Table Contents
Table Of Contents
Table Of Contents
What Is Data Fabric? Definition and Key Concepts
Data fabric is a modern data architecture that connects and integrates data from different sources into a unified, organized and easy-to-access system, no matter where it resides - on the cloud, on-premises or across hybrid environments. Organizations often struggle with data silos and data scattered across multiple systems, making it difficult to access and manage information efficiently. Data fabric addresses these challenges by integrating and governing data from disparate sources. It handles large volumes of structured, unstructured and semi-structured data, using AI to figure out the best ways to organize, access and clean that data, making it ready for emerging AI-led use cases.
In a recent survey of U.S.-based IT leaders, the top five business priorities for data modernization are aiming to increase business operational efficiency (61.3%), improve business decision-making (52.7%), boost business automation (51.4%), improve data culture adoption and self-service data analytics (44.9%) and satisfy regulatory or compliance monitoring (39.9%).
Traditional data management approaches often fall short of these goals because they are siloed, fragmented, and struggle to handle the scale and complexity of modern data environments. Data fabric is an emerging modern data architecture framework that overcomes these limitations and checks all those boxes using the power of AI.
The chief purpose of data fabric is to unify and simplify data management across complex environments while enabling self-service data access through no-code/low-code principles. This enables reliable, real-time data processing and access, delivering complete customer insights and continuous real-time data — both vital for AI and machine learning applications.
Its effectiveness comes from several key components working seamlessly together. By combining these components, data fabric creates a robust, dynamic framework that powers modern data architectures and supports AI and ML applications. It’s not just about connecting data; it’s about doing so in a way that’s reliable, secure, efficient and intelligent.
The Key Components of Data Fabric
A typical data fabric operates across several layers that work together to enable enterprise data management at scale:
Data Virtualization
Data virtualization defines data and provides seamless access across sources without requiring physical data movement. This layer creates a unified view of distributed enterprise data, enabling data scientists and analysts to query information from multiple systems as if it were stored in a single location. For machine learning applications, this means AI models can access training datasets from various sources in real-time without the latency of traditional ETL processes.
Data Integration
Data integration connects disparate data sources and prepares them for advanced AI and analytics use cases. Modern data integration platforms use AI-powered capabilities to automatically map, cleanse, and transform data from structured, semi-structured, and unstructured sources. This automated approach significantly reduces the time data engineers and data scientists spend on manual data preparation tasks, accelerating time-to-insight for critical business decisions.
Data Orchestration
Data orchestration moves data around and makes it available to consumers and applications through intelligent workflow automation. This layer manages data pipelines, schedules processing tasks, and ensures data quality throughout the entire data lifecycle. For enterprise data fabric implementations, orchestration enables automated model training, real-time inference, continuous data lineage tracking, and seamless integration across multiple systems.
Supporting these three operational layers is a critical discovery and governance component:
Data Catalog
Serving as a centralized inventory, the data catalog provides metadata and context for each dataset — enabling users and systems to quickly discover and trust the right data. Data fabric ensures consistent quality, compliance and security of the data across the fabric, helping organizations confidently meet industry or geography-specific regulations.
Modern data catalogs leverage AI and active metadata to automate discovery and classification, making data more accessible to users across the organization.
Active Metadata and Knowledge Graph Architecture for AI
Building on these foundational components, modern data fabric architecture leverages advanced AI capabilities to create intelligent connections and insights. The knowledge graph connects data intelligently, providing context and enabling better understanding of data. This component leverages semantic context to make data easier to interpret and work with, using AI-driven reasoning to help uncover hidden relationships between diverse datasets for richer insights.
Active metadata analysis refers to the continuous use and examination of metadata (data about data) to automate and optimize data management tasks. Data fabric enables real-time, unified access to data across complex data storage environments, without physically moving it, allowing users and applications to find and access data seamlessly, regardless of where it resides or what formats it is in.
AI and machine learning (ML)-powered capabilities leverage a combination of active and passive metadata and knowledge graphs to recommend actions, optimize data usage, and enhance decision-making processes in real time.
Benefits of Data Fabric in AI-Powered Modern Data Architecture
Data fabric offers key advantages over traditional data management systems, particularly in AI-powered data environments where flexibility, real-time insights, and scalability are crucial. By seamlessly integrating diverse data sources, data fabric supports modern analytics and enhances decision-making capabilities across the organization.
Unified, Self-Service Data Access Speeds Up Time to Value
Unlike traditional systems facing challenges unifying fragmented data across environments, data fabric provides a seamless layer for accessing and integrating data across multiple environments (cloud, on-premises, and hybrid). This real-time access enables AI algorithms to work with a holistic view of data, eliminating bottlenecks and making trusted data accessible faster.
For business users, this accelerates self-service data discovery and analytics. While traditional systems require longer setup times for data integration and preparation before AI applications can generate insights, data fabric accelerates the deployment of AI initiatives by streamlining and orchestrating workflows and delivering ready-to-use data quickly, helping businesses to realize value faster.
