Imagine an online retailer that struggles to get a comprehensive view of its customers and operations because data is fragmented across product catalogs, order systems, fulfillment centers, and more. While the business has invested heavily in analytics, data silos limit the insights that analysts can extract.
This is where a modern data fabric architecture comes in. Data fabric creates a unified data layer to break down silos and maximize the value of analytics.
As the chart in Figure 1 shows, interest in data fabric solutions has rapidly increased since 2020 as companies aim to harness the power of their data.
Figure 1. Worldwide interest in data fabric has surged since 2020. [1]
Though still eclipsed by traditional ETL frameworks as seen in Figure 2, data fabric offers new ways to integrate, manage, and analyze data distributed across complex modern IT environments.
Figure 2. ETL leads data fabric in global popularity for now. [2]
This guide explores seven key use cases where data fabric architecture can enable more valuable data analytics. For each use case, we’ll look at examples of how data fabric can benefit specific industries like retail, healthcare, finance, and more.
The top seven data fabric analytics use cases are:
- Data Integration
- Data Warehousing
- Master Data Management
- Data Governance
- Cloud Migration
- Real-Time Analytics
- Artificial Intelligence
We‘ll compare data fabric to traditional integration methods and review key considerations for implementation. Let‘s get started with the first use case – unified data integration.
1. Data Integration Across Silos
With data spread across ever more systems, integration remains a pressing challenge. As Figure 3 shows, interest in data integration has held steady worldwide.
Figure 3. Consistent global interest in data integration. [3]
Data fabric delivers a modern approach to integration that tears down data silos. It abstracts the complexities of integrating disparate sources into a unified data layer.
For example, consider a bank with product data trapped in core banking systems while customer data resides in separate CRM applications and analytics databases. Data fabric can virtually consolidate these domains into integrated views.
This enables a wide range of analytics use cases:
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Customer 360: Combine customer, product, transaction, and marketing data to deeply analyze customer behaviors, risks, and needs.
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Anti-Money Laundering: Unite transaction data with customer, account, and watchlist data to detect suspicious patterns of financial activity.
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Product Portfolio Analysis: Bring together customer demographics, product details, and market data to optimize product offerings.
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Campaign Management: Merge transaction history with customer details and response models to target marketing campaigns.
In one real-world example, data fabric helped a multinational bank break down data silos to improve customer lifetime value by 15-25%. [4]
2. Cloud Data Warehousing
As shown in Figure 4, interest in cloud-based data warehousing spiked during the pandemic as businesses rapidly migrated analytics to the cloud.
Figure 4. The pandemic accelerated adoption of cloud data warehouses. [5]
Data fabric complements cloud data warehouses by providing integrated data pipelines. It acts as a logical data layer over physical storage in cloud data lakes and warehouses.
With data fabric, enterprises can build cloud analytics hubs that bring together data from across their hybrid infrastructure:
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Retail: Merge in-store sales data with online order data into a cloud data warehouse to enable cross-channel analytics.
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Media: Analyze viewership and engagement across streaming, social media, and traditional TV channels via integrated cloud analytics.
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Manufacturing: Combine IoT sensor data from machines with operational data in cloud data lakes to improve quality and output.
For example, data fabric helped a broadcasting company migrate siloed data to a cloud analytics platform, reducing reporting time from weeks to hours. [6]
3. Master Data Management
While less buzzworthy than some technologies, master data management remains relevant for authoritative definitions of critical business entities like customers, products, and suppliers.
As Figure 5 illustrates, interest in master data has held steady over the past five years:
Figure 5. Master data management maintains long-term popularity. [7]
Data fabric strengthens master data management by propagating master definitions across integrated downstream systems. This ensures shared data Accuracy across applications and analytics.
For example, in financial services, banks can centrally govern counterparty master data like corporate customers and trading partners. These "golden records" synchronize across credit risk analysis, know your customer (KYC) checks, transaction monitoring, and regulatory reporting.
Similarly, retailers can manage master product data like pricing and availability via data fabric. This flows downstream to supply chain systems, online catalogs, and in-store POS.
4. Data Security and Governance
Effective governance ensures data quality, security, and compliance across environments. As Figure 6 shows, interest in data governance spiked in 2020 with increased regulatory focus.
Figure 6. Surging demand for robust data governance. [8]
Data fabric supports governance by applying centralized policies and controls across integrated data flows. For example:
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Role-based access controls on sensitive data like healthcare records or financial transactions.
