When data is spread across disconnected systems, decision making slows down. We help you modernize data foundations, build governance in from the start, and scale analytics and AI. This readiness helps your teams act faster, trust the numbers, and drive operational excellence and growth.
Our Data & Analytics services support you at every stage of your data to decision journey, so insight is always ready when decisions matter.
Build trusted data platforms and governance controls.
Enable analytics and visualization for faster business decisions.
Operationalize AI, MLOps, and trusted AI capabilities.
Embed GenAI into applications and workflows.
Connect enterprise systems through integration and APIs.
Bring clarity, confidence, and momentum in every business decision.
Build a trusted data foundation from the start
Turn data into competitive insights
Scale AI with confidence, control, and trust
Make everyday work simpler, faster, and more effective
Keep data connected and insight moving
Solve your biggest business challenges first. And build value from there.
Select your industry to see the most common infrastructure challenges and how we help you solve them.
Clinical, operational, and compliance data is spread across disconnected systems.
Poor data quality and lineage reduce confidence in analytics and reporting.
Data governance and auditability are difficult to enforce consistently.
Weak data foundations prevent AI initiatives from scaling.
Creates a single, trusted data foundation with built-in governance.
Improves trust in data through quality and traceability.
Supports regulatory reporting, auditability, and consistent policy enforcement.
Strengthens data foundations so AI initiatives can scale with confidence.
Transaction, risk, and customer data remain disconnected.
Batch processing delays insights for business and risk teams.
Machine learning models lack transparency and governance.
AI initiatives fail to operationalize across the enterprise.
Unifies transaction, risk, and customer data for consistent access.
Delivers clear, decision ready insights for business and risk teams.
Ensures AI models are explainable, governed, and ready for enterprise use.
Helps predictive models deliver real, repeatable business impact.
Production, supply chain, and business data aren’t integrated.
Delayed data restricts timely operational decisions.
Teams rely on spreadsheets instead of analytics platforms.
Data exists, but advanced analytics isn’t fully leveraged.
Connects operational and enterprise systems to keep data flowing end to end.
Enables near real time visibility into operations and performance.
Reduces manual reporting by giving teams easy access to trusted insights.
Uses predictive insights to optimize manufacturing performance.
Customer, channel, and product data resides in silos.
Insights are delayed due to manual reporting processes.
Dashboards exist but don’t drive action.
AI initiatives lack governance and data readiness.
Creates a single, trusted view of customer and sales data across channels.
Delivers clear, KPI driven insights aligned to business decisions.
Improves adoption by putting insights directly into everyday workflows.
Helps AI use cases move from pilots to scalable retail intelligence.
Legacy systems limit scalability and analytics performance.
Ownership, lineage, and quality are difficult to manage.
Analytics lag real-time operational needs.
Compliance reporting is manual and error prone.
Improves scalability, performance, and reliability across legacy data platforms.
Establishes clear ownership, lineage, and accountability across data assets.
Delivers timely visibility into operations to support faster responses.
Supports accurate, audit ready regulatory and compliance reporting.
Rapid scale overwhelms existing data infrastructure.
Teams use disconnected tools and inconsistent metrics.
Lack of MLOps and governance prevents AI from scaling.
Enterprise knowledge isn’t easily accessible across teams.
Supports rapid growth with flexible, high performance data platforms.
Aligns teams around consistent metrics and shared views of performance.
Enables AI models to move from experimentation to reliable, governed scale.
Makes enterprise knowledge easier to access, share, and act on.
Clinical, operational, and compliance data is spread across disconnected systems.
Poor data quality and lineage reduce confidence in analytics and reporting.
Data governance and auditability are difficult to enforce consistently.
Weak data foundations prevent AI initiatives from scaling.
Creates a single, trusted data foundation with built-in governance.
Improves trust in data through quality and traceability.
Supports regulatory reporting, auditability, and consistent policy enforcement.
Strengthens data foundations so AI initiatives can scale with confidence.
Transaction, risk, and customer data remain disconnected.
Batch processing delays insights for business and risk teams.
Machine learning models lack transparency and governance.
AI initiatives fail to operationalize across the enterprise.
Unifies transaction, risk, and customer data for consistent access.
Delivers clear, decision ready insights for business and risk teams.
Ensures AI models are explainable, governed, and ready for enterprise use.
Helps predictive models deliver real, repeatable business impact.
Production, supply chain, and business data aren’t integrated.
Delayed data restricts timely operational decisions.
Teams rely on spreadsheets instead of analytics platforms.
Data exists, but advanced analytics isn’t fully leveraged.
Connects operational and enterprise systems to keep data flowing end to end.
Enables near real time visibility into operations and performance.
Reduces manual reporting by giving teams easy access to trusted insights.
Uses predictive insights to optimize manufacturing performance.
Customer, channel, and product data resides in silos.
Insights are delayed due to manual reporting processes.
Dashboards exist but don’t drive action.
AI initiatives lack governance and data readiness.
Creates a single, trusted view of customer and sales data across channels.
Delivers clear, KPI driven insights aligned to business decisions.
Improves adoption by putting insights directly into everyday workflows.
Helps AI use cases move from pilots to scalable retail intelligence.
Legacy systems limit scalability and analytics performance.
Ownership, lineage, and quality are difficult to manage.
Analytics lag real-time operational needs.
Compliance reporting is manual and error prone.
Improves scalability, performance, and reliability across legacy data platforms.
Establishes clear ownership, lineage, and accountability across data assets.
Delivers timely visibility into operations to support faster responses.
Supports accurate, audit ready regulatory and compliance reporting.
Rapid scale overwhelms existing data infrastructure.
Teams use disconnected tools and inconsistent metrics.
Lack of MLOps and governance prevents AI from scaling.
Enterprise knowledge isn’t easily accessible across teams.
Supports rapid growth with flexible, high performance data platforms.
Aligns teams around consistent metrics and shared views of performance.
Enables AI models to move from experimentation to reliable, governed scale.
Makes enterprise knowledge easier to access, share, and act on.
Managed services delivery built for enterprise scale
Governance-first approach to analytics and AI
Proven ability to scale across industries and geographies
Practical solutions designed for measurable impact
11,500+
Professionals
34
Global Locations
80+
Fortune 100 Clients
15+
Years of Delivery Experience
Driving business outcomes with trusted data for enterprises.
“Confident decisions start with trusted data. Our role is to help enterprises turn information into business value through strong foundations, governance, and analytics.”
Anshuman Mishra,
Head—Data & Analytics Practice, Global, Scalence
Put your data to work with a solid foundation for better business outcomes.
Scalence Navi