This approach facilitates data consumption by enabling both business users and data analysts to easily access, discover, and utilize diverse data sources for comprehensive analysis and data-driven decision-making.
Faster and Smarter Decision Making with Real-Time Data Processing
Data fabric automates data engineering tasks and augments data integration to deliver real-time insights. Users can find, understand and trust data with automated data discovery and enrichment. This low-latency, real-time data processing is critical for AI-powered applications like fraud detection, predictive analytics, and dynamic pricing, which also benefit from the continuous flow of high quality and consistent data for more accurate predictions and decisions.
Drive Productivity and Accuracy with AI-Driven Automation
Traditional approaches with manual data mapping, quality checks and pipeline creation can be time-consuming and error prone. Data fabric leverages AI-powered automation for end-to-end workflows, from pipeline creation to data governance, which accelerates the availability of high-quality data for advanced analytics and AI models.
Risk Mitigation and Enhanced Data Governance
Built-in governance frameworks help automate data lineage, ensuring compliance and building trust in data for AI. Data fabric enhances data security by integrating policies, access controls, and encryption to protect sensitive data and ensure compliance across diverse data environments. Data fabric leverages active metadata to improve data quality, data curation, data classification, policy enforcement and more — unlike traditional systems that lack the capabilities for automated governance and lineage tracking, making compliance a challenge.
Advanced data security features, including integrated policies, access controls, and encryption, protect sensitive data across diverse data environments while maintaining accessibility for authorized users.
Keep Up with the Pace of Business in the AI Era
Companies must scale with the exponential growth of data and optimize costs while maintaining agility and elasticity. Data fabric easily scales across distributed data sources and supports diverse data types, making it ideal for AI environments that require massive and varied datasets for training and inference.
It is also a flexible architecture that adapts dynamically to change, such as integrating new data sources or shifting between cloud and on-premises environments. Because it can natively support multi-cloud and hybrid environments, data fabric lets businesses leverage the best of different platforms with the agility needed for evolving workloads and business needs.
This scalability ensures data fabric can meet growing enterprise data needs while providing the flexibility and capacity for expanding data requirements across hybrid and multi-cloud environments.
When to Choose Data Fabric Over Other Data Architecture Approaches
While other modern data architectures, such as AI data mesh and data lakehouse, offer strengths in terms of data ownership and access, data fabric offers unique advantages. As shown in Figure 1, data fabric is ideal for organizations where the business demands real-time data access, but the data is distributed across on-premises and cloud systems in various structured and unstructured formats.
| Data Fabric | |
|---|---|
| Data ownership | Centralized - creates a unified layer to manage data across distributed environments |
| Strength | Seamless integration of fragmented data |
| User advantage | Simplifies data access |
| Ideal user | Organizations with multi-cloud or hybrid data environments |
Data fabric can work wonders with data integration across distributed environments, providing seamless support for AI-driven applications that need a continuous stream of cleansed, transformed, enriched and high-quality 360 enterprise data. It is even more useful when data governance, compliance and security are of strategic significance. This is because it simplifies data access for users while centralizing data management by creating a unified layer across distributed environments.
For example, regulated industries with stringent data governance and data format norms, such as healthcare and financial management, typically store data in diverse formats across various systems. Often, security regulations dictate that some sensitive health or financial data be stored on-premises and not on public cloud servers. At the same time, these industries require their data consumers to have on-demand access to relevant data to process information, garner insights and make decisions that run the business.
For instance, patient data or loan applications need quick resolution, and any errors or delays can lead to severe consequences for both the customer and the company. In these situations, data fabric adds a unified layer for seamless data integration and access, helping deliver the right data to the right user regardless of where the data is stored or the format it is in.
Key Considerations for Data Fabric Implementation
The choice of data architecture depends on your business priorities, resource availability and current data maturity. If your organization is considering modernizing data architecture, start by assessing your current data landscape. How distributed and fragmented is your data? Is it spread across multiple environments — on-premises, hybrid-cloud and multi-cloud — and in multiple structured and unstructured formats? Is accessing or integrating data complicated by silos? How urgent is it for your business users to access continuous, real-time data for AI, machine learning or analytics use cases?
Next, identify your business priorities. Is automating data workflows to improve operational efficiency your most pressing need? Do you have stringent governance and regulatory requirements that must be complied with urgently? How much scale and flexibility will your data management needs demand now and in the future?
Organizational readiness is also important, including resource allocation, technical skills and management buy-in.
While planning is key, document any anticipated roadblocks that may arise.
For instance, data integration is often more challenging than expected, especially when legacy systems do not connect easily with newer parts of your data stack. Outdated infrastructure may limit modern data fabric capabilities. Overlooking implementation complexity can result in delays and cost overruns. While data fabric improves data quality and governance, poor existing data quality can undermine its effectiveness. Allocating appropriate budgets and technical skills is key because while data fabric provides end-to-end visibility, it can require significant investment to ensure comprehensive outcomes.
