Data Platform Modernisation
Most data platforms weren’t designed to fail. They were designed for an organisation that has since grown, changed, and added systems that nobody anticipated. The result is a patchwork that works — until it doesn’t.
Antares helps Australian organisations move from fragmented, ageing data infrastructure to a modern, governed platform on Microsoft Fabric — built to serve the business as it is today, and structured to grow with it. We start with an honest assessment of your current state before we recommend anything. You’ll know exactly what you’re getting into before you commit to a build.
In production across Australian organisations
The situation most organisations are in
Most organisations we work with aren’t operating on a single, deliberate data architecture. They’ve accumulated one — gradually, over years, through decisions that made sense at the time. A warehouse that was built for a reporting need in 2015. A cloud migration that was done in a hurry. A new CRM that was never properly integrated. A Power BI rollout that outgrew the data underneath it.
The result is a platform that’s costly to maintain, slow to change, and increasingly unable to answer the questions leadership is now asking — about performance, customers, risk, and operational efficiency. It’s not that the data doesn’t exist. It does. It’s that accessing it reliably, at the speed the business expects, has become genuinely difficult.
Pressure from two directions tends to make this problem impossible to ignore. AI initiatives stall because the data foundation isn’t trustworthy enough to build on. And the business keeps asking for reporting capabilities that the existing platform can’t support without significant manual effort.
The question isn’t whether to modernise. It’s how to do it without disrupting the reporting and operations that already depend on what you have.
What organisations are dealing with
Where you’re heading
Modernisation isn’t about rebuilding everything. It’s about addressing the specific constraints that are limiting you today — and building a foundation that doesn’t create the same problems again in five years. Here’s what a well-executed modernisation on Microsoft Fