Unify fragmented customer, product, and financial data across your enterprise with MDM, Data Mesh, Data Lakehouse, and Data Warehouse architectures—achieving 85% improvement in data quality and a single source of truth.
OUR EXPERTISE
Your Partner in Data Architecture Excellence
Create a single, authoritative source for customer, product, supplier, employee, and financial master data across all enterprise systems with automated data quality, governance, and synchronization.
Design and build modern data architectures—from traditional data warehouses to hybrid Lakehouse platforms—optimized for analytics, AI/ML, and real-time reporting at petabyte scale.
Deploy domain-oriented, decentralized data ownership with federated governance, enabling business units to own and serve their data products while maintaining enterprise-wide standards.
Expert guidance on data platform selection, modernization roadmaps, migration strategies, and future-proof architectures aligned with your business growth and technology evolution.
Create a single, authoritative source for customer, product, supplier, employee, and financial master data across all enterprise systems with automated data quality, governance, and synchronization.
Design and build modern data architectures—from traditional data warehouses to hybrid Lakehouse platforms—optimized for analytics, AI/ML, and real-time reporting at petabyte scale.
Deploy domain-oriented, decentralized data ownership with federated governance, enabling business units to own and serve their data products while maintaining enterprise-wide standards.
Expert guidance on data platform selection, modernization roadmaps, migration strategies, and future-proof architectures aligned with your business growth and technology evolution.
Why Sylox
The Right Partner for Enterprise Data Architecture
Achieve single source of truth with automated data quality rules, deduplication, matching, merging, and golden record creation—eliminating conflicting data across systems.
Integrate customer, product, supplier, and financial master data from SAP, Salesforce, Workday, NetSuite, and legacy systems into centralized MDM hub with real-time synchronization.
90-day implementation for foundational MDM and data architecture—delivering immediate business value with phased approach for complex, multi-year transformations.
Why Sylox
The Right Partner for Enterprise Data Architecture
Achieve single source of truth with automated data quality rules, deduplication, matching, merging, and golden record creation—eliminating conflicting data across systems.
Integrate customer, product, supplier, and financial master data from SAP, Salesforce, Workday, NetSuite, and legacy systems into centralized MDM hub with real-time synchronization.
From Data Warehouse → Data Lake → Lakehouse → Data Mesh, our team has designed and built all modern data architectures at Fortune 500 scale (Amazon, Microsoft, PwC experience).
90-day implementation for foundational MDM and data architecture—delivering immediate business value with phased approach for complex, multi-year transformations.
SERVICE OFFERINGS
Customer MDM:
Single customer view across sales, marketing, service, finance with 360-degree profile
Product MDM:
Centralized product catalog, hierarchies, attributes, pricing, and lifecycle management
Supplier/Vendor MDM:
Unified supplier data with risk scoring, performance tracking, and contract management
Employee MDM:
Golden employee records integrated across HR, finance, operations systems
Financial MDM:
Chart of accounts, cost centers, GL hierarchies, legal entities across ERP systems
Data Quality Management:
Automated validation, standardization, deduplication, enrichment
Match & Merge:
Fuzzy matching algorithms to identify and consolidate duplicate records
Golden Record Creation:
Master record with best-available data from multiple sources
MDM Governance:
Data stewardship workflows, data quality scorecards, lineage tracking
Real-Time Synchronization:
Bi-directional sync between MDM hub and source systems
Enterprise Data Warehouse (EDW): Centralized repository for integrated, historical, subject-oriented data
Dimensional Modeling: Star schema, snowflake schema design for optimal query performance
Data Mart Development: Department-specific data marts (Finance, Sales, HR, Operations)
Slowly Changing Dimensions (SCD):Historical tracking of dimension changes (Type 1, 2, 3)
Fact & Dimension Tables: Optimized table structures for analytical queries
Aggregate Tables: Pre-aggregated summaries for faster dashboard performance
ETL Pipelines: Extract, transform, load workflows with data quality and error handling
Incremental Loading: Delta load, CDC, and full load strategies
Performance Optimization: Indexing, partitioning, materialized views, caching
Multi-Tenant Architecture: Isolated data warehouses for different business units or clients
Data Lake Design: Scalable storage for raw, unstructured, and semi-structured data at low cost
Zone-Based Architecture: Bronze (raw) → Silver (cleansed) → Gold (curated) data layers
Lakehouse Implementation: Unified platform combining data lake flexibility with warehouse performance
Delta Lake / Iceberg / Hudi: ACID transactions, time travel, schema evolution on data lakes
Data Cataloging: Automated metadata discovery, lineage tracking, data dictionaries
Schema-on-Read: Flexible schema definition at query time vs rigid schema-on-write
File Format Optimization: Parquet, ORC, Avro for efficient storage and query performance
Partitioning Strategy: Optimized data partitioning for query performance and cost control
Data Lifecycle Management: Automated tiering, archival, and deletion based on policies
Lake Security: Fine-grained access control, encryption, data masking
Domain-Oriented Design: Decentralized data ownership by business domains (Sales, Finance, HR, etc.)
Data Products: Self-serve, discoverable, addressable, trustworthy data assets
Federated Governance: Central governance policies with distributed implementation
Self-Service Data Platform: Enable domain teams to create and publish data products autonomously
Data Contracts: SLAs, schemas, quality guarantees for data product consumers
Computational Policies: Push governance policies to execution (vs centralized enforcement)
Data Marketplace: Catalog of available data products with lineage, quality, and usage metrics
Domain Data Teams: Cross-functional teams owning data within their domain
Interoperability Standards: Common formats, APIs, and protocols across domains
Observability: Monitoring data product health, usage, and quality
Legacy to Cloud Migration: Migrate on-premises data warehouses to Snowflake, Redshift, BigQuery
Data Warehouse Modernization: Transform legacy EDW to modern cloud-native architectures
ETL to ELT Transformation: Shift from traditional ETL tools to modern ELT patterns
Database Migration: Oracle → PostgreSQL, SQL Server → Azure SQL, MySQL → Aurora
Zero-Downtime Migration: Phased migration strategies with parallel run validation
Data Validation: Automated testing to ensure data integrity and completeness post-migration
Performance Testing: Load testing, query optimization, and capacity planning
Rollback Planning: Contingency plans and rollback procedures for migration failures
Historical Data Migration: Archive and migrate years of historical data efficiently
Application Code Refactoring: Update queries, stored procedures, and application logic
Conceptual Data Modeling: Business-level entity relationships and data concepts
Logical Data Modeling: Normalized data structures independent of technology
Physical Data Modeling: Technology-specific table designs, indexes, partitions
Data Vault Modeling: Scalable, auditable modeling for enterprise data warehouses
Anchor Modeling: Temporal, evolving data models for agile development
Graph Data Modeling: Relationship-centric models for connected data (Neo4j, CosmosDB)
NoSQL Data Modeling: Document, key-value, columnar, and graph database designs
Data Normalization: 1NF, 2NF, 3NF for transactional systems
Data Denormalization: Optimized structures for analytical performance
Metadata Management: Data dictionaries, business glossaries, lineage documentation
Explore how we’ve built modern data architectures across industries
Case Studies
Customer MDM & Data Warehouse Across 5 ERP Systems
Data Lakehouse Migration for Professional Services
Multi-Source Data Integration & Quality Management
INDUSTRY BENEFITS
Customer 360 view across banking, lending, wealth management
Regulatory reporting with auditable data lineage
Risk data aggregation and stress testing (BCBS 239)
Anti-money laundering (AML) data integration
Real-time fraud detection with streaming architectures
Patient 360 across EHR, billing, labs, pharmacy, imaging
Research data lakes for clinical trials and genomics
Claims data integration for payer organizations
Population health analytics and SDOH data
HIPAA-compliant data architectures
Product catalog MDM across channels (web, mobile, stores)
Customer 360 with online and offline purchase history
Inventory data lake for omnichannel fulfillment
Supplier and vendor master data management
Real-time inventory and pricing data lakes
Product lifecycle management (PLM) data integration
Supplier and component master data
IoT sensor data lakes for predictive maintenance
Quality data integration across plants
Supply chain visibility across ERP, MES, WMS
Multi-tenant data warehouse architectures
Product usage data lakes for analytics
Customer master data across billing, CRM, support
Marketing attribution data integration
Application performance monitoring data lakes
Client master data across projects, billing, CRM
Employee master data for resource planning
Project data warehouse for portfolio analytics
Knowledge management data lakes
Time and expense data integration
Our Tools
Achieve a single source of truth with MDM, Lakehouse, or Data Mesh architectures designed for scale. Schedule a consultation to discuss your data architecture goals.
Achieve a single source of truth with MDM, Lakehouse, or Data Mesh architectures designed for scale. Schedule a consultation to discuss your data architecture goals.
