What is a Data Fabric?

A data fabric is a modern approach to data management that speeds and simplifies access to data assets across the entire business. It accesses, transforms, and harmonizes data from multiple sources, on demand, to make it usable and actionable for a wide variety of business applications.

It’s fundamentally different compared with approaches like data lakes and data warehouses in that rather than creating more data silos, it complements an organization’s existing data and data management assets already in place, and can access the required data on demand – directly from the source systems – as needed.

It achieves this by creating a non-disruptive, overarching layer that connects to data at the source, and transforms it into a harmonized, consistent, and unified view that can be used for a wide variety of applications across the organization.

Through automation and real-time processing, it ensures data is consistently accessible, secure, and ready for analysis.

This approach not only simplifies data management but also empowers organizations to efficiently harness more - and more current - data for deeper insights, driving innovation and operational efficiency.

Organizations that adopt a data fabric benefit from better operational efficiencies and more strategic use of data.

To understand data fabrics, it's important to grasp the essential components and the value they bring to businesses through improved data management and accessibility.

A data fabric refers to an architecture and set of data services providing consistent capabilities across a spectrum of data sources, in different formats, and with different latencies, in on premises, hybrid and multi-cloud environments.

Data fabrics enable a centralized and consistent view of disparate data – despite the data residing in different formats and locations – for use by a wide range of consumers and use cases.

You can think of a data fabric like the conductor of an orchestra.

Just as a conductor harmonizes the diverse instruments to produce a unified and beautiful piece of music, data fabrics integrate and manage data from various sources – applications, databases, files, message queues, etc. – into a cohesive and usable fabric.

• Connect and Collect:  For some applications, it is more appropriate or efficient to process the data where it lies, without persisting the data (connect). For other scenarios, it is desirable to persist the data (collect). A data fabric should support both approaches.
• Scalability: The architecture is designed to scale both horizontally and vertically, accommodating the growing volume of data without compromising performance.
• Flexibility: The data fabric must support a wide range of data types, enabling businesses to work with data from any internal and external sources and in any formats.
• Interoperability: Data fabric architectures emphasize interoperability across different platforms and environments, ensuring that data can flow freely and securely.
• Automation: By automating data management tasks, data fabrics reduce the need for manual intervention, improving efficiency and reducing the likelihood of errors.

Businesses have no shortage of data. In fact, organizations today collect far more data than at any time in the past. This is why data fabrics are so important – they address the complexities introduced by the massive amounts of dissimilar data generated from diverse sources.

Data fabrics streamline data accessibility and interoperability among disparate systems, hence empowering organizations with timely and well-informed decision-making.

They also significantly reduce the time and effort required to manage data, a non-negotiable for modern data-driven businesses.

Utilizing a data fabric architecture offers businesses a multitude of advantages, specifically tailored to navigating the complexities of modern data landscapes and unlocking the value hidden within vast and diverse data assets.

High-level advantages include:

• Increasing operational efficiencies
• Improving strategic decision-making
• Streamlining operational workflows
• Boosting regulatory compliance

Here are some other specific benefits:

• Seamless Access Across Silos: Data fabrics bridge the gaps in data silos, providing unified access to consistent and trusted data across different environments, platforms, and locations. This seamless access supports better integration and collaboration within the organization.
• Real-Time Data Availability: By facilitating real-time data processing and integration, a data fabric ensures that decision-makers have access to up-to-date information, enhancing responsiveness to market changes and opportunities.

• Simplified Data Governance: With a data fabric, businesses can implement and enforce consistent data governance policies across all their data, regardless of where it resides. This unified approach to governance helps in maintaining data quality, accuracy, and compliance with regulations.
• Automated Data Processing: Data fabric architectures incorporate automation for integration, data discovery, classification, access and quality control, reducing manual efforts and minimizing errors. This automation supports more efficient and reliable data management practices.

• Faster and More Flexible Analytics: By providing a holistic view of an organization’s data landscape, data fabrics enable faster data analytics and more flexible business intelligence. This capability allows companies to quickly turn data into actionable insights.
• Support for Advanced Data Analytics: Some data fabrics are designed to handle complex data processing and analytics workloads directly within the fabric including machine learning and AI, enabling businesses to execute advanced analytics strategies directly within the fabric, for competitive advantage. These capabilities eliminate the need to copy large data extracts to separate environments for analytics, and are ideal for real-time and near real-time use cases. These are sometimes referred to as smart data fabrics.

• Reduced Data Management Complexity: By abstracting the complexity of underlying data sources and infrastructure, data fabrics allow organizations to manage their data more efficiently, reducing the time and resources required.
• Lower Infrastructure Costs: Through better data management and the ability to integrate diverse data sources efficiently, businesses can optimize their data storage and processing infrastructure, leading to significant cost savings.

• Consistent Security Policies: Data fabrics enable the enforcement of consistent security policies and access controls across all data, helping to protect sensitive information and reduce the risk of data breaches.
• Simplified Compliance: The unified governance model supported by data fabrics simplifies compliance with data protection and industry regulations by providing tools for data tracking and lineage, reporting, and policy enforcement across different jurisdictions.