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Encryption of personal data to ensure compliance with regulations like GDPR and CCPA.
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Data masking to anonymize sensitive fields for analytics while preserving privacy.
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Metadata management for visibility into data lineage across systems.
With consistent governance, organizations can confidently open up more data for analytics while safeguarding sensitive information.
5. Migrating Data Pipelines to The Cloud
The disruptions of 2020 led enterprises to accelerate their move to the cloud. As Figure 7 shows, interest in cloud migration spiked during the pandemic:
Figure 7. The pandemic accelerated interest in cloud migration.[9]
Data fabric supports incremental migration of data pipelines and workloads. With data fabric, enterprises can keep some applications on-premise while shifting others to the cloud. This avoids a disruptive "big bang" migration.
For example, media companies can leave video production systems on-premise while migrating customer data to cloud data lakes. Finance firms can retain trading systems in colocated data centers while shifting risk analysis to the cloud.
Data fabric seamlessly spans these hybrid environments to keep data flowing between systems. This enables a smooth transition to cloud analytics.
6. Real-Time Data Analytics
While batch ETL pipelines handle periodic analytics, data fabric powers real-time analytic applications:
Figure 8. Common real-time analytics use cases enabled by data fabric.
By integrating disparate live data streams, data fabric acts as a distributed messaging backbone for stream processing. This powers real-time analytics across use cases like:
- Fraud detection in banking and payments
- Alerting in industrial IoT environments
- Personalization in retail and media
- Inventories in supply chain systems
For instance, a telecom used data fabric for real-time analytics that boosted mobile ad revenue by 25%. [10]
7. AI and Machine Learning
Finally, data fabric can feed more data to drive artificial intelligence and machine learning initiatives. The integrated data fabric acts as a virtualized data lake to fuel advanced analytics.
As Figure 9 shows, interest in AI grew steadily over the past decade before accelerating in recent years:
Figure 9. Surging interest in artificial intelligence and machine learning. [11]
With data fabric, organizations can leverage AI/ML to uncover insights across diverse datasets:
- Predictive maintenance algorithms that combine sensor data with maintenance logs and weather data.
- Chatbots that analyze customer support transcripts along with CRM data.
- Image recognition models built using aggregated image libraries and metadata.
- Recommendation engines fueled by integrated customer, product, and transaction data.
For example, a streaming media firm used data fabric to improve AI-driven recommendations. This increased streaming hours by 12%. [12]
Key Advantages Over ETL Frameworks
While legacy ETL pipelines help move data from point A to point B, data fabric creates a virtualized data layer to unlock more value from analytics.
Data fabric minimizes data replication. It provides access to integrated data without having to move and duplicate it across data warehouses. This reduces infrastructure costs.
Data fabric enables real-time use cases. By logically integrating live data streams, it powers stream processing and real-time analytics not feasible with batch ETL.
Data fabric simplifies bridging hybrid infrastructure. It readily spans on-premise systems and cloud environments to support incremental migration.
Data fabric federates queries. Rather than centralizing data, it can distribute processing across systems while unifying results. This achieves flexibility not possible in centralized data warehouses built via ETL.
However, ETL may still excel at use cases like periodic bulk data loads that do not require low-latency access or query federation. The right choice depends on an organization‘s specific analytics requirements and existing infrastructure.
Key Considerations for Implementation
When planning to implement a data fabric architecture, keep these best practices in mind:
- Start with high-value use cases to demonstrate quick wins and build momentum.
- Assess existing infrastructure like ETL pipelines to integrate rather than replace.
- Modernize incrementally across domains and systems to minimize disruption.
- Define unified data models and semantics for consistency.
- Centralize governance policies to enforce compliance across environments.
- Automate tasks like schema changes to ease ongoing management overheads.
This guide has explored seven ways data fabric architecture can empower analytics across common use cases like customer 360 insights, cloud migration, master data sharing, real-time processing, and AI/ML initiatives.
While still an emerging approach, data fabric solves pressing problems around fragmented data, governance, hybrid infrastructure, and the shift to real-time analytics.
To determine if data fabric is right for your organization, evaluate your analytics roadmap and infrastructure maturity. Quickly materializing high-impact use cases can demonstrate data fabric‘s value and build adoption across your enterprise analytics community.
To discuss your analytics goals and data integration needs, reach out to our experts: