Data moves up the agenda when business demands outgrow the estate behind it

Today, data is actively moving from reporting into the workflow itself. It now shapes product behavior, operational decisions, compliance evidence, and secure exchange beyond enterprise boundaries. That raises the bar for what data foundations now need to support.

Enable production-grade AI workflows

AI workflows stop scaling when every use case requires separate context enrichment, labeling, and validation logic. The requirement shifts toward reusable annotation pipelines, governed retrieval paths, and feedback loops that support copilots and agentic workflows without rebuilding context each time.

Power data-driven product behavior

Once telemetry, usage signals, session flows, and service events become part of the product experience, the data layer needs to support low-latency ingestion, event consistency, journey analytics, and API-ready outputs that can directly trigger product behavior, SLA actions, personalization flows, and retention playbooks.

Ensure quality, control, and compliance

As more teams and workflows rely on the same data, stewardship, lineage, privacy controls, and audit-ready evidence need to be built directly into pipelines and consumption layers. The challenge is keeping quality and compliance in place without every release turning into a review cycle.

Support secure exchange and monetization

When data moves across suppliers, customers, platforms, or sector data spaces, access rules, revocation, and auditability become part of the engineering scope. This creates the foundation for APIs, benchmark services, and federated data products that can be securely shared, reused, and monetized.

Sigma Software turns fragmented data capabilities into production-ready systems the business can trust and expand

Build the data backbone

Bring fragmented data under control as the organization, systems, and requirements evolve, while keeping the cost and effort of adaptation under control.

We help you to:

  • Bring new data into the backbone through data source integration and ETL/ELT pipelines
  • Make data movement and storage reliable through lakehouse architectures, streaming pipelines, and observability controls
  • Evolve the backbone through migration, modernization, open-core architectures, and expansion for new use cases

Activate data in products and workflows

Move data beyond dashboards into product logic, team actions, AI copilots, and natural-language interfaces so signals turn into decisions and operational triggers in real time.

We help you to:

  • Ensure data is decision-ready through enrichment, normalization, labeling, annotation, and context pipelines
  • Apply decision logic through anomaly detection, predictive models, business thresholds, and RAG-ready retrieval paths
  • Expose trusted access through APIs, orchestration, permission-aware semantic layers, and governed natural-language analytics

Control, share, and monetize data

Extend data beyond internal use into trusted exchange, reusable products, and external data services while keeping governance, access, and evidence built into every access path.

We help you to:

  • Establish control boundaries through stewardship, lineage, privacy, and evidence workflows
  • Extend trusted data access through external APIs, partner exchange, and federated data spaces
  • Package reusable data products through benchmark services, shared intelligence APIs, and product catalogs

Bringing data engineering into live business operations

case-study-tecalliance

Multi-brand automotive aftermarket data ecosystem reengineering

  • Spare parts data from 900+ brands unified
  • Robust data platform rebuilt for cloud scale
  • 90% process automation across the system
  • Dual data center migration covering 240 servers, 100 apps, and 10M+ files

Supported TecAlliance’s digital transformation program with reengineering the aftermarket platform that gathers, processes, and distributes spare parts data across a large multi-brand ecosystem. The work focused on improving efficiency and reliability while improving efficiency and reliability of data gathering, processing, and distribution across the ecosystem.

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case-study-siemens

Leading device usage data and analytics migration for CT scanner monitoring cloud move

  • 15+ years of historical CT device data migrated
  • Near real-time analytics pipelines adapted
  • Internal and client-facing data products supported
  • Delivered within a complex multi-vendor migration program

Sigma Software managed the CT device usage data and analytics layer within Siemens Healthineers’ cloud migration program, adapting the pipelines and historical data flows needed for near real-time analytics and continued support of internal and client-facing data products.

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case-study-aol

Real-time ad analytics platform scaling for AOL ecosystem integration

  • 2.5M events per second processed in real time
  • Used by 30K+ companies globally
  • Platform adapted to AOL ecosystem after acquisition
  • Built for sharply increased data volumes

Sigma Software scaled the real-time analytics platform after AOL’s acquisition of the video advertising solution, adapting the data pipelines and reporting layer to the new ecosystem while ensuring stable processing under significantly increased event volumes.

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case-study-princeton-university

Multimodal research data platform optimization for large-scale video analysis

  • 100 years’ worth of video data streamlined for analysis
  • 300% faster historical processing for core algorithms
  • Single source of truth with ~50% lower storage costs
  • CCPA-aligned observability, durability, and cross-region resilience

Sigma Software optimized the multimodal data platform behind Princeton’s First 1000 Days research initiative, re-architecting ingestion, parsing, labeling, and storage workflows to support large-scale video analysis, secure third-party data handling, and long-term reliability for future datasets in social, behavioral, and cognitive science.

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case-study-esmia

Building the data backbone for large-scale energy modeling

  • Dozens of millions of records processed in minutes instead of hours
  • Report preparation reduced to 5–10 minutes per document
  • Legacy and spreadsheet-based workflows consolidated
  • Accelerated backbone delivery with Datuum

Sigma Software built the data backbone behind ESMIA Consultants’ large-scale energy modeling workflows, replacing fragmented legacy and spreadsheet-based operations with a solution powered by Datuum product. The new setup automated consolidation, filtering, and reporting, reducing manual effort while making large datasets faster to process and easier to analyze.

