Data that can
carry AI at scale

AI is redefining how enterprises create value, operate at scale, and compete. Data is a critical underpinning – without reliable, well-governed data foundations, AI cannot scale safely or perform in production. Hitachi Digital Services helps enterprises build GenAI-ready data platforms engineered for reliability – so data is governed, observable, and cost-optimized from edge to core to multicloud.

We modernize, automate, and operate the data value chain using Data Reliability Engineering for AI, fusing DataOps, FinOps, and SRE practices to deliver resilient, self-healing data ecosystems that keep AI accurate and running.

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Data that can carry AI at scale
Challenge & Opportunity

When Data Isn’t Reliable, AI Becomes a Liability

Most enterprises already have data platforms in place – lakes, warehouses, pipelines, analytics tools, and cloud services. But many were built for reporting, not for AI operating continuously at enterprise scale. As AI moves into core business processes, gaps in data reliability, visibility, and governance surface fast. Quality issues propagate into models, costs rise without transparency, and control struggles to keep pace.

Solution
Data Reliability Engineering for AI
Hitachi Digital Services helps enterprises modernize, govern, and operate their data value chain so AI can perform reliably in production. We take a reliability-first approach to data modernization. Our Data Reliability Engineering for AI framework spans the full data-to-AI lifecycle – from assessing readiness and architecting modern platforms, to embedding governance, engineering resilience, and activating AI at scale. Powered by HARC, this approach brings operational discipline to data and AI environments, ensuring they perform under real-world conditions.

Our Data Practice brings together five solution areas. Each capability strengthens the next, creating a unified data fabric that supports AI at scale.

Architect Data Platforms That Can Evolve With AI

Modernize legacy data environments into scalable, cloud-ready platforms designed to support AI workloads. We apply a data fabric approach to reduce fragmentation, improve performance, and embed reliability from the start.

Architect Data Platforms That Can Evolve With AI
  • Data Fabric Architecture
    Implement cloud-ready data fabrics connecting edge, core, and multicloud environments
  • Scalable Data Platforms
    Deploy hybrid data lakes and warehouses built for scale, performance, and flexibility
  • Cloud Data Migration
    Modernize legacy platforms through structured data migration and ETL, including on-prem to cloud transitions
  • DataOps Automation
    Integrate automation to reduce latency, improve pipeline reliability, and accelerate decision-making
Explore Data Modernization

Embed Trust, Control, and Compliance by Design

As AI scales, governance must move faster. We embed governance directly into data flows using automation, enabling trusted self-service while meeting regulatory and compliance requirements.

Embed Trust, Control, and Compliance by Design
  • Governance Frameworks
    Establish consistent governance models, business glossaries, and data ownership structures
  • Automated Data Controls
    Automate data quality, classification, lineage, and metadata management across domains
  • Policy Enforcement
    Enforce privacy, security, and regulatory alignment across hybrid and multicloud environments
  • Audit Transparency
    Enable traceability and controlled access for analytics, automation, and AI use cases
Explore Data Management & Governance

Connect Data Across the Enterprise with Reliability Built In

AI depends on data that can move securely and consistently across systems. We simplify integration and automation across hybrid environments, ensuring data flows remain governed, observable, and resilient as they scale.

Connect Data Across the Enterprise with Reliability Built In
  • Integration Automation
    Reduce integration effort and complexity through automation and standardized patterns
  • Hybrid Connectivity
    Connect cloud, on-prem, and edge environments with consistent control and orchestration
  • Real-Time Data Flow
    Enable real-time and batch data movement to support operational and AI-driven use cases
  • Secure Data Movement
    Maintain security, compliance, and reliability across all data flows and dependencies
Explore Data Integration & Automation

Keep Data and AI Running in Production

Reliability is not an afterthought – it is engineered. Through Data Reliability Engineering for AI, powered by HARC, we apply DataOps, FinOps, and SRE practices to ensure data platforms remain observable, resilient, and cost-aware under real-world AI workloads.

Keep Data and AI Running in Production
  • Observability Engineering
    Engineer monitoring, early-warning detection, and proactive issue prevention across pipelines
  • Self-Healing Pipelines
    Enable automated remediation and continuous validation across data systems
  • Performance Optimization
    Optimize performance and cost through FinOps-aligned monitoring and resource management
  • Data Trust Assurance
    Improve data availability, consistency, and reliability across analytics and AI environments
Explore Data Reliability & Engineering

Activate Reliable Data for Enterprise AI

Turn trusted data into actionable insight and AI-driven outcomes. We enable analytics, AI, and GenAI on top of governed, reliable data foundations – with lifecycle control and monitoring built in.

