Data AI Platform Modernization
(includes Data Platform Modernization + AI Innovation Platform)
Data & AI Platform Modernization
Turn scattered data into a platform your business can actually build AI on
Most organizations have more data than ever, and less confidence in it than ever. It's spread across on-premises databases, departmental spreadsheets, and a handful of cloud tools that were adopted independently, with no shared model connecting them. AI projects stall not because the AI doesn't work, but because the data underneath it is inconsistent, hard to access, or nobody trusts the numbers. SiS runs a combined program that modernizes your core data platform and builds a governed AI capability on top of it — so AI initiatives are built on solid ground, not a spreadsheet someone hopes is current.
This is not a data warehouse migration in isolation, or an AI pilot disconnected from your real data. It's a structured program covering data platform modernization, governance, and applied AI — delivered by a Microsoft Solutions Partner with a Data & AI specialization.
The problem organizations run into
- Data spread across disconnected systems. On-premises databases, cloud applications, and departmental spreadsheets don't share a common model, so getting one reliable number means manually reconciling multiple sources.
- No single source of truth. Different teams report different figures for the same metric, because each pulled from a different system with a different definition — and nobody is confident which one is right.
- AI pilots that never scale. Proof-of-concept AI projects get built on a narrow, cleaned-up dataset that doesn't reflect the mess of production data, so they can't be deployed at scale without starting over.
- Governance treated as an afterthought. Without data quality standards, lineage tracking, and access controls, a modernized platform can become just as untrustworthy as the systems it replaced — only faster.
- AI use cases chosen for novelty, not value. Without a structured process to identify and prioritize use cases, AI investment goes toward whatever's easiest to demo rather than what actually moves the business.
Our approach
We run this as one connected program across three phases, each with clear deliverables — so you always know what stage you're at and what you're getting.
Phase 1 — Data & AI Readiness Assessment (typically 3–5 weeks)
We map your current data landscape and identify where AI can create the most real value.
- Data landscape discovery across on-premises, cloud, and departmental systems, including data quality and consistency review
- Current-state architecture review against a modern data platform reference model
- AI use case discovery workshops with business stakeholders, scored by feasibility and business value
- Data governance maturity assessment — lineage, access controls, and quality standards
- A prioritized roadmap covering both data platform modernization and applied AI use cases, with a business case for each
You leave this phase with a clear view of what data foundation to build, and which AI use cases are actually worth pursuing first.
Phase 2 — Data Platform Modernization (typically 10–18 weeks)
We build the modern data platform your AI initiatives — and your reporting — will run on.
- Modern data platform architecture on Azure, using Microsoft Fabric or Azure Synapse Analytics depending on your scale and needs
- Data integration pipelines consolidating on-premises, cloud, and departmental sources into a single governed platform
- Data governance implementation using Microsoft Purview — lineage tracking, data quality rules, and access controls
- A unified semantic model so business metrics are defined once and reported consistently across the organization
- Self-service reporting and dashboards in Power BI, built on the governed data model
Phase 3 — Applied AI Development (typically 8–16 weeks per use case)
We build the prioritized AI use cases directly on top of the modernized, governed data platform.
- AI/ML model development using Azure AI and Azure Machine Learning, scoped to the highest-value use cases identified in Phase 1
- Generative AI solutions using Azure OpenAI Service where relevant — for example, intelligent search, document processing, or copilots built on your own data
- Responsible AI review for each use case — bias testing, explainability, and compliance alignment
- Deployment into production workflows, not left as a standalone proof of concept
- Monitoring and retraining processes so models stay accurate as your data evolves
What's included
| Component | What you get |
|---|---|
| Data & AI Readiness Assessment | A prioritized roadmap covering data modernization and scored AI use cases |
| Modern Data Platform | Consolidated, governed data architecture on Microsoft Fabric or Azure Synapse Analytics |
| Data Governance | Lineage tracking, quality rules, and access controls via Microsoft Purview |
| Unified Reporting | A single semantic model and self-service dashboards in Power BI |
| Applied AI Development | AI/ML and generative AI solutions built on your governed data and deployed into production |
| Responsible AI Review | Bias testing, explainability, and compliance alignment for every deployed model |
What you can expect to gain
- One trusted source of truth — metrics defined once, reported consistently across every team
- AI built to scale, not stuck in pilot — because it's built on production-grade, governed data from the start
- Faster, more confident decisions — self-service reporting means teams get answers without waiting on a data request
- Reduced governance risk — lineage and access controls mean you can show exactly where data came from and who can see it
- AI investment aimed at real value — use cases are prioritized by business impact, not novelty
Is this the right fit for you?
This program is built for organizations whose data is spread across multiple systems with no unified model, and who want to build real AI capability rather than run isolated pilots. It's especially relevant if any of the following is true:
- Different teams report different numbers for what should be the same metric
- Your data lives across a mix of on-premises databases, cloud apps, and spreadsheets
- You've run AI pilots before that never made it into production
- Leadership wants an AI strategy grounded in actual business value, not a list of trending use cases
- You don't have confidence in who can access what data, or where it came from
Built on Microsoft's own platform
Microsoft Fabric · Azure Synapse Analytics · Microsoft Purview · Power BI · Azure Machine Learning · Azure AI · Azure OpenAI Service
Typical timeline
| Weeks | Focus |
|---|---|
| 1–5 | Data & AI Readiness Assessment |
| 4–22 | Data Platform Modernization |
| 15–30+ | Applied AI Development (scales per use case, often run in parallel with later platform work) |
| Ongoing | Model monitoring, retraining, and new use case delivery |
Timelines flex based on the number of data sources, platform scale, and how many AI use cases are in scope — the assessment phase gives us the real numbers for your specific roadmap.
What you'll walk away with
- A prioritized roadmap covering data modernization and scored AI use cases
- A modernized, governed data platform with a unified semantic model
- Self-service reporting and dashboards in Power BI
- One or more AI/ML or generative AI solutions deployed into production, not left as a pilot
- Documented data lineage, access controls, and a responsible AI review for every deployed model
Common questions
Do we need to finish the data platform modernization before starting any AI work?
Not entirely. High-value use cases with strong existing data can start earlier, but generally AI built on an ungoverned platform tends to inherit the same trust and consistency problems — most clients see better results sequencing the platform work first, even if phases overlap.
We've already tried AI pilots that didn't go anywhere — how is this different?
Most stalled pilots fail because they were built on a narrow, cleaned-up dataset that doesn't reflect production reality. This program builds AI directly on your modernized, governed platform, and includes a deployment step so it doesn't stop at proof of concept.
Can we prioritize just one or two AI use cases instead of a broad program?
Yes. The assessment scores all identified use cases, and engagements can be scoped to start with the highest-value one or two, with the platform work sized to support them.
About SiS
SiS is a Microsoft Solutions Partner with a Data & AI specialization. We don't treat AI as a separate initiative from your data platform — we treat them as one connected capability, because AI is only as trustworthy as the data it's built on.
Ready to see where your data and AI opportunity actually is? Talk to SiS about a Data & AI Readiness Assessment.