Choosing the right technology partners for successful data fabric implementation is critical. Look for vendor with proven credentials in data governance and expertise in advanced data architectures. Avoid solutions that limit flexibility or tie you to a single vendor. Instead, select a vendor-agnostic data management platform that offers a unified, end-to-end solution to meet your current and future needs.
Use Cases and Examples of Data Fabric in Modern Data Architecture
Data fabric has a wide range of practical applications across industries, as it enables organizations to seamlessly integrate, manage and access data in real-time. Beyond enhancing decision-making and operational efficiency, data fabric optimizes data flow and scales data systems to support advanced AI use cases.
Enterprise data fabric implementations provide the unified architecture needed for DataOps and incorporate fundamental components that research organizations like Forrester identify as critical for modern data management.
Unified Customer 360 View
By aggregating data from CRM systems, social media, e-commerce platforms and IoT devices, organizations can create a comprehensive, 360-degree unified view of their customers. This holistic insight empowers personalized experiences, targeted marketing campaigns and improved customer service.
In sectors such as retail and eCommerce, data fabric helps unify data from in-store sales, online transactions and customer behavior analytics. Leveraging this integrated data, businesses can use AI to optimize pricing strategies, streamline inventory planning and improve customer recommendations.
Fraud Detection and Risk Management
By providing real-time access to diverse transactional data and using AI to detect patterns and anomalies, data fabric improves fraud detection accuracy in industries such as financial services, insurance and energy management. It can also support real-time risk assessment by integrating and analyzing data from multiple sources, empowering organizations to respond proactively to market changes or operational risks.
Accelerated AI and ML Development
Modern-day AI applications require high-quality, consistent “AI-ready” datasets to train the models effectively. App owners struggle with the availability of real-time streaming data that is not just connected but also accurate and reliable. Data fabric automates and streamlines the preparation of reliable, high-quality data from a diverse set of sources. This approach accelerates the time-to-deployment for AI solutions and ensures models remain accurate with real-time data updates.
Managing Sensitive Data
In industries such as healthcare, which handle highly sensitive data and where the consequences of mishandling data can be severe, data fabric unifies patient records, clinical trial data, and diagnostic images across providers and systems, many of which use different formats due to regulatory mandates. It also automates the tracking of data lineage and ensures compliance with data privacy laws like the Global Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA).
Not only does this enable better collaboration between an ecosystem of healthcare service providers for the most holistic patient care, but data fabric also improves treatment outcomes by allowing timely medical interventions and precision medicine.
Why Choose Informatica for AI Data Fabric
Businesses that can harness the raw power of data typically lead their respective industries. However, managing and using data is easier said than done.
Informatica’s expertise in data integration and cloud-native solutions provides end-to-end support for implementing data fabric in AI-powered environments. Key capabilities such as AI-powered optimization, automated governance and real-time data access make Informatica an ideal partner for data fabric, leading to concrete business outcomes.
Enable frictionless data sharing – Gain better insights, smarter decision making and improved business outcomes through integrating and connecting complex enterprise data.
Accelerate self-service data discovery and analytics – Make trusted data available, discoverable and accessible faster to all applications and data consumers.
Lower data management costs and efforts – Focus on strategic work, improving operational efficiency and freeing up human resources with the intelligent automation, optimization and augmentation of data integration and management tasks.
Respond to emerging business needs with greater speed and agility – Get greater clarity and insights with a unified view of business data via continuous integration and analysis of diverse, siloed data assets and their business-relevant relationships.
Better data governance and protection – Ensure your company remains compliant with data privacy and security regulations and laws without hampering user access to the right data to run their business from AI-powered automation of data lineage and quality.
Data Fabric in Practice: Customer Story
BMC transforms complex technology into extraordinary business performance with a data fabric. BMC software (BMC) helps companies around the world improve how they deliver and consume digital services. For their accounts payable and generic ledger operations, BMC had been using decentralized, manual processes. This caused a lack of standardization across countries and affected the BMC treasury team’s ability to see current account balances. As a result, BMC had to maintain excessive cash reserves to cover any unexpected cash needs.
Working with Informatica, BMC rapidly built a functional system. They then added more sophisticated capabilities for improved visibility into actual and projected cash flows. This allowed BMC to right-size its cash position and optimize the use of its working capital.
BMC saved hundreds of thousands of dollars and now has much better reporting and control across hundreds of bank accounts. Accurate visibility into its holdings has allowed it to improve risk management and mitigation strategies.
Leveraging Data Fabric for Scalable, AI-Ready Data Architecture
For businesses looking to build AI-ready, scalable, and real-time data ecosystems, data fabric offers a compelling case for modernizing the data architecture.
Data fabric intelligently and efficiently integrates and connects an entire organization's data by abstracting underlying complexity. It minimizes disruption by enabling a highly adaptable data management strategy with augmented data integration and management.
Learn how data fabric can transform your enterprise data architecture today.