Activate Reliable Data for Enterprise AI
  • Automated AI Pipelines
    Automate data preparation, feature engineering, and model deployment across environments
  • AI Accelerators
    Accelerate time to value with pre-built analytics, AI, and industry-specific accelerators
  • Model Lifecycle Management
    Monitor models, detect drift, and maintain accuracy over time using AI/MLOps practices
  • Responsible AI Enablement
    Enable explainable, governed, and production-ready AI aligned to business outcomes
Explore Data, AI & Analytics
Customer Story

Logan Aluminum: Reliable IT/OT data for operational performance

Unifying IT and OT data improves safety, optimizes production performance, and strengthens advantage.

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Logan Aluminum: Reliable IT/OT data for operational performance
Customer Story

Raiffeisen bank: Banking in the cloud

Improving client experience with industry leading innovation using cloud.

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Raiffeisen bank: Banking in the cloud
Customer Story

Salford Royal: Data and Insight Drives Better Patient Care

Digital Control Centre improves care coordination and expands clinical capacity

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Salford Royal: Data and Insight Drives Better Patient Care
How We Work

We help businesses modernize, integrate, and run data platforms that perform, scale, and evolve. Our global teams embed quality, observability, and resilience into every data platform.

Advisory & Professional Services
Support
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We Build Reliability into Every Byte
Why Hitachi Digital Services

We Build Reliability into Every Byte

  • Mission-critical reliability and SRE-led operations
  • Unified DataOps + FinOps methodology
  • Integrated IT/OT expertise
  • Global systems integrator with 8 delivery centers
  • 110 years of engineering heritage
  • 30% productivity gain with DataOps and DRE
  • Proven engineering-led reliability (SRE and DRE) boost accuracy, trust, resilience
Our Experts Our Experts
Our Experts
Madhusudhanan Panchapakesan
Madhusudhanan Panchapakesan
Data Practice Lead
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Marimuthu Muthusamy
Marimuthu Muthusamy
Global Delivery Lead: Hitachi Application Reliability Centers (HARC)
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Senthil Ramachandran
Senthil Ramachandran
Global Delivery Lead: Data Reliability & Engineering
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Partners

Collaboration with industry leaders extends our reach and accelerates results.

Talk to an Expert | Explore more:

Data Modernization | Data Management & Governance | Data Reliability & Engineering | Data, AI & Analytics | Data Integration & Automation | Capabilities | Industries

INSIGHTS

Hitachi Digital Services Launches HARC Agents to Power Enterprise-Grade Agentic AI Insights

Hitachi Digital Services Launches HARC Agents to Power Enterprise-Grade Agentic AI

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Operationalizing AI at Scale with GenAIOps Insights

Operationalizing AI at Scale with GenAIOps

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Data Reliability Engineering: An Imperative for Cloud Transformation Insights

Data Reliability Engineering: An Imperative for Cloud Transformation

Watch the webinar – 2024, HV and HDS

FAQ

GenAI-ready data is data that is reliable, well-governed, discoverable, and accessible across the enterprise. It includes strong lineage, quality controls, and security so data can be used safely for analytics, automation, and AI – not just stored or reported on.

Most AI challenges are data challenges. Models depend on pipelines, quality, and governance staying stable over time. When data environments are fragmented or poorly observable, issues like drift, failures, and rising cost surface quickly – undermining trust and slowing adoption.

Data Reliability Engineering for AI is Hitachi Digital Services’ reliability-first approach to operating data at scale. It fuses DataOps, FinOps, and Site Reliability Engineering (SRE) practices to improve data trust, observability, resilience, and cost control – so analytics and AI can perform reliably in production.

Traditional modernization focuses on platforms and migration. Our approach modernizes the full data value chain – including governance, integration, and run operations – so data is not only modern, but reliable, visible, and ready to support enterprise AI.

Leaders typically see increased trust in reporting and analytics, faster delivery of data products, fewer operational disruptions, better compliance and audit readiness, and stronger cost transparency – alongside improved readiness to deploy AI responsibly at scale.

Governance ensures data is secure, compliant, traceable, and controlled across environments. It enables trusted self-service access, supports regulatory requirements, and provides accountability as AI is scaled into business workflows.

Yes. We design and operate connected data fabrics across edge, core, and multicloud environments. This improves integration, visibility, and reliability as data moves across platforms, regions, and business domains.

We use Data Reliability Engineering practices such as continuous monitoring, early-warning detection, automated remediation, and cost-aware operations. Where required, HDS also provides run support through Hitachi Application Reliability Centers (HARC) to keep platforms stable, observable, and optimized.

Most organizations start by assessing reliability, governance maturity, and platform readiness, then prioritizing improvements across architecture, integration, automation, and operational controls. The goal is to build confidence fast while creating a scalable roadmap.

Our Data Practice integrates five solution areas – Data Modernization, Data Management & Governance, Data Integration & Automation, Data Reliability & Engineering, and Data, AI & Analytics. Together, they form a connected system that moves data from ingestion to AI while keeping it trusted, governed, observable, and cost-optimized.