Data is the lifeblood of the modern organization. As data volumes grow exponentially, integrating and managing data from disparate sources becomes increasingly critical – yet challenging. This is where data fabric comes in.
Data fabric refers to an interconnected data architecture that breaks down data silos to create a single, consistent data environment. By virtually or physically integrating data from multiple systems and sources, data fabric empowers organizations to consume, manage, and leverage data more efficiently.
Data fabric breaks down data silos for unified access. Credit: Fauxels from Pexels
As per Allied Market Research, the global data fabric market is projected to reach $4.5 billion by 2031, growing at a CAGR of 22.6% from 2022 to 2031. This growth will be fueled by the surging need for better data integration, analytics, and management across industries.
In this article, we explore the 8 key benefits of data fabric and how major industries from agriculture to manufacturing can leverage data fabric architecture to enhance operations, analytics, and decision making.
1. Enhanced Data Quality
With data coming from various operational systems and external sources, issues like inconsistencies, inaccuracies, duplication, and staleness are commonplace. Data fabric helps establish trust in enterprise data by breaking down data silos and enabling a single source of truth.
Centralized data curation, management, and governance policies enabled by data fabric radically improve data hygiene. Organizations can consume high-quality data that is consistent, complete, unified, and reflects a single version of the truth.
Consolidating data improves accuracy and consistency. Credit: Scott Graham on Unsplash
2. Increased Operational Efficiency
Data fabric enables organizations to tap into integrated data from across all business units and external sources. This powers data-driven decision making, gives way to valuable data insights, and helps automate manual processes – all leading to higher productivity and efficiency.
As an example, data fabrics reduced manual reporting efforts by over 50% for a healthcare provider by creating a unified data foundation. The integrated data asset enabled embedded analytics and process automation using technologies like AI/ML.
Automation and analytics improvements from data fabric architecture enhance operational efficiency. Credit: Carl Heyerdahl on Unsplash.
3. Enhanced Data Traceability and Observability
With data coming together from all enterprise systems, data fabric makes the entire data pipeline fully observable. Organizations can trace data from its origin through various transformation states to its final analytics output.
Data fabric gives full visibility into data health, movement, and modification. This is invaluable for debugging issues and ensuring compliance with data regulations. Moreover, absolute data traceability improves trust in analytical insights and models.
Integrating data gives organizations full observability into their data. Credit: NESA by Makers on Unsplash
4. Flexible and Democratized Data Access
Data fabric breaks down data silos and integrates data from across the organization into a cohesive layer. This enables flexible, democratized access to data through modern interfaces.
With data fabric, any stakeholder can access integrated, unified data relevant to their role. Data consumers are no longer limited or dependent on specific applications or repositories. This self-service access gives rise to valuable cross-departmental analytics.
Data fabric enables self-service access to data. Credit: Solen Feyissa on Unsplash
5. Agile Data Exploration and Analytics
With data integrated from across the organization, users can slice and dice data however they want to uncover key insights. Data fabric allows analyzing enterprise data in the context of department-specific data, unlocking new cross-functional relationships and trends.
The unified data foundation makes way for complex exploratory analysis without moving terabytes of data across repositories. This powers agile, iterative analytics at scale. Pinpoint insights can be actioned immediately to drive innovation.
Data fabric enables exploratory analysis at scale. Credit: Jason Goodman on Unsplash
6. Easy Adoption of New Data Sources
As enterprises accelerate digital transformation, adopting new data sources and types is essential for growth. However, piecing together analytics and business logic on incrementally newer datasets can be challenging.
Data fabric provides standard interfaces to quickly onboard any internal or external data feed. New data automatically becomes available enterprise-wide in relevant contexts. This drives faster adoption of new signals like IoT data, wearable device data, sentiment data, and more to stay competitive.
7. Improved Data Security and Compliance
By consolidating data from multiple systems into a virtual or physical data layer, data fabric enables unified data security, access control, and compliance policies. Sensitive data is protected in an integrated manner across the organization.
Moreover, with full data traceability, organizations can easily analyze audit logs and access history to prove compliance during audits. Data protection regulations like GDPR also become easier to implement.
Consolidating data allows unified data security policies. Credit: Christian Lue on Unsplash
8. Lowered Data Storage and Processing Costs
Data fabric enables storing data in lower cost tiers while presenting users with a virtual data layer for consumption. The virtualization decouples data backend from frontend consumption, allowing the use of cost-optimized data lakes.
Moreover, by preventing redundant copies and synchronizing security policies, data fabric reduces data sprawl across repositories. This cuts down data infrastructure, management, and compliance costs.
With these overarching advantages across usability, manageability, cost, and performance, data fabric delivers immense value. In the next section, we discuss the key use cases and benefits of data fabric for major industries.
Adoption of data fabric architecture and integrations varies across industries based on specific data challenges and analytics needs. Below we explore how some of the leading industries are leveraging data fabric to unlock value.
Agriculture
The agriculture sector generates massive volumes of machine sensor data, weather data, soil nutrient data, yield data, and more. Analyzing this multi-structured data in context has the potential to greatly enhance farming outcomes through data-driven decision making.
Data fabric allows farms to integrate and centrally store all their operational, IoT, GIS, weather, and external data. Farmers gain access to unified farm data lakes that can power advanced analytics for precision agriculture. Key use cases and benefits include:
- Unified view of sensor, weather, farm operation data for better crop planning
- Combined weather data analysis with historical crop yield data to guide key farming decisions on exact water, fertilizer, pesticide needs optimizing for yield quality, crop health, and climate resilience
- Easy integration of satellite imagery data for advanced farm insights around soil variability within fields to zone-in on irrigation, crop rotation etc.
