Explore how a data fabric architecture can transform data into a strategic asset, propelling organizations ahead through automation, AI and analytics initiatives.

1. Fueling Next-gen
Data Management
with Data Fabric
Basavaraj Darawan
Senior Director, Analytics, WNS Triange
Sekharamantri Uday Kiran
Director, Analytics, WNS Triange

2. 01
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The global economic landscape is showing
signs of recovery. Yet, the International
Monetary Fund's latest update for the
second half of 2023 cautiously warns that
we are not entirely out of the woods.1
This
characterization aptly captures the persistent
underlying tension and continual fluctuations
in market conditions as uncertainties linger
and advanced economies grapple with a
deceleration in economic activities.
This balance still tilts toward the downside.
The fiscal stimuli that drove the
post-pandemic global economic resurgence
have inadvertently left behind a trail of
imbalances capable of triggering
macroeconomic volatility. Meanwhile, the
escalating geoeconomic fragmentation,
which has led to the emergence of competing
economic blocks, further threatens the
stability of emerging economies.
Amid this unpredictability, businesses must
weave agility and resilience into their
operating fabric by harnessing
comprehensive data insights. Navigating the
winds of change – supply chains, demand
patterns or policy shifts – demands early
detection and decisive action fueled by
precise decisions rooted in meticulously
curated, trusted data.
1
International Monetary Fund

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The pursuit of heightened agility necessitates
data democratization – a paradigm that
empowers business users with data accessibility
that is intelligible and actionable. This access
transforms into a formidable tool, capable not
only of problem-solving but also of igniting
innovation. It stimulates curiosity among
employees, motivating them to tinker with data
and explore novel possibilities.
However, amid the burgeoning deluge of data
and the proliferation of disconnected data silos,
the endeavor to facilitate reliable data access has
become a progressively intricate challenge. While
data lakes have been embraced as repositories for
diverse data sets, their deployment is increasingly
dispersed across both on-premise and cloud
infrastructures, exacerbating the silo conundrum.
Hence emerges the imperative for a data solution
that transforms data into a strategic asset,
propelling organizations forward through
amplified automation. Enter the data fabric: a
dynamic, interconnected solution that adeptly
knits disparate data sources – from data lakes to
data warehouses and NoSQL repositories – to
create a unified data view. This fabric, woven
with intelligence and security, empowers
self-service exploration and supports the
realization of Artificial Intelligence (AI) and
advanced analytics initiatives.
A recent global data and analytics survey by
Forrester Consulting, commissioned by WNS
Triange, revealed that within organizations with
advanced data and analytics maturity, a pivotal
foundation emerges: data architecture is based
on big data fabric. An astounding 92 percent of
digital-only businesses, distinguished by their
adept utilization of advanced data and analytics
practices, have anchored their data architecture
on the robust framework of big data fabric.
Data Fabric: Catalyzing Data
Democratization

4. Gartner foresees a landscape where
organizations leveraging active metadata to
enrich and orchestrate dynamic data fabrics
stand to reduce their integrated data delivery
timelines by 50 percent and enhance data team
productivity by 20 percent by 2024.2
The data
fabric accomplishes this feat by enabling
enriched analytics on data and reporting assets.
Central to this transformation is an active catalog
model, which not only facilitates the design and
automated deployment of integrated data but
also spans across on-premise, cloud, hybrid and
multi-cloud environments. The business
imperatives fulfilled by the data fabric are
multifarious, encompassing:
■ Integrated security and governance, which has
become vital in the wake of new privacy
regulations and escalating security breaches
■ Empowered access to high-integrity data to
support AI applications for quick, well-informed
decisions
■ Elevated security posture attained through a
cohesive data architecture spanning physical
and cloud environments
■ Augmented visibility and transparency to
support data integrity and quality issues
■ Accessible data mart that promotes
participation of citizen developers, stoking a
spirit of experimentation with diverse data
sources and novel model creation
2
Gartner
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The Ascendance of
Data Fabric in the
Modern Enterprise

