What is it about?
For enterprise teams ready to put an approved data roadmap into action, Exadel builds the governed data platform, integrates priority data sources, and delivers reusable data products.
Our analytics enablement work turns that trusted data into reporting, BI, and AI-ready foundations the business can use.
This is the first paper in a three-part series. Whitepaper 2 covers Operational Excellence. Whitepaper 3 covers Implementation, Roadmap, and Cloud Deployment.
This service is for enterprise teams that:
Alexey Girzhadovich
Chief Enterprise AI and Solutions Officer
Have a data roadmap or target architecture but no clear path into delivery
Alexey Girzhadovich
Chief Enterprise AI and Solutions Officer
Run pipelines that are growing faster than their quality checks, lineage, and access controls
Alexey Girzhadovich
Chief Enterprise AI and Solutions Officer
Still rely on manual reporting, inconsistent KPIs, or duplicated BI logic
Alexey Girzhadovich
Chief Enterprise AI and Solutions Officer
Have AI use cases waiting on clean, governed, reusable data
Alexey Girzhadovich
Chief Enterprise AI and Solutions Officer
Have completed a Data Foundation engagement and are ready to start the build
Typical stakeholders:
CDO
CTO
Head of Data Engineering
Head of BI / Analytics
Why Data Platform Programs Stall Between Strategy and Delivery
Turning a data roadmap into a working platform takes more than getting the architecture right. Integration, governance, and analytics also need to come together as you build.
MIT CISR research found that, on average, only 28% of employees draw on reusable data assets.
Choosing Architecture Before Understanding Your Constraints
Platform decisions are made without enough visibility into existing systems, data flows, data quality, governance, and operational requirements.
Carrying Existing Data Problems Into a New Platform
A new platform doesn’t fix existing problems with data quality, definitions, or ownership. It simply carries them forward.
Losing the Investment Case Before the Build Starts
When the path forward is unclear, it becomes harder to make the case for further investment. Leaders need to know what they’re funding and what it will deliver.
Planning a Data Platform Investment?
Make sure you’re asking the right questions before you build.ion
Start
What the 6–8 Week Engagement Looks Like
Over 6–8 weeks,Exadel works with your teams to understand your current data environment, prioritize use cases, shape the target architecture, and define the governance needed for delivery.
Stage 1: Mobilize & Baseline — Current-State Assessment
Build a clear picture of the current data environment, business priorities, and the issues that could affect delivery.
Stage 2: Prioritize & Design — Use Cases & Architecture
Identify the use cases worth pursuing and shape the architecture around what they require.
Stage 3: Shape Governance & Delivery Roadmap
Define how the data will be governed and organize the work into practical delivery waves.
Phase 4: Validate & Hand Over
Validate the plan and confirm what it will take to move into implementation.
Finish
What You Leave With
A practical decision packthat gives your team the information and direction needed to make investment decisions and prepare for implementation.
Current-State Baseline
A shared view of your current data environment and the issues that need to be addressed before build.
Prioritized Use Case Register
The opportunities worth pursuing, prioritized by business value and readiness to show what should move forward first.
North Star Data Architecture
A clear view of the target architecture and the key decisions that will guide the build.
Governance Baseline
The governance requirements that need to be in place as you move into delivery.
Phased Delivery Roadmap
A practical roadmap showing how the work can move forward, including what comes first and what implementation will involve.
How Data Foundation Fits Into Your Data & AI Journey
The Data Foundation is the first step in a wider data and AI journey. It gives your organization the priorities, architecture, governance direction, and delivery roadmap needed to move confidently into platform build.
Phase 1
Data Foundation — Discovery & Design
Your Current State
Assess the current state, prioritize use cases, define the target architecture, and create a fundable delivery roadmap.
Phase 2
Platform Build & Data Integration
Build the platform foundation, integrate priority data sources, implement pipelines and controls, and create initial reusable data products.
Data Platform Build & Analytics Enablement
Phase 3
Analytics, BI & AI Enablement
Turn governed data products into trusted reporting, semantic models, dashboards, self-service analytics, and AI-ready foundations.
Data & AI Governance Across the Journey
Ownership and stewardship
Data quality
Metadata and lineage
Access and security
Monitoring and remediation
Responsible AI controls
Turn governed data products into trusted reporting, semantic models, dashboards, self-service analytics, and AI-ready foundations.
Operational Data & AI Governance
Our Reference Architecture for Governed Data and AI Platforms
You don’t have to design every data and AI capability from scratch. Exadel starts with a reference architecture, then adapts it to your priorities, existing technology, and where you are today.
Intelligent Applications
AI assistants and copilots
Autonomous AI agents
Conversational analytics
Business applications
Custom experiences
AI & Automation Services
AI assistants and copilots
Autonomous AI agents
Conversational analytics
Business applications
Custom experiences
Enterprise Data & Knowledge
AI assistants and copilots
Autonomous AI agents
Conversational analytics
Business applications
Custom experiences
Intelligent Applications
AI assistants and copilots
Autonomous AI agents
Conversational analytics
Business applications
Custom experiences
Intelligent Applications
AI assistants and copilots
Autonomous AI agents
Conversational analytics
Business applications
Custom experiences
Intelligent Applications
AI assistants and copilots
Autonomous AI agents
Conversational analytics
Case Studies
Our Work
FAQ
What is a data foundation engagement?
Exadel's Data Foundation engagement is a 6–8 week advisory process across four parallel workstreams that helps enterprise teams define the strategy, architecture, use case priorities, and governance model they need before investing in platform build. It produces a decision pack and delivery roadmap, not a built platform.
When should an organization start with a data foundation engagement?
Before committing to platform build. If your organization is planning a data modernization program, AI initiative, or analytics investment but lacks clarity on architecture, use case priorities, or governance, a data foundation engagement addresses those gaps first — preventing costly rework once engineering begins.
What is the difference between data foundation and data platform build?
Data foundation covers discovery and design — assessing current state, prioritizing use cases, defining architecture, and establishing governance. Data platform build is the engineering phase that follows. Exadel's Data Foundation engagement is Phase 1; Platform Build & Data Integration is Phase 2. The foundation engagement produces the roadmap the build phase executes against.
What does Exadel deliver at the end of a Data Foundation engagement?
A decision pack that includes: a current-state data baseline, prioritized use case register, north star target architecture, governance framework baseline, and a phased delivery roadmap with investment estimates. These outputs give leadership what they need to fund and launch the platform with confidence.
How long does a data foundation engagement take?
Exadel's Data Foundation engagement runs 6–8 weeks. Four workstreams run in parallel — baseline assessment, use case prioritization, architecture design, and governance shaping — converging in a validation and handover phase.
What types of organizations benefit from a data foundation engagement?
Enterprise teams planning a first data platform build, organizations modernizing a legacy data environment, post-merger teams unifying fragmented data estates, and organizations whose AI initiatives have stalled due to data quality or architecture gaps.