next Step: Prepare Your Data for GenAI

The journey to successful GenAI adoption starts with data readiness. Organizations that prioritize data quality will not only solve common challenges but also accelerate innovation and deliver measurable business outcomes.

The eBook “Realize the Promise of GenAI Today—and Avoid Common Pitfalls” offers proven strategies to help organizations ensure their data is ready for GenAI. It offers seven steps to achieve data readiness for GenAI and shares strategies to future-proof your data infrastructure for GenAI-driven success. It can equip organizations with the tools and knowledge to make GenAI work for them—powered by clean, trusted, and accurate data.

About Actian Corporation

Actian makes data easy. We deliver cloud, hybrid, and on-premises data solutions that simplify how people connect, manage, and analyze data. We transform business by enabling customers to make confident, data-driven decisions that accelerate their organization’s growth. Our data platform integrates seamlessly, performs reliably, and delivers at industry-leading speeds.