5. An enterprise data fabric dismantles the barriers to effective data utilization, unifying disparate sources
with an intelligent platform to support new and emerging use cases. Specifically, a data fabric empowers
with the following compelling capabilities:
Dynamic Data Integration Backbone:
A fusion of data sources encompassing batch, streaming, replication,
messaging and microservices is key to data fabric. Intelligent data
mapping, driven by metadata, bridges the gap between IT and
business demands.
Holistic Metadata Integration:
At the core of the dynamic data fabric lies a meticulously interconnected repository
of metadata. This metadata convergence empowers the fabric to interlace technical
and operational facets with business context, establishing a potent data catalog.
Active Metadata Integration / Data Plasticity:
Dynamic integration of evolving data creates an active catalog, which helps define a
graph-based model to track key metrics and statistics. Graph-based metadata helps easily
understand patterns based on relationships across business entities. AI / Machine Learning
(ML) models, nurtured by metadata, help provide advanced forecasts, enhancing data
management and integration while invigorating the existing information landscape.
Analytics-enriched Knowledge Graph:
A knowledge graph, synergistically intertwined with graph marts, adds
semantic value to the data, aligning interconnected datasets. This
semantic layer invigorates analytics, rendering data meaningful. This, in
turn, empowers ML models to extract profound insights for analytical
and operational management.
Empowered Data Ownership:
Integral to the data fabric is the articulation of data ownership and lineage, aligned
with industry compliance and robust security. It simultaneously safeguards personal
privacy and enterprise integrity through a governance framework tailored for
hyper-intensive data utilization fueled by AI / ML, the Internet of Things (IoT) and
other emerging technologies.
AI-enabled Data Security Features:
Identifying sensitive data locations and classifying them based on content or
behavior forms the bedrock of data fabric security. Certified data assets, and
controlled access and robust audit mechanisms round out the comprehensive
security suite.
Excelling with Data Fabric
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6. When conceptualizing data fabric architecture, a
user-centric lens is paramount. Purpose-built
modules, catering to diverse tasks and user
categories, create a seamless environment for
data discovery, reliance and utilization.
A well-crafted data fabric integrates intelligence
with data quality and master data management
prowess, orchestrating data processing across
on-premise and public cloud landscapes.
Automation, coupled with Kubernetes
integration, lends operational agility to the data
realm.
The dynamic fusion of active metadata fuels the
identification and integration of new data
elements. This fusion facilitates intelligent data
Crafting the Canvas of Data
Fabric Architecture
tagging, aligning data with business glossaries
and dictionaries for nuanced interpretation.
A hallmark of the data fabric architecture is its
graph-based knowledge engine, driving swift
data analysis through blended graph data
models. This innovation provisions analytical
cubes and empowers cross-functional reports
with enhanced visual lineage tracking.
Bolstering data fabric security entails the
calibration of robust controls to accommodate
next-generation analytics. The data fabric unearths
clean, high-quality data, bolstering ML model
efficacy. The fabric also positions data as a product,
creating a user-friendly data marketplace enriched
with search and consumption capabilities.
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Enterprise Data
Sources
AI and ML
Analytics
Tools
Data
Market
Place
Search
Data Virtualization
Graph-based Metadata Data Management, Governance & Lineage
Data Security
Batch
IoT /
Real-time
Data Integration
Data Storage
Data Lake Infrastructure
Data
Quality
Master
Data
Management
Active Metadata Integration
Automated Deployment and Operations with Kubernetes
Knowledge Graph Engine
ONBOARD
Ingest &
Map
■ Automated
ETL
■ Collaborative
Mapping
■ Metadata
Capture
■ Lift Data
into Data
Fabric
■ Connect
Data Models
■ Design
Ontologies
MODEL
Graph Data
Model
■ Align
Related
Data Sets
■ Data Layers
■ OLAP
Query
Engine
BLEND
Graph
Marts
■ Data
Available
for Analysis
■ API Access
■ Ad-hoc
Iterative
Queries
ACCESS
Hi-Res
Analytics
Figure 1: Data Fabric Framework

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As technological and business landscapes evolve,
the data fabric remains a steadfast guide in
navigating the complexities of the data-driven
epoch. Data-derived insights have transcended
luxury to become a necessity for contemporary
enterprises grappling with uncertainty. The
capacity to rely wholeheartedly on organizational
data has emerged as a cornerstone, with data
fabric architecture empowering businesses
to wield insights as strategic tools – catalysts
for decision-making, risk mitigation and
opportunity identification.
It's crucial to acknowledge that every organization
has unique requirements and is at a different stage
Conquering Uncertainty with
Data Fabric
of data maturity. Hence, the data fabric must be
tailored to address precise objectives and
challenges, culminating in a bespoke solution.
In conclusion, crafting a data fabric architecture
necessitates a user-centric ethos underpinned by
purpose-built modules serving an array of tasks
and user profiles. This approach ensures a
seamless panorama of data discovery,
dependability and application, benefiting
citizens and stakeholders alike.
To learn how WNS Triange can help you
harness data fabric to maximize data potential
and drive growth, talk to our experts.

8. with WNS
Co-create to
outperform
About WNS
WNS (Holdings) Limited (NYSE: WNS) is a leading Business Process
Management (BPM) company. WNS combines deep industry
knowledge with technology, analytics, and process expertise to
co-create innovative, digitally led transformational solutions with
over 400 clients across various industries. WNS delivers an entire
spectrum of BPM solutions including industry-specific offerings,
customer experience services, finance and accounting, human
resources, procurement, and research and analytics to re-imagine
the digital future of businesses. As of June 30, 2023, WNS had
59,871 professionals across 66 delivery centers worldwide including
facilities in Canada, China, Costa Rica, India, Malaysia, the
Philippines, Poland, Romania, South Africa, Sri Lanka, Turkey, the
United Kingdom, and the United States.
About WNS Triange
WNS Triange powers business growth and innovation for 200+
global companies with Artificial Intelligence (AI), Analytics, Data and
Research. Driven by a specialized team of over 6000 analysts, data
scientists and domain experts, WNS Triange helps translate data
into actionable insights for impactful decision-making. Built on the
pillars of consulting (Triange Consult), future-ready platforms
(Triange NxT), and domain and technology (Triange CoE), WNS
Triange seamlessly blends strategy, industry-specific nuances, AI
and Machine Learning (ML) operations, and intelligent cloud
platforms.
Driving a futuristic edge are WNS Triange’s modular cloud-based
platforms and solutions leveraging advanced AI and ML to provide
end-to-end integration and processing of data to actionable
insights. WNS Triange leverages the combined strength of WNS’
domain expertise, co-creation labs, strategic partnerships and
outcome-based engagement models.
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