A Business Executive‘s Guide to Adopting Data Fabric

Hi there!

As a business leader in the digital age, you know that leveraging data is critical to drive decisions and unlock value in your organization. However, with data spread across cloud, on-premises, edge, and more, managing it has become highly complex. This is where adopting a data fabric architecture can make a huge difference.

In this comprehensive guide, I‘ll explain what data fabric is, its key capabilities, and how it can help overcome common data management challenges. I‘ll also dive into the top 7 use cases of data fabric with real-world examples and statistics.

By the end, you‘ll understand how data fabric enables deriving maximum value from distributed data at enterprise scale. Let‘s get started!

What is Data Fabric and Why Does it Matter?

Data fabric provides integrated data management and analytics across your organization‘s diverse infrastructure and environments. It creates a scalable, flexible data layer that sits above your raw data sources.

Data fabric provides an integrated data management and access layer above raw sources

Here are some key capabilities a data fabric architecture offers:

  • Unifies data silos: Integrates data spread across databases, lakes, warehouses, apps into a cohesive layer.
  • Manages distributed data: Operates seamlessly across on-premise, multi-cloud, edge and hybrid environments.
  • Enables self-service access: Allows users to easily query and analyze data through familiar interfaces like SQL, BI tools or notebooks.
  • Handles data at scale: Elastically scales to process large volumes of batch and real-time data.
  • Portable analytics: Run analytics close to where data resides, minimizing movement. Avoid cloud vendor lock-in.
  • Enforces security and governance: Apply fine-grained data access controls, encryption, masking consistently across environments.

This combination of distributed data management, scalability, and governance is essential for modern data-driven organizations. Data fabric empowers deriving value from data, regardless of its volume, location or format.

The Top 7 Use Cases Driving Data Fabric Adoption

Data fabric adoption has accelerating over the past few years, with worldwide Google searches growing 5X since 2018.

Global Google searches for "data fabric" have risen sharply since 2018 [Source]

What‘s driving this surge in interest? The top use cases that are propelling data fabric adoption include:

1. Unified Data Integration and Analytics

Integrating enterprise data from diverse sources is difficult with traditional methods. A Forrester survey of data management professionals found that:

  • 76% struggle with integrating data across cloud and on-premises environments
  • 72% have issues combining structured and unstructured data sources
  • 67% find it difficult to join real-time and batch data

Data fabric provides a unified integration layer that connects these disparate data landscapes.

Data fabric integrates data silos into a unified analytics-ready data layer

With data fabric, you can build data pipelines that combine real-time events, customer transaction data, social media feeds, and more regardless of where they reside. This powers holistic analytics.

For instance, Swisscom leveraged data fabric to integrate over 60 data sources. This reduced time spent finding and preparing data by over 80%, accelerating analytics adoption.

2. Distributed Hybrid Analytics

Organizations often have their data spread across both cloud and on-premises environments today. But moving huge volumes of data to the cloud for analytics can be prohibitively expensive – up to 5-10X the storage cost!

A data fabric architecture provides a powerful alternative that connects cloud and on-prem data to enable hybrid analytics. You can leverage cloud elasticity while keeping data close to sources and avoiding replication.

Data fabric allows running analytics across cloud and on-prem environments

For example, Michelin performs near real-time analytics on over 1 Billion tire sensor readings per day. By using data fabric, they avoided moving massive volumes of sensor data to the cloud.

3. Self-Service Data Access

Data teams often struggle with constantly answering ad-hoc analytical requests from business users. Data fabric provides self-service analytics capabilities to democratize data access.

The unified view and rich catalog metadata makes it easy for users to query data via intuitive interfaces without deep technical knowledge. Analysts get access to fresh data faster.

Data fabric powers self-service analytics by business users via easy-to-use interfaces

At [LabCorp](https:// ARQ.io/press-release/labcorp-drug-development-experience-data-fabric/), data fabric reduced time for users to find, understand, and query data from weeks to minutes. This accelerated insights and decision making.

4. Real-Time Data and Event Streaming

Applications like fraud detection, personalized recommendations, and predictive maintenance rely on analyzing real-time data feeds.

Data fabric provides robust event streaming capabilities to build these systems. It can consume and process millions of events per second from sources like databases, IoT devices and transactional apps.

Data fabric enables real-time streaming analytics on events from diverse sources

For instance, Banco Santander uses data fabric to stream millions of daily transactions into their real-time anti-fraud engine. This reduced false positives and improved fraud detection accuracy.

5. Multi-Cloud Data Management

According to a Flexera 2022 survey, 93% of organizations have a multi-cloud strategy today combining AWS, Azure, GCP and others. But 46% find it difficult to manage data across cloud.

Key challenges faced in multi-cloud adoption [Source]

Data fabric enables a portable analytics layer that connects data and apps across multiple cloud vendors. You get the best capabilities of each cloud without lock-in.

For example, Thomson Reuters leveraged data fabric to analyze financial data across AWS and GCP, while retaining sensitive data on-premises.

6. Master Data Management

Master data management (MDM) aims to create single golden records for customers, products, suppliers etc. by consolidating data from across enterprise systems.

Data fabric offers data virtualization and consolidation capabilities that are ideal for implementing MDM at scale, without moving data. It can virtually integrate master data from tens of downstream systems.

Data fabric federates master data from sources into curated MDM hubs

For example, Shell leverages data fabric to manage product data entities across 200+ downstream systems, avoiding complex ETL pipelines.

7. Regulatory Compliance and Governance

With data privacy regulations like GDPR and HIPAA, demonstrating governance and compliance is a must. Data fabric provides unified data security, access control, and auditing across environments.

Rich metadata enables consistent policy implementation. You can track data lineage, detect breaches with analytics, and mask/tokenize sensitive data flows.

Data fabric strengthens regulatory compliance across fragmented data ecosystems

For instance, Santen Pharmaceuticals leveraged data fabric to speed up GDPR compliance audits from months to weeks.

Key Takeaways on Data Fabric Adoption

Here are some key points on how data fabric can help your enterprise manage and extract value from distributed data:

  • Unified data access and insights: Break down data silos to enable holistic analytics with integrated, up-to-date information.
  • Cloud-agnostic analytics: Connect and analyze data across on-prem and multi-cloud environments to avoid lock-in.
  • Self-service data marketplace: Empower users with easy self-service access to trusted data via catalogs, search and AI-recommendations.
  • Real-time data-in-motion architecture: Ingest, process and analyze real-time events from across your business for instant insights.
  • Accelerated cloud migration: Gradually migrate apps and data to the cloud without business disruption or downtime.
  • Compliant-by-design: Apply security, access control and governance consistently across heterogeneous systems.

As you evaluate options for maximizing returns from your data, I recommend considering if a data fabric approach aligns with your business goals. Feel free to reach out if you need any guidance! I‘m glad to help.

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