- Improved traceability and auditability of farming activities required for food certifications
Banking
Banks handle large volumes of customer data from core banking platforms, credit card transaction systems, channel interactions, financial advisory platforms, and many other application-specific repositories. Without a unified customer data foundation, key processes suffer –
- Customer onboarding cannot leverage insights from relationship history
- Wealth managers cannot recommend comprehensive plans
- Loan providers analyze risk in a siloed manner
- Detecting identity thefts and financial fraud becomes difficult
Data fabric in banking helps create the crucial single customer view – integrating data from all interactions irrespective of channel or product line. This drives exponential efficiency improvements and growth opportunities.
Data fabric helps banks gain a unified view of customer data across products. Credit: Kelly Sikkema on Unsplash
Education
Educational institutions capture extensive data around students, faculty, courses, assignments, grades, alumni records, and more. However, critical analysis suffers due to disconnected systems. Institutes stand to gain significantly by unifying campus data.
Potential use cases powered by education data fabric include –
- Analyze grades in combination with demographics, attendance, engagement metrics, and socioeconomics for insights into improvement areas
- Design personalized curriculum and mentoring programs based on identified strengths/weaknesses
- Streamline administration by tracking students across applications – transport, library, access control systems
- Support admissions by holistically assessing applicants across various qualification criteria
- Optimize hiring based on unified view of faculty research productivity, feedback, and workload
Energy & Utilities
Energy companies like power generators and utility providers leverage vast sensor data from production, transmission, and distribution infrastructure. Integrating this OT data with business KPIs and external signals is key to ensuring stability, efficiency, safety, and reliability of critical national infrastructure.
Key aspects benefiting from data fabric include –
- Combine equipment sensor data with weather forecasts to optimize renewable energy production
- Correlate usage spikes with local events to analyze demand better and improve load balancing
- Overlay outage data with environment, grid, and pipeline data to determine failure causes
- Unify customer data from billing systems, contact center logs with tech support calls to improve services
- Implement data protection rules like data residency uniformly across the enterprise
Financial Services
Banks, insurance agencies, investment firms, and other financial institutions build data fabrics to unlock the power of data-driven insights while managing tight compliance needs.
Key financial use cases around client analytics, fraud detection, and portfolio management leverage the unified data foundation provided by data fabrics.
- Single customer/household view – Map relationships across individuals, organizations, beneficiaries, and holdings irrespective of product lines
- Sentiment analysis – Combine quantitative transaction data with qualitative feedback data from multiple channels to enhance CX and product positioning
- Risk analysis across asset classes – Model macro/micro economic variables against historical instrument performance to manage risk exposure
- Detecting insider trading – Correlate trade logs with employee actions and external news/social media threads to identify fraudulent behavior
Government
Public sector agencies like transportation, taxation, utilities, law enforcement, and local governments handle extensive citizen documentation. Managing this data is challenging due to legacy architectures. Data fabric is becoming fundamental to public digital transformation initiatives.
Some high-value use cases are:
- Master citizen record – Assign unique identity across agencies and demographic/address changes
- Data exchange – Securely share KYC, income, permits, case-history data across departments to improve services
- Compliance – Enforce uniform data retention, access policies across agencies
- Law enforcement – Combine crime incident data with camera feeds, public records, and watchlist data for preventive enforcement
Healthcare
Healthcare providers manage protected health information across various point solutions – EHR, billing, imaging, lab tests, pharmacy, medical devices etc. This fragmentation introduces patient safety risks. Data fabric helps protect data while enabling usage for highest quality care.
Key scenarios positively impacted are:
- Patient history – Clinicians can quickly review full diagnosis details without piecing fragmented records
- Clinical decision support – Live recommendations based on patterns identified from population-level integrated data
- Public health – Unified health records from across hospitals can help detect disease outbreaks through cluster analysis
Insurance
Insurance providers rely on massive amounts of semi-structured and unstructured data from domains like health, automotive, home, life, and business insurance. Key to harnessing insights is the ability to correlate data across domains.
This enables advanced applications like:
- Hyper-personalization – Combine individual risk models, qualitative feedback, and behavioral data to offer tailored premiums/deductibles
- Cross-selling – Identify coverage gaps through holistic portfolio views across families and businesses
- Claims analysis – Accelerate processing by tracing policy, medical history, accident forensics data
- Risk modeling – Develop models leveraging aggregated data while preserving privacy
Retail & eCommerce
Modern retailers integrate data across website interactions, inventory management, logistics, point of sale, marketing campaigns, finance systems, and more. This powers real-time decision making and optimizations.
Data fabric enables use cases like:
- Personalization – Serve web experiences based on customer data from CRM, email campaigns etc.
- Smart supply chain by correlating sales, inventory, transportation, and weather data
- Dynamic pricing by instantly analyzing competitor prices and local demand signals
- Omnichannel experience by bridging data across in-store, web, mobile, and call center interactions
As evident, data fabric drives tremendous value across industries by enabling a unified view of enterprise data. Core benefits around analytics, visibility, governance, reliability, and model building have a wide-ranging impact on business functions.
With exceptional improvements in efficiency, innovation potential, and decision making, data fabric adoption is expected to grow exponentially over the next decade. Organizations must evaluate data fabric as a key enabler of data-first strategies for guaranteed success.
