Bridging the AI Data Gap: How to Optimize Underutilized Data

The Challenge: Underutilized Data Limits AI Potential

Data is one of the most valuable assets for AI, yet many organizations struggle to use it effectively. Several factors contribute to this problem:

• Data silos: Organizations collect massive amounts of data from various sources. In fact, 64% of businesses manage data from 100 to 499 sources daily. When data is fragmented across multiple platforms, it becomes difficult to integrate and analyze.
• Poor data quality: If data is incomplete, inconsistent, or unverified, it cannot be reliably fed into AI models.
• Lack of governance: Without clear policies on data usage, organizations may not know which data should be prioritized for AI.
• Scalability challenges: AI systems need to process large amounts of data efficiently, but many organizations struggle with integration and scaling.

This underutilization of data leads to AI models that don’t reach their full potential. They provide insights based on partial information, making them less accurate and effective.

The Solution: Enhancing Data Readiness

To bridge the AI data gap, organizations must focus on improving data readiness. This involves:

1. Better Data Governance
• Establishing clear policies on data collection, storage, and usage.
• Ensuring data security and compliance with industry standards.
• According to the survey, 40% of organizations prioritize verified, secure, and transparent data, a key factor in AI success.
2. Seamless Data Integration
• Breaking down data silos by connecting data sources into a unified system.
• The survey shows that public cloud usage is expected to grow by 3%, indicating a shift towards more integrated and scalable environments.
• Moving towards hybrid or cloud-based solutions allows businesses to access and process data more efficiently.
3. Improving Data Quality
• Cleaning and structuring data before feeding it into AI models.
• Implementing automated tools to validate and enrich data in real time.
• Ensuring that AI models receive high-quality, trustworthy data for better decision-making.

Best Practices for Optimizing AI Data Usage

Organizations that optimize their data for AI can significantly improve performance and ROI. Here are key best practices:

1. Implement Robust Data Pipelines

• Data pipelines automate data collection, cleaning, and transformation.
• They ensure that AI models always receive fresh, high-quality data.
• Companies that process more than 50% of their data through AI models (22% of organizations surveyed) are likely leveraging strong data pipelines.

2. Prioritize Accessibility and Usability

• AI should have access to all relevant data, not just a fraction of it.
• Businesses should invest in platforms that allow for seamless data retrieval and processing.
• Ensuring data is structured and labeled correctly makes it easier for AI to interpret.

3. Scale AI Models for Future Growth

• AI needs scalable infrastructure to process increasing amounts of data.
• Organizations planning to expand AI should consider hybrid or cloud-based architectures.
• By investing in scalable solutions, businesses ensure that they can continuously improve their AI performance.

The Outcome: AI Models That Deliver Real Value

Optimized data means optimized AI. When businesses maximize their data usage, they see benefits such as:

• More accurate AI insights leading to better decision-making.
• Higher efficiency: since AI models can analyze complete datasets rather than partial ones.
• Improved ROI: because AI investments generate more actionable and reliable outcomes.
• Enhanced scalability: allowing organizations to keep up with growing AI demands.

By improving data readiness through governance, integration, and quality control, organizations can unlock the full power of AI. Instead of leaving valuable data unused, they can turn it into a competitive advantage.

Read the entire report “Data Readiness for Impactful Generative AI.”