Data fabrics are able to work with multiple data types and data integration styles across many platforms and locations.

Here's some more detail on how they work under the hood.

1. Data Ingestion Layer

This layer is responsible for connecting to and collecting data from various sources, including databases, cloud services, SaaS platforms, IoT devices, and on-premises systems.

It supports multiple data formats and ingestion methods, including both connect (virtualization) and collect (persistence) paradigms, ensuring that data is accurately captured and made available for processing.

2. Data Storage and Organization Layer

At this level, ingested data should be stored in a flexible, multi-model data storage engine.

This layer emphasizes the organization and cataloging of data, employing metadata management to facilitate easy discovery and access.

3. Data Processing and Integration Layer

Data within the fabric is processed and transformed to meet the needs of different applications and analyses. This includes cleansing, transformation, normalization, validation, reconciliation, enrichment, and other tasks.

The integration aspect allows for the harmonization of data from disparate sources, ensuring that data is consistent, accurate, and ready for use across the organization.

4. Data Governance and Security Layer

Central to the data fabric architecture, this layer implements policies for data quality, privacy, compliance, and security.

It ensures that data usage adheres to regulatory standards and organizational policies, applying encryption, access controls, and auditing mechanisms to protect sensitive information.

5. Data Access and Delivery Layer

This layer facilitates the efficient access and sharing of data across the enterprise and with external partners, when necessary.

It supports various data delivery mechanisms, including APIs, data services, and event streams, enabling users and applications to retrieve and subscribe to the data they need in a convenient manner. The data fabric should support a wide variety of access protocols, including relational, document, REST, etc. without the need for data mapping and duplication.

6. Analytics and Insights Layer

The analytics processing capabilities should be built directly within the fabric itself, including:

• Advanced analytics
• Machine learning
• Generative AI
• Business intelligence
• Natural language processing
• Business rules
• Analytic SQL

and other analytics capabilities to generate insights and programmatic actions from the data – all without the need to copy data extracts to external environments..
The data fabric should natively support real-time analytics, intelligent operational workflows, and decision-making, helping organizations to derive actionable intelligence and strategic value from their data.

Let's explore a few hypothetical examples of how different types of companies could leverage data fabric technologies to solve unique business challenges, highlighting the diversity and adaptability of data fabric solutions.

Scenario: A global retail company wants to create a unified customer view across its online platforms, physical stores, and mobile apps to offer personalized shopping experiences and improve customer loyalty.

Data Fabric Use: The company implements a data fabric to integrate customer data from its e-commerce systems, point-of-sale systems in physical stores, CRM system, mobile app usage data, and customer feedback across social media platforms

The data fabric provides a real-time 360-degree view of customer interactions and preferences, and suggestions for customer next-best actions and promotions.

Technologies Used: Real-time analytics for customer behavior, machine learning models for personalization, and data virtualization capabilities to integrate disparate data sources seamlessly.

Scenario: A multi-national bank needs to enhance its fraud detection capabilities and ensure compliance with global regulatory requirements without impacting customer service.

Data Fabric Use: By employing a data fabric, the bank integrates transaction data across different business units and platforms in real-time, applying advanced analytics and AI-driven models to detect fraudulent activities more effectively. It also automates compliance reporting by ensuring all data adheres to regional regulations through a unified governance framework.

Technologies: Machine learning for fraud detection, real time streaming ingestion capabilities that trigger the programmatic execution of ML models, and automated compliance tools within the data fabric architecture.

Scenario: A healthcare system aims to improve patient care outcomes and advance medical research by integrating patient records, research data, and real-time health monitoring devices.

Data Fabric Use: The healthcare system uses a data fabric to unify electronic health records (EHR), genomic research data, and IoT device data from wearables and in-hospital monitoring equipment. This integration enables personalized patient care plans and breaks down data silos that hinder good patient care.

Technologies: IoT data integration for real-time health monitoring, data analytics for research, and secure data exchange platforms to access data securely and privately.

Scenario: An international manufacturing company seeks to optimize its supply chain operations to reduce costs and improve time-to-market for its products.

Data Fabric Use: The company deploys a data fabric to integrate data from its supply chain partners, production line sensors, and inventory management systems.

Using predictive analytics, the data fabric identifies potential supply chain disruptions before they occur and suggests optimization strategies to meet customer commitments and SLAs.

Technologies: Predictive analytics for supply chain insights, IoT for production line monitoring, and data integration tools for partner ecosystems.

Data virtualization is a technology that allows for the real-time or near-real-time integration of data from disparate sources, without requiring the physical movement or replication of data.

It creates a unified, abstracted view of data from multiple heterogeneous sources, including databases, files, web services, and applications, making it accessible through a single virtual layer.

This approach facilitates access to data in a format and structure that is most useful to the end-users or applications, regardless of the original format or location of the data.

