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.
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.
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:
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.
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 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.
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 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.
International Monetary Fund
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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