This Engagement Service Is For
Alexey Girzhadovich
Chief Enterprise AI and Solutions Officer
Are preparing to invest in a new data platform but lack a clear architecture baseline
Alexey Girzhadovich
Chief Enterprise AI and Solutions Officer
Have data they can’t fully trust because of inconsistent quality or weak governance
Alexey Girzhadovich
Chief Enterprise AI and Solutions Officer
Have stalled AI initiatives blocked by poor data foundations
Alexey Girzhadovich
Chief Enterprise AI and Solutions Officer
Are post-merger with fragmented data environments that need unifying
Alexey Girzhadovich
Chief Enterprise AI and Solutions Officer
Need a funded business case and delivery roadmap to secure executive approval
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Head of Enterprise Architecture
Why Data Platform Projects Run Into Trouble Before the Build
The success of a data platform depends on decisions made long before the first line of code is written. These early decisions shape what gets built, how much it costs, and whether the investment delivers the expected value. Yet this is often where projects begin to run into trouble.
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
Reference Architecture With Governance Enforced by Design
Exadel uses a platform-agnostic reference architecture to embed governance controls where your data is created, transformed, shared, and used.
Where controls sit
- Data Quality and Validation: Automate quality checks at ingestion, transformation, and data product boundaries.
- Metadata, Classification and Lineage: Make metadata and lineage business-readable to support traceability, auditability, and controlled data usage.
- Access Control and Secure Data Sharing: Apply controls at the platform and data product level for secure internal and external data usage.
- AI-Assisted Monitoring: Detect anomalies and drift while keeping prioritization and decision authority with your people.
How the platform is built
ERP and other data sources feed 360 and other data products. Those products support data science, analytics and reporting, and data applications.
Across these layers, data governance, secure data sharing, data exchanges, and security provide the controls. Quality, usage, and compliance are monitored continuously across the data lifecycle.
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.
