Smart Data: The Real Foundation of Enterprise Artificial Intelligence

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The Enterprise AI Paradox

Data for artificial intelligence has become the true foundation of business innovation. Yet many organizations try to apply AI on top of incomplete, duplicated, or poorly integrated data.

According to the Rethink Data report by Seagate and IDC, only 32% of enterprise data is effectively used, meaning that nearly 70% of corporate information remains unused or not ready for analysis.

The issue isn’t the technology—it’s the information infrastructure behind it. Without clean, integrated, and governed data, AI cannot learn, scale, or deliver reliable results.

Why Data Is the True Foundation of Artificial Intelligence

For years, companies focused on automating processes. Today, the real challenge is teaching machines how to decide—and that ability depends directly on data quality.

Google Cloud warns that “organizations struggle with fragmented data silos, poor data quality, lack of proper governance, and other challenges that hinder AI projects.”

Meanwhile, IDC reports that up to 80% of enterprise data is unstructured, making it difficult to analyze and prepare for AI models.

The Cost of Poor Data Preparation

80% of AI project time is spent cleaning and preparing data.
More than 60% of AI initiatives stall at the proof-of-concept stage due to inadequate infrastructure.
Companies with strong data governance are twice as likely to scale AI profitably, according to Aliz

How to Build an Intelligent Data Foundation

1. Data Quality and Governance

An AI model is only as good as the data that feeds it. Establishing clear policies on quality, lineage, and access is essential to ensure reliability.
Data governance enables organizations to trace data origins, enforce security controls, and ensure regulatory compliance.

2. Integration and Accessibility

Information silos limit a company’s global vision. Modernizing integration architecture through APIs allows data to flow seamlessly across systems, departments, and AI applications.

3. Modern Architectures: Data Lakes and Lakehouses

These platforms store both structured and unstructured data in a single environment. They simplify analytics, reduce duplication, and lay the foundation for machine learning and generative AI models.

A strong data foundation provides unified access to all your data sources, real-time performance, and accessibility for all teams.

4. Self-Service and Data Culture

Beyond technology, the challenge is cultural. A data-driven organization empowers its teams to access, understand, and use trustworthy data. This multiplies the value of AI investments and accelerates adoption.

Recommendations

Assess your data maturity. Identify gaps in quality, accessibility, and governance.
Unify your sources. Break down silos and prioritize real-time API integrations.
Create a strong governance framework. Define roles, responsibilities, and quality metrics.
Modernize your architecture. Implement Data Lakes and Lakehouses built for AI.
Engage the business side. Align your data strategy with organizational objectives.
Treat data as a strategic asset. It’s not about having more data, but having smarter data.

The success of artificial intelligence doesn’t depend on algorithms—it depends on the data that powers them.
Smart, governed, and high-quality data are the difference between experimental AI and transformational AI.

At Linko, we help organizations build AI-ready data infrastructures, integrating quality, governance, and modern architectures so every data-driven decision generates real business value.Smart Data: The Real Foundation of Enterprise Artificial Intelligence