Key features of data virtualization include:

• Reduced Complexity: Simplifies the data landscape by minimizing the need for data replication and physical data storage, thereby reducing storage costs and eliminating data redundancy.
• Integration of Diverse Data Sources: It can combine data residing in various formats and locations, providing a consolidated view across distributed and dissimilar data.
• Real-time Data Access: Offers the capability to access and query data in real time, ensuring that users have the most current information at their disposal.

A universal semantic layer is an abstraction layer that sits between business users (such as business analysts, applications, and decision-makers) and the underlying data sources.

This layer abstracts the technical details of how data is stored, presenting a unified, simplified interface for accessing data across the enterprise.

A universal semantic layer supports:

• Data Abstraction: It presents a consistent and business-friendly data model to all consumers of the data.
• Query Optimization: Intelligent query processing capabilities ensure that data requests are fulfilled in an efficient manner, optimizing performance and resource utilization.
• Data Security and Governance: Centralized control over data access and usage, implementing security, privacy, and compliance rules consistently across all data.

Implementing a data fabric involves a structured approach and adherence to industry best practices to ensure a robust and scalable solution.

The process involves several key steps, from planning and design to deployment and ongoing optimization. Here’s a step-by-step guide to help an organization embark on this journey:

1. Identify Business Goals: Understand the specific business outcomes you aim to achieve with a data fabric, such as improved data accessibility, better decision-making, or enhanced customer experiences.
2. Assess Current Data Challenges: Pinpoint existing data management challenges, including data silos, data quality issues, or inefficiencies in data processing.

1. Catalog Data Sources: Inventory the relevant data sources within the organization, including databases, files, on premises and cloud applications, cloud storage, and third-party data and applications.
2. Evaluate Data Infrastructure: Assess the current state of your data infrastructure to identify potential gaps or areas for improvement in handling, processing, storing, and analyzing data.

1. Choose the Right Technologies: Based on the objectives and current state assessment, select the appropriate capabilities for your data fabric. A data fabric architecture may require many different data management capabilities or services. One best practice is to look for platforms that combine many of the required capabilities in a single product, minimizing complexity and speeding time-to-value.
2. Architectural Blueprint: Develop a detailed architectural blueprint that outlines how different components of the data fabric will interact, ensuring scalability, security, and compliance.

1. Data Governance Policies: Establish clear data governance policies that cover data quality, privacy, security, and compliance standards.
2. Roles and Responsibilities: Define roles and responsibilities for data stewardship, ensuring accountability and ownership of data across the organization.

1. Select a Pilot Area: Choose a specific business area or use case to pilot the data fabric implementation. This should be an area that can provide quick wins or valuable insights.
2. Implement and Test: Deploy the necessary functionality and integrate the selected data sources. Validate the implementation by exercising data access, integration, and analytics functionalities on a specific use case.

1. Expand Gradually: Based on the success of the pilot, gradually expand the scope of the data fabric to include additional data sources and business areas.
2. Monitor and Optimize: Continuously monitor the performance of the data fabric, making adjustments as needed to improve efficiency, scalability, and data quality.

1. Training and Support: Provide training and resources to ensure that employees can effectively utilize the data fabric for data access and analysis.
2. Encourage Collaboration: Foster a collaborative environment where data insights are shared and used to drive decision-making processes across the organization.

1. Feedback Loop: Establish mechanisms for collecting feedback from users of the data fabric to identify areas for improvement.
2. Stay Updated: Keep abreast of advancements in data management technologies and practices to ensure that the data fabric evolves to meet future business needs and opportunities.

By following these steps, an organization can successfully deploy a data fabric that enhances its ability to leverage data for competitive advantage, operational efficiency, and innovation.

By breaking down silos and integrating data across diverse sources and platforms, a data fabric not only simplifies data management but also unlocks a new realm of insights, efficiency, and innovation.

As businesses continue to navigate the complexities of the digital era, the agility and intelligence provided by a data fabric architecture become indispensable assets.

Among the numerous technologies enabling the construction of a robust data fabric, InterSystems IRIS stands out above the rest.

InterSystems IRIS provides many of the capabilities required to implement real-time, smart data fabric architectures in a single product, eliminating the need to deploy, integrate, and maintain dozens of different technologies.

Providing all of these capabilities in a single product built on a single code base speeds time to value, reduces system complexity, simplifies maintenance, and delivers higher performance while requiring fewer system resources, compared with building a solution using multiple point solutions.

With its ability to handle a wide variety of data types in a single data engine, high performance real-time data integration and sophisticated analytics, and mission-critical transaction and event processing capabilities, InterSystems IRIS provides organizations with a scalable, secure, and efficient way to realize the full potential of their data.

By leveraging advanced technologies like InterSystems IRIS, organizations can accelerate their journey towards becoming truly data-driven, ensuring that they are well-equipped to meet the challenges and opportunities of the future.

The road to implementing a data fabric may require strategic planning, commitment, and the right technological partnership, but the benefits of enhanced data accessibility, improved decision-making, and operational excellence are well worth the effort.