Big Data Consulting
Services

An enterprise with EU + US operations requested a compliance audit across fragmented data flows.

• We mapped all ingestion and storage logic to GDPR and HIPAA standards
• Identified violations tied to region-based routing and expired retention policies
• Delivered a remediation roadmap with automated validation triggers

Their compliance backlog was cleared in under 6 weeks, avoiding regulatory review escalation.

A data-heavy enterprise struggled with duplicated BI tools across departments.

• Conducted usage and dependency mapping across 14 tools
• Consolidated logic into 3 core systems with role-based views
• Introduced shared schema standards for all analytics dashboards

Data clarity increased, team friction dropped, and licensing costs were cut by 47%.

A client with over 90 TB of structured and semi-structured data planned to migrate.

• Assessed pipeline fragility and downstream schema breakage risks
• Designed modular ingestion logic and incremental migration phases
• Built sandbox validation frameworks for staging every delta sync

The shift occurred with zero operational downtime and maintained data trust throughout.

An enterprise needed to align its data goals across four business units and geographies.

• Facilitated discovery sessions to define system bottlenecks and data pain points
• Created a centralized data mesh roadmap with per-unit node ownership
• Added traceability protocols and unified meta-tagging standards

Cross-unit visibility improved, enabling shared KPIs and faster analytics deployment.

A legacy data infrastructure in Insurance required re-architecture.

• Audited physical servers, ETL jobs, and latency metrics
• Proposed hybrid patterns using Snowflake and existing BI tools
• Embedded SOC 2 and industry-specific controls at every transition layer

The org moved from overnight syncs to near real-time pipelines with legal-grade compliance baked in.

A pharmaceutical company needed to segment vast datasets by legal and risk sensitivity.

• Ran metadata fingerprinting across 120M+ records
• Applied jurisdiction-aware tags and risk scores
• Designed partitioned storage based on data class and regulatory flags

Sensitive data was isolated and secured, while operational data remained fast and lightweight.

An e-commerce client expanding to 5 regions needed consistent onboarding.

• Defined a country-agnostic schema layer for product and sales data
• Built a modular data onboarding checklist with infrastructure reusability
• Standardized vendor pipelines for faster ETL setup and data ingestion

Each new country took days, not weeks, to integrate into the master data ecosystem.

A consulting firm ran critical data flows across GCP, Azure, and Snowflake—but lacked unified access.

• Identified conflicting access controls and redundant sync logic
• Rewrote inter-cloud orchestration with lineage tracing and sync boundaries
• Delivered unified monitoring for cost, latency, and error handling

Access policy breaches disappeared, and data latency was cut by 61%.

The client had 14 active ML models, but no visibility into data or cost efficiency.

• Benchmarked each model’s data footprint and prediction lag
• Mapped models to decision outcomes and organizational KPIs
• Proposed a learner modeling strategy with shared datasets and a model registry

Only 6 models were retained, with 3 retrained on cleaner data, saving over $380K/year.

A Healthcare provider had no unified view of metadata quality or usage.

• Defined schema-agnostic metadata definitions across systems
• Created validation rules, version control, and descriptive tagging workflows
• Trained internal teams to scale this across new vendor integrations

Audit time dropped by 80%, and cross-system data searchability significantly improved.