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case-study-secure-finance-reporting

Secure finance reporting across fragmented data sources

  • Multiple fragmented sources consolidated into one source of truth
  • Reporting time reduced from 1 week to 1 minute through intelligent automation
  • Secure data warehouse with flexible access rights
  • Future source expansion built into the ingestion framework

Sigma Software rebuilt the client’s financial reporting flow around an automated ingestion and warehouse architecture that continuously collects data from multiple systems and formats, validates it against defined metrics, and structures it into one secure reporting backbone for business users and management.

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Data work rarely starts from a blank slate. Here is where teams usually begin

The right starting point depends on where the current data setup begins to limit progress, or where the next business priority needs new data capabilities to move forward. That usually determines the most effective way to begin.

Audit and roadmap

Assess architecture, data quality, governance gaps, compliance risks, access controls, and reporting bottlenecks to define the next practical move.

Modernize core data flows

Redesign ingestion, ETL/ELT, storage, and reporting pipelines while accelerating delivery through proven partner products and implementation patterns.

Build reusable data products

Create reusable data products, decision tools, semantic interfaces, and ML-powered applications accelerated through Sigma Software's internal product catalog and proven implementation patterns.

Establish secure data access

Design access models that control how teams, services, agents, and external consumers use data, with privacy boundaries, audit evidence, and non-human identity controls built in before data is exposed operationally.

Enable interoperable data exchange

Design repeatable exchange models for how partners, platforms, and data products share information across boundaries through APIs, connectors, and federated standards without rebuilding integrations for every new participant.

Chosen by teams that need data to scale, adapt, and stay trusted

Faster exploration and onboarding with reusable products

Sigma Software accelerates discovery, first-domain onboarding, and platform rollout through 20+ reusable internal data products, implementation accelerators, and proven delivery patterns.

Platform engineering built for real production scale

We engineer end-to-end data platforms across hyperscalers and market-leading stacks, with proven delivery under extreme throughput, low-latency, and multi-region operational load.

Governance, access, and natural-language analytics by design

Semantic access control, non-human identity boundaries, permission-aware prompting, and governed natural-language analytics are built into how data is exposed and used.

Interoperability beyond enterprise boundaries

We design data exchange for suppliers, customers, partner platforms, and federated ecosystems so new participants can connect without rebuilding integrations each time.

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The same foundation starts to look different once it meets industry reality

In AdTech, the stack changes faster than most internal platforms can absorb. New identity standards, CTV surfaces, retail media workflows, and agentic buying protocols all put pressure on the data layer to keep latency, consent, measurement, and cross-platform interoperability stable while product teams continue shipping.

We do:
  • Real-time event ingestion and low-latency decision pipelines 
  • Identity, consent, and measurement-ready data flows
  • Audience, optimization, and agentic decision data products
  • DSP, SSP, publisher, and protocol-layer interoperability
  • Privacy-safe activation aligned with GDPR, CCPA, and emerging AI-driven buying standards

Vehicle data quickly becomes ecosystem data. Once the same telemetry and service signals start driving dealer actions, predictive maintenance, fleet decisions, and spare-parts workflows, the data layer has to stay reliable across OEM, supplier, and aftermarket systems without losing traceability.

We do:
  • Connected vehicle telemetry and diagnostic data pipelines
  • Predictive maintenance and fleet health decision logic
  • OEM, dealer, supplier, and aftermarket interoperability
  • Data products across service, fleet, and aftermarket workflows
  • AI-ready continuity from vehicle signals into service workflows

In financial services, new products and automation initiatives are usually constrained by the same underlying data estate. Core banking data, transaction records, and customer data models often sit across legacy platforms that were never designed for fast product change, partner connectivity, or governed AI workflows. The challenge is modernizing this foundation without creating new fragmentation every time the business adds a service, integration, or control requirement.

We do:
  • Core banking, payments, lending, and claims data pipelines
  • Legacy modernization and cloud-ready financial data architectures
  • Fraud, risk, and operational decision data products
  • Open banking, fintech, and partner interoperability
  • Governance foundations for regulated automation and AI scaling

In logistics, data has to move with the shipment itself. The same shipment state needs to stay synchronized as goods pass through warehouses, carriers, and delivery networks, giving operations teams a consistent view of status, location, and fulfillment progress without manual reconciliation between systems and partners.

We do:
  • Order, shipment, and warehouse event pipelines
  • TMS, WMS, ERP, and carrier data interoperability
  • Inventory, route, and fulfillment data products
  • Real-time supply chain visibility and exception monitoring
  • IoT and BI-ready logistics data foundations

Telemetry often begins as a visibility layer. The moment it starts driving uptime commitments, service workflows, and post-sale revenue models, the data must remain reliable across service platforms, customer access, and third-party use, with sharing, revocation, auditability, and secure lifecycle controls aligned to the EU Data Act and the CRA requirements.

We do:
  • Device telemetry ingestion and long-term installed base continuity
  • Predictive maintenance, anomaly detection, and SLA logic
  • Service and customer-facing data products 
  • OEM, supplier, and partner data exchange
  • Regulatory compliance and cybersecurity consulting

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Other areas we help strengthen around the data foundation

Regulatory Compliance

Translate regulatory requirements into controls, evidence, and audit-ready operations.

AI Adoption

Turn data foundations into governed AI workflows, copilots, and scalable agents.

Cloud Infrastructure

Build resilient cloud foundations for secure, observable, and scalable data systems.
Sigma Software has offices in multiple locations in Europe, Middle East, Northern and Latin America