What is a data fabric?

A data fabric is a unified approach to data management that seamlessly connects various data sources, whether they’re on-premise or in the cloud. It’s a technical framework for managing data in a distributed multi-cloud environment. A data fabric is also a mindset—an organizational approach to how you think about, access, and manage data. Think of it as a digital fabric that weaves together data from different databases, storage systems, and even streaming platforms, creating a unified view that simplifies data access and analysis.

In the past, traditional business intelligence (BI) models tended to limit data access, disempowering users. IT personnel managed every connection and access request. Although this workflow ensured the data was secure, it also prevented companies from scaling their data transformation.

A data fabric, however, offers companies a flexible, adaptive, and secure data architecture—one that can actually scale as they grow. With this approach, non-technical business users get to explore, analyze, and use data in a way that won’t be hindered by technical bottlenecks.

Key principles of a data fabric include:

• a centrally governed data framework, allowing any system to connect.
• automated data ingestion to securely manage data on an ongoing basis.
• self-service access so all employees can find and use the data they need.

Why does a data fabric matter?

A data fabric is both a technical framework and an organizational approach. This combination helps organizations scale access to ever-increasing amounts of data.In the age of information overload, a data fabric is a crucial tool for staying competitive and agile. That’s because the framework allows for quickly bringing in new data sources without relying on IT support. With a data fabric, organizations can give more users access to insights that will change, ease, benefit, and amplify their work.

Data fabric’s benefits include:

• Data exploration and analysis without IT : A data fabric empowers non-technical users to directly access and analyze data, reducing the burden on IT departments and accelerating decision-making.
• More efficient data management: Instead of data silos, a data fabric provides a unified platform for data storage and management, saving time and resources.
• Streamlined data delivery: A data fabric ensures that the right data is available to the right people at the right time, promoting collaboration and informed decision-making across the organization.

Innovation and disruptive trends in data fabric

The data fabric space is developing as fast as any. Below, we’re recapping recent innovations and trends that leaders should know about.

Querying data in place

Why it’s disruptive: Traditional data analytics often requires data to be moved from its original location to a centralized data warehouse for querying. The ability to query data in place without moving it disrupts this model, reducing the time, cost, and security risks associated with data movement.

The impact: This approach not only streamlines data access but also enhances real-time analytics capabilities. It could revolutionize sectors like IoT, where data is generated at the edge and needs to be analyzed in real-time. It also aligns well with privacy regulations that restrict data movement across borders.

AI model management

Why it’s disruptive: The rise of AI and machine learning has led to the proliferation of models that need to be managed, monitored, and updated. AI model management solutions offer a centralized platform for these tasks, making it easier to deploy, scale, and monitor machine learning models.

The impact: Effective model management can accelerate the time-to-market for AI initiatives and improve the ROI of AI projects. It also allows for better collaboration between data scientists and business analysts, thereby democratizing AI within the organization.

Integrations with large language models (LLMs)

Why it’s disruptive: The integration of LLMs like GPT-3 into data fabric solutions can automate tasks such as data querying, summarization, and even analytics. This makes the technology accessible to non-technical users and can significantly speed up data-related tasks.

The impact: By making it easier for a broader range of employees to engage with data, LLMs can drive a more data-driven culture within organizations. They can also automate routine tasks, freeing up data scientists and analysts to focus on more complex problems.

Data and model governance

Why it’s disruptive: As data and models become more integral to business operations, the need for robust governance mechanisms grows. Advanced governance solutions that offer automated compliance checks, version control, and audit trails are becoming increasingly important.

The impact: Enhanced governance tools can significantly reduce the risks associated with data and model management, including compliance risks and the potential for data breaches. This can make it easier for organizations to adopt data and AI solutions, knowing that governance is taken care of.

Data fabric vs. data virtualization

A data fabric and data virtualization both handle the intricacies of data integration, but each has a unique approach to data integration. Data virtualization provides a layer of abstraction for querying distributed data without physically moving it, like giving you a way to access data through an open window. The data stays in its separate location, but you can view it and interact with it without creating redundant copies of the data on a new, unified platform. The drawback to data virtualization is that data still lives in different areas, which makes it hard to see the bigger picture of the data.

A data fabric, on the other hand, encompasses a broader architecture that includes integration, orchestration, and accessibility. This trifecta promotes real-time use and analysis. Unifying data into a single platform takes more resources up front, but ultimately the data fabric creates an easier way to analyze disparate data sources together. A data fabric does the work on the back end to translate data sources into a common structure that allows for people to see all the data in one place.

The bottom line? Data virtualization can help you answer single questions within the data, while a data fabric helps you see a comprehensive story within the data.

Data fabric architecture

Data fabric architecture is an end-to-end data management process, one that encompasses the entire data lifecycle from integration to the end-user display. The architecture is centralized around a single source to which all the data connects. This can be done by IT personnel or through user-friendly, pre-built connectors that bring data sources into a single platform and help create a unified view of disparate data. Once data is connected and integrated, the data fabric architecture supports user interface access.

Because the data fabric architecture relies on a centrally governed location for all the data, the architecture fosters data democratization. In other words, it makes data available to people throughout the organization while maintaining data security and governance.

