Radixiy Inc.Claimed by Official Representatives

A logistics company operating across multiple business units and transportation networks was generating high-volume operational data across a fragmented technology stack TMS platforms, WMS systems, ERP modules, CRM tools, and a mix of legacy reporting databases. No canonical data layer existed. Each system held its own version of "truth," reporting pipelines were largely manual or semi-scripted, and there was no consistent mechanism for schema management, lineage tracking, or data quality enforcement across sources. The engagement scope was to design and implement a centralized data platform capable of near real-time ingestion, transformation, and governed access replacing ad hoc ETL scripts and manual reconciliation with a scalable, observable architecture.

Challenge

1. Data fragmentation across heterogeneous sources - Operational data was distributed across transactional databases, SaaS application APIs, flat-file exports, and legacy on-prem systems with inconsistent schemas and no shared identifiers across systems requiring entity resolution and key mapping before any unification was possible.

2. Manual, brittle ETL processes - Existing pipelines were largely point-to-point scripts with no orchestration layer, no retry/failure handling, and no lineage visibility. Schema drift in source systems routinely broke downstream reports without warning.

3. No single semantic layer - The same business metric (e.g., "on-time delivery rate") was calculated differently across departments due to divergent business logic embedded in disparate reporting tools, with no shared metric definitions or governed KPI layer.

4. High latency between event occurrence and availability for analysis - Batch cycles ran on a 24–48 hour cadence, which was inadequate for operational use cases like exception management, carrier performance monitoring, and SLA breach detection.

5. No scalable foundation for advanced analytics - The architecture had no support for feature engineering, model scoring, or streaming use cases a hard constraint given the client's roadmap toward predictive ETA modeling and dynamic routing optimization.

Solution

1. Data Landscape Assessment & Source Profiling We conducted a full technical audit of source systems: schema profiling, data volume and velocity analysis, API rate-limit and access-pattern review, and an inventory of existing integration jobs. This produced a source-to-target mapping and a prioritized ingestion roadmap based on business criticality and technical feasibility.

2. Layered Data Platform Design We designed a multi-zone data architecture following a raw - curated - semantic (consumption) pattern, commonly implemented as a lakehouse model:

3. Raw/Landing zone — immutable, schema-on-read ingestion of source data in native or near-native format, partitioned by source and ingestion timestamp for full auditability and replay capability.

4. Curated/Silver zone — cleansed, deduplicated, conformed data with standardized types, enforced schemas, and applied business keys for cross-source joins.

5. Semantic/Gold zone — governed, business-ready datasets and metric definitions exposed to BI tools, ad hoc query engines, and downstream applications via a consistent access layer.

This zoning strategy decoupled ingestion from transformation logic, allowing schema evolution and reprocessing without destructive rewrites upstream.

1. Ingestion & Orchestration (i) Implemented change data capture (CDC) and incremental extraction patterns for transactional source systems, avoiding full-table reloads and reducing source-system load. (ii) Built ingestion connectors for API-based SaaS sources with pagination handling, backoff/retry logic, and idempotent writes to prevent duplication on job restarts. (iii) Introduced a workflow orchestration layer to manage DAG-based pipeline dependencies, enforce SLAs per pipeline stage, and provide centralized monitoring, alerting, and lineage tracking across all jobs. (iv) Standardized transformation logic using a modular, version-controlled SQL/dbt-style transformation framework, enabling testable, documented, and peer-reviewed data models rather than opaque script-based logic.

2. For latency-sensitive use cases, we implemented a micro-batch/streaming hybrid pattern: (i) High-priority event streams (shipment status changes, exception events, carrier telemetry where available) were processed via streaming ingestion with sub-minute to few-minute latency targets. (ii) Lower-priority, high-volume dimensional data continued on scheduled incremental batch cycles, balancing infrastructure cost against freshness requirements. (iii) Materialized views and pre-aggregated tables were introduced in the semantic layer to keep dashboard query performance consistent as data volume and concurrency scaled.

3. Data Governance & Semantic Standardization (i) Defined a centralized metric and dimension catalog (a governed semantic layer) so that KPIs such as on-time delivery rate, dwell time, and cost-per-shipment had a single, version-controlled definition consumed consistently across all reporting tools. (ii) Implemented schema validation and automated data quality checks (completeness, uniqueness, referential integrity, freshness SLAs) at each zone boundary, with failed-check alerting routed to data engineering before bad data could propagate downstream. (iii) Established data lineage tracking from source system to consumption layer, enabling root-cause tracing when metrics were questioned, a key factor in rebuilding organizational trust in reported numbers. (iv) Applied role-based access controls and column-level masking on sensitive fields (rate data, customer PII) to satisfy internal governance and compliance requirements.

Results

1. Latency reduction - Operational data availability moved from a 24–48 hour batch cycle to near real-time (sub-minute to few-minute) for priority event streams, enabling exception-based operational workflows that weren't previously feasible.

2. Consistent metric definitions - A single governed semantic layer eliminated department-level discrepancies in KPI calculation, reducing time spent reconciling conflicting reports.

3. Reduced manual engineering overhead - Automated orchestration and CDC-based ingestion eliminated the majority of manual reconciliation work previously required to keep reports current, freeing engineering capacity for higher-value work.

4. Improved data trust and auditability - End-to-end lineage tracking and automated quality checks gave both technical and business stakeholders visibility into data provenance, directly addressing prior skepticism around reported metrics.

5. Foundation for advanced analytics - The layered architecture with clean, governed, timely data already in place positioned the organization to begin predictive ETA modeling and other ML-driven initiatives without needing to re-architect the underlying data layer.

A hospitality group operating multiple hotel brands, properties, booking channels, and loyalty programs was scaling fast. But with every new property, system, and digital channel added, guest and property data grew more fragmented and the organization needed a single, trusted view of its guests, properties, and operations across the enterprise.

Challenge

Guest and property information lived across multiple platforms and systems with no single source of truth connecting them.

1. Duplicate guest profiles
2. Inconsistent property and room data
3. Fragmented loyalty records
4. Conflicting reports across systems
5. Increasingly complex integrations
6. Limited ability to personalize the guest experience

At the core of it all: the same guest could be recognized differently depending on where they showed up.

Solution

Our Team designed a Hospitality Master Data Management (MDM) framework to connect the organization's critical data sources and establish governed Golden Records across the full guest and property hierarchy:

1. Data standardization
2. Entity matching & deduplication
3. Survivorship rules
4. Master-data hierarchies
5. Data governance
6. API & system integration
7. Continuous data-quality monitoring

Results

A single trusted data foundation 360 degree view, ready to be consumed by CRM, loyalty, analytics, marketing, and AI platforms alike.

1. Consistent property data
2. Sharper guest personalization
3. Faster, cleaner integrations
4. Stronger enterprise reporting
5. An AI-ready data foundation