Establishing enterprise-grade data foundations for analytics, AI, and compliance
In today's data-driven world, the strength of an organization's data foundation determines its ability to innovate, comply, and compete. Yet, for most financial institutions, data landscapes remain fragmented — burdened by legacy systems, inconsistent quality, and redundant pipelines. At Eklogi Consulting, we help organizations design and operationalize modern data management frameworks that unify, govern, and mobilize data across the enterprise.
Banks and financial services organizations operate with thousands of data sources — from core systems to digital channels and third-party integrations. The resulting silos create inefficiencies, reporting inconsistencies, and compliance risk. A robust data management strategy unifies this fragmented landscape, creating a single, trusted version of truth across business lines.
Multiple systems with inconsistent data definitions and lineage gaps
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Duplicates, errors, and stale data impacting analytics accuracy
Ambiguity around accountability for data domains
Our Enterprise Data Management (EDM) framework provides a structured path to build, operate, and optimize modern data ecosystems. It integrates architecture design, governance, and automation to ensure data is accurate, available, and actionable — at scale.
Creating a unified, future-ready data architecture. We start by designing data architectures that integrate structured, semi-structured, and unstructured data across systems — ensuring consistency and scalability.
Enterprise Data Model Design: Standardized data schemas with cross-domain integration points.
Cloud-Native Architecture: Design of hybrid or multi-cloud data foundations using Azure, AWS, or GCP.
Data Integration Strategy: Transition from legacy ETL to event-driven and API-based data pipelines.
Real-Time Data Streaming: Implement Kafka, Flink, or Snowpipe for low-latency processing.
Data Virtualization Layer: Enable federated access for analytics and regulatory reporting.
Establishing a single source of truth for critical business entities. We design and implement MDM frameworks that ensure consistent, accurate, and complete master data across products, customers, and transactions.
Data Domain Definition: Identification of core master data domains and ownership.
Entity Resolution: Deduplication, consolidation, and enrichment using deterministic and probabilistic matching.
Golden Record Creation: Unified customer and product views across systems.
Workflow & Stewardship Enablement: Role-based review and approval processes.
MDM Technology Enablement: Evaluation and deployment of leading platforms like Informatica, Reltio, or Talend.
Ensuring accuracy, reliability, and completeness of enterprise data. We embed data quality management into both operational and analytical pipelines.
Data Quality Framework: Define and automate rules for accuracy, completeness, timeliness, and consistency.
Quality Dashboards: Real-time monitoring of data health across systems and domains.
Root Cause Analysis: AI-driven anomaly detection and impact tracing.
Data Remediation Workflows: Automated correction and notification mechanisms.
Continuous Improvement: Integration with metadata and governance systems for feedback loops.
Making data discoverable, traceable, and compliant. We build metadata ecosystems that improve transparency and control over data assets.
Enterprise Data Catalog: Central repository of data assets, owners, and business definitions.
Data Lineage Visualization: End-to-end traceability from source to consumption.
Business Glossary: Harmonized definitions for key metrics and attributes.
Integration with BI Tools: Metadata-driven automation for dashboards and reports.
Governance Integration: Link metadata with data privacy and risk management processes.
Operationalizing agility and scalability in data delivery. Our DataOps practices bring DevOps principles to data engineering — enabling faster, more reliable data delivery.
Pipeline Orchestration: Automated ingestion, transformation, and validation.
CI/CD for Data: Version control, automated deployment, and rollback for data workflows.
Monitoring & Alerting: Real-time operational visibility for data pipelines.
Integration with AI Models: Seamless data provisioning for ML training and inferencing.
Automation at Scale: Use of AI to detect schema drift, optimize transformations, and manage anomalies.