How Embracing Data Fabric Can Raise AI Initiative’s Potential

Discover how data fabric empowers AI by integrating active metadata,

context, and real-time insights into your data strategy.

Data fabrics and data mesh help end users access the information they need for better decisions and work support. They do this by unifying data across systems, creating context, and reducing IT dependency.

 

 

Rising Complexity Calls for Data Agility

 

Today, data and analytics leaders work closely with IT and customer experience teams. Their goal is to align strategies for the highest business impact. Meanwhile, data volumes grow rapidly, especially unstructured data. IDC forecasts that by 2025, 80% of enterprise data will be unstructured. Despite this, 20% of structured data will still inform most business decisions.

Businesses often struggle to operationalise and understand unstructured data. As a result, corporate knowledge management is more critical than ever. AI success depends on strong data management, and new architectural models like data fabric and data mesh are taking centre stage.

 

 

Why Data Fabric Matters

 

Unlike traditional systems, data fabric offers flexibility and scalability. It adjusts to business needs and handles complex data expansion. Therefore, companies are adopting it as the core of their AI strategy. It ensures both governance and easy access, which are vital for successful AI deployments.

Context Is Crucial for AI

 

AI systems, including large language models (LLMs), need high-quality, contextual data. Informal knowledge sharing can help but only to a point. For lasting value, metadata-centric approaches, such as knowledge graphs and ontologies, are required. However, these tools often fall short when not connected to the underlying data.

Modern businesses want more than raw data. They want to link every dataset to its origin, intent, and audience. That’s why enterprise architecture must evolve to include semantic layers coherent, comprehensive, and accurate data contexts.

 

Outdated Data Practices Don’t Work

 

Previously, organisations copied data into analytical zones from ERP or CRM systems. Later, they used data lakes for unstructured content. But now, multiple systems must be integrated, requiring accurate, non-duplicated data in real time.

Although data warehouses provide a single source of truth, they’re rigid. Updating them with new external sources is slow. This limits real-time analytics. Ironically, while they gather data from silos, they often become silos themselves.

What Is Data Fabric?

Data fabric simplifies access to enterprise data by combining data and metadata into one unified view. Through a semantic layer, it maps organisational data assets and enables real-time access to knowledge. It boosts teamwork, encourages self-service analytics, and reduces reliance on IT.

This semantic layer reads data schemas automatically, maintains freshness, and presents information in a graph format. This allows LLMs and human users to interpret it easily.

 

Data Mesh vs. Data Fabric

 

Both data mesh and data fabric aim to simplify data access. Yet, they differ in governance and ownership. Data mesh distributes data ownership by domain. It encourages teams to manage data products independently. In contrast, data fabric leans on central governance with broader integration.

Most companies benefit from a hybrid approach. By combining federated governance with a central repository and robust security, organisations gain both flexibility and control.

According to Gartner, by 2027, 30% of businesses will use data ecosystems powered by data fabric to gain a competitive edge.

Advantages of Integrated Data Platforms

Structured data dominated analytics for years. However, AI needs more, like images, documents, and video. Organisations must now treat qualitative data as essential to their data and AI strategy.

Only a few platforms manage all these formats well. Most reshape the data into columns and rows. In contrast, modern platforms can retain data in its native form graph, list, document, or table.

These platforms also build knowledge models, business glossaries, and active metadata. That way, businesses deliver results faster and with less risk.

 

 

A Full-Stack Data Fabric Approach Enables:

 

• Connect – Bring all data and metadata into one enterprise-grade platform.
• Create – Use semantic AI to build knowledge graphs and metadata.
• Consume – Deliver high-quality, contextual data tailored to users’ needs.

Maintaining Trustworthy Data for AI

AI and ML offer huge promise. But accuracy is essential. Language models like ChatGPT can hallucinate, so their answers need validation.

How? By connecting outputs back to their data sources. Data fabric enables this traceability, grounding AI in trusted enterprise data.

Take retail as an example. Tracing a faulty product back to its supplier three months ago is tough. But it’s vital for quality assurance. Supply chain transparency and knowledge management help close these gaps.

 

Powering Smarter Decisions with Enterprise Data Services

 

AI reaches full potential only when active data, metadata, and meaning come together. As businesses shift from app-centric to data-centric models, they need platforms that store both data and its context.

With such tools, CIOs and CDOs can broaden their data access, improve decision-making, and develop better AI solutions. Real-time pipelines allow teams to ingest, curate, and act on data quickly.

By doing so, they respond faster to changes, innovate confidently, and stay ahead in a competitive AI landscape.