Durga Analytics Enterprise Offering
Enterprise Data Modernization 360 — End-to-End Functional & Technology Transformation
A structured, end-to-end data modernization product for enterprises that want to move from legacy warehouses, ETL and siloed reports to a modern, cloud-native data platform with clear business use cases, governance, and an updated operating model.
Data Modernization 360 Snapshot
- • End-to-end modernization blueprint from business vision to technology architecture
- • Functional stream for KPIs, domains, products, journeys and regulatory needs
- • Technology stream for cloud data platform, pipelines, modeling and tooling
- • Phased migration plan that reduces risk while delivering visible quick wins
Why Enterprise Data Modernization, End-to-End?
Many enterprises sit on aging warehouses, ETL scripts and report farms that are costly to run, slow to change and not ready for AI or real-time analytics. Data Modernization 360 aligns business stakeholders and technology teams around a single transformation — from use cases and data domains all the way to cloud data platforms, governance and operating model.
Business + Tech Together
We treat data modernization as both a functional and technical program: aligned KPIs, domains and journeys on one side; cloud platforms, pipelines and models on the other.
From Legacy to Cloud
Reduce legacy warehouse and ETL footprint over time, while building a modern lakehouse or warehouse that supports BI, APIs and advanced analytics in parallel.
Outcome-Driven
Anchor every modernization step to concrete outcomes: faster MIS, regulatory compliance, self-service analytics, AI readiness, cost optimization and resiliency.
Modernization Pillars — Functional + Technology
Data Modernization 360 is structured into six pillars that jointly cover business-functional design and technology transformation, so nothing is left to “figure out later”.
Pillar 1 — Vision, Use Cases & Domain Blueprint (Functional)
- Business vision for data and analytics, aligned to corporate strategy
- Prioritized use case backlog across finance, risk, operations, sales, CX and digital
- Domain & subject-area map (customer, product, transaction, risk, etc.)
- Alignment of KPIs and analytical questions to domains and data products
Pillar 2 — Target Data Platform & Architecture (Technology)
- Target architecture for lake, warehouse or lakehouse (e.g., Databricks, Snowflake, BigQuery, Synapse)
- Patterns for batch, streaming and API-based data movement
- Integration architecture with core systems, SaaS platforms and external data
- Non-functional requirements: scalability, cost, security, DR and observability
Pillar 3 — Canonical Models, KPIs & Data Products (Functional)
- Conceptual & logical models for core entities and events
- Standardized KPI definitions and metric catalogs mapped to data products
- Design of reusable data products for BI, APIs and AI/ML
- Alignment with data governance and stewardship roles
Pillar 4 — Pipelines, Migration & Automation (Technology)
- Design and build of ingestion, transformation and serving pipelines
- Coexistence and migration patterns from legacy warehouses and ETL
- Automation for CI/CD, testing, data quality and observability
- Refactoring or retiring legacy jobs, reports and schemas
Pillar 5 — Governance, Quality, Security & Compliance
- Data ownership, stewardship and RACI aligned to domains and data products
- Data quality framework and controls embedded into modern pipelines
- Privacy, access control and security patterns for cloud and hybrid
- Traceability and documentation needed for regulatory and audit stakeholders
Pillar 6 — Operating Model, Skills & Change (Functional + Tech)
- Target operating model for data platform, BI, advanced analytics and governance
- Role definitions: platform teams, data engineers, product owners, analysts, stewards
- Training and enablement plan for business and technology users
- Change management and communication plan across the enterprise
Example Modernization Outcomes
- Consolidated modern data platform replacing multiple legacy warehouses
- Trusted, documented KPIs across finance, risk and sales
- Faster MIS and regulatory reporting with fewer manual reconciliations
- Self-service analytics and governed BI for business teams
- Foundations for AI/ML on high-quality, well-modeled data
- Reduced run cost of legacy ETL and reporting estate
- Clear ownership of data domains and products
- Improved compliance posture and audit readiness
- Scalable operating model for future data initiatives
Delivery Approach — Data Modernization 360 in Phases
The product is delivered as a structured program with clear entry and exit criteria for each phase, so stakeholders see progress and risk is managed throughout.
Phase 1 — Discovery & Blueprint
Assess current landscape: systems, platforms, reports, pain points and costs. Define target-state vision, domain map, high-level architecture and prioritized use case roadmap. Identify quick wins and pilot candidates.
Phase 2 — Pilot Domain & Platform Foundation
Stand up the core modern data platform, pipelines and governance for 1–2 priority domains (e.g., customer, finance). Deliver end-to-end flows from sources to dashboards/APIs and validate value vs legacy stack.
Phase 3 — Scale, Migrate & Optimize
Extend to additional domains and use cases, execute structured migration from legacy, optimize costs and performance, refine the operating model, and embed continuous improvement practices.
What You Get — Functional & Technical Deliverables
Data Modernization 360 leaves you with a working modern data platform, functional design and a roadmap, not just a slide deck.
- Current-state assessment report with quantified pain points and cost drivers
- Target-state architecture, domain blueprint and use case roadmap
- Conceptual & logical data models for key domains and data products
- Deployed cloud data platform foundation (as per agreed scope) with pipelines and DQ checks
- Reference dashboards / data products for priority use cases (BI or API-based)
- Governance and operating model documentation, RACI and forum charters
- Migration plan and decomposition of legacy warehouses and ETL landscape
- 12–24 month modernization execution roadmap with cost and benefit view
Engagement Models & Indicative Pricing
We tailor the product to your starting point and ambition level. Typical options:
Modernization Assessment
Fast, structured assessment and blueprint focused on 1–2 domains and a high-level platform roadmap.
- 4–8 weeks
- Target architecture + roadmap
- Quick-win implementation options
Pilot + Foundation Build
Full Data Modernization 360 for pilot domains, including platform foundation, pipelines, data products and governance.
- 12–20 weeks
- 1–3 domains in scope
- Working modern data platform + use cases
Enterprise Rollout & Managed Modernization
Multi-wave rollout across business units and regions, with optional managed services for platform ops, data engineering and governance.
- Phased multi-quarter program
- Extended domains and migration
- Co-managed or fully-managed model
Durga Analytics Data Modernization Team
Data, Platform & Domain Experts
Cross-functional team with experience across banking, energy, retail and digital-native enterprises — blending data architecture, engineering, governance, domain knowledge and product thinking.
Technology & Platform Experience
- Modern data platforms: Databricks, Snowflake, BigQuery, Synapse, Redshift
- Cloud ecosystems: Azure, AWS, GCP and hybrid deployments
- ETL/ELT & orchestration tools, streaming frameworks and API gateways
- BI, analytics and AI platforms integrated with governed data products
Request a Data Modernization 360 Consultation
Share a brief overview of your current data landscape, core systems, key challenges and modernization priorities. We’ll respond with a tailored assessment proposal and an initial roadmap for your enterprise data modernization journey.