Benefits of a data fabric

Data fabric’s advantages extend beyond a technical setup. This approach can impact how the business will scale data use today and in the future. These benefits include:

• More agile management: A data fabric’s flexibility allows businesses to adapt quickly to changing data requirements and analytical needs; administrators can quickly add new data sources as they become available or share data with new users who will benefit from the insights.
• Scalable growth: As data volumes grow, an organization’s data fabric makes it easier to scale while accommodating expanding data sources and user demands. Tools that enable automatic data integration and easy transformation of new sources will be the pivotal piece of your data fabric and your company’s ability to scale data use.
• Seamless integration: As data sources grow, technical teams won’t have the capacity to manually integrate each one. A data fabric solves for this issue by using automatic ETL tools that allow teams to bypass IT to connect and integrate data sources.
• Democratization of data: By empowering people with direct access to data, a data fabric enhances innovation and collaboration across departments.
• Better data protection: A data fabric’s centralized approach simplifies data security implementation, ensuring data compliance and privacy.

Data fabric use cases

From healthcare to logistics, the use cases for data fabrics below showcase the transformative impact of data fabrics in creating seamless integration, informed decision-making, and increased efficiency.

Healthcare
A data fabric architecture could make it possible to seamlessly integrate patient data from electronic health records, wearable devices, and medical imaging systems to help clinicians make better decisions. A data fabric can give health providers a comprehensive view of a patient’s health history, enabling more accurate diagnoses and personalized treatment plans.

Retail
Using a data fabric, retail organizations can combine online and offline sales data with customer browsing behavior to create comprehensive customer insights. With a fuller understanding of customer preferences and behaviors, retailers can optimize their marketing strategies, tailor promotions, and improve overall customer satisfaction.

Finance
Within a data fabric architecture, finance organizations can streamline financial data from different branches, databases, and platforms to facilitate risk assessment and compliance monitoring. This enables them to make more informed lending decisions, identify fraud or risk within transactions, and ensure adherence to regulatory requirements.

Manufacturing
A data fabric can integrate data from Internet of Things (IoT) devices embedded in machinery to optimize production processes and predict maintenance needs, manufacturing organizations can use this framework to get a holistic picture of their processes and tools. By monitoring equipment health in real-time, manufacturers can reduce downtime, cut maintenance costs, and enhance overall operational efficiency.

Logistics
These organizations can use a data fabric to improve supply chain efficiency by unifying data from shipping, tracking, and inventory systems. This allows logistics companies to track shipments in real time, optimize delivery routes, manage inventory levels effectively, and improve overall customer service by providing accurate delivery estimates.

Marketing
Marketers can use a data fabric combine data from customer interactions, social media engagement, and campaign performance; this combination creates a 360-degree view of customer behavior. When marketers understand the entire customer journey, they can tailor their strategies, refine targeting, and deliver more relevant and resonant campaigns, ultimately boosting customer engagement and driving sales.

How to implement data fabric

Data fabric implementation can involve a lot of upfront resources to ensure data sources are correctly and efficiently connected and integrated. Implementing a data fabric requires resources up front to ensure the data sources are correctly and efficiently connected and integrated. But doing so will save time down the line in scaling data management and access for organizations. This process involves these key steps:

1. Assessment
Evaluate your organization’s data sources, analytical needs, and existing systems to determine the integration’s scope. Identify which data sources can be brought in, what transformations the data will require to be usable, and who will need access to the data.

2. Integration
Find tools that will allow you to easily and automatically connect data sources so your data fabric can scale with your needs. For example, find a tool with pre-built connections to common data sources and with the ability to build custom connections on the fly. This will help your organization move quickly and avoid technical bottlenecks. Choose appropriate integration tools that can connect diverse data sources while maintaining data quality.

3. Governance
Because a data fabric creates a centralized data architecture, it’s critical that, while your organization is connecting and integrating data, you establish a metadata management system to ensure data lineage, quality, and governance. A data fabric allows organizations to adopt a centralized data governance model that can support distributed data sources and access from one location.

4. User access
Your data fabric won’t mean much if your employees can’t access the data where and when they need it to make data-driven decisions in their roles. Your data fabric needs to provide a self-service way for them to access the unified data without relying on IT resources. The user interface should be intuitive for both technical and non-technical users. And, it can be as far reaching as a single BI platform or as targeted as data applications embedded into business workflows.

Data fabric is more than just a way to approach data management; it’s a strategic tool within your business. By seamlessly connecting disparate data sources, providing real-time access, and simplifying analysis, a data fabric empowers businesses to make informed decisions, enhance collaboration, and unlock new levels of innovation. As the digital landscape continues to evolve, a data fabric can become a powerful ally in navigating the increasing complexity of all the data within your organization.

Data fabric FAQs

Have more questions about data fabrics or how to choose the best data fabric platform? We’re here with answers.

What is an example of a data fabric?
An example of a data fabric is a system that seamlessly connects various data sources, such as databases, cloud storage, and streaming platforms. This system then creates an end-to-end data management platform that connects, integrates, and distributes data. A data fabric can be a series of tools an organization manages, or it can be one supported by a single tool that focuses on the entire lifecycle of the data.

Where is data fabric used?
A data fabric is most often used in organizations that create and store data in multiple cloud environments. It’s particularly valuable in industries like finance, healthcare, and retail.

What is a data fabric vs. a data lake?
A data fabric is a dynamic and interconnected architecture that unifies data sources, while a data lake is a centralized repository that stores large volumes of unstructured, raw data. Data fabric focuses on accessibility and real-time use, whereas data lakes primarily store data for future processing.

Is a data fabric the same as data virtualization?
No, data fabrics and data virtualization are not the same. A data fabric integrates data sources into a coherent architecture, whereas data virtualization provides a layer of abstraction that lets users access and query data without having to physically move or replicate it. Data virtualization requires fewer upfront resources but does not allow for a unified view unless users manually connect data.

What is a data fabric platform?
A data fabric platform refers to the software, tools, and infrastructure that enable the creation, management, and utilization of a data fabric. It includes components for data integration, orchestration, security, and analytics, providing a comprehensive solution for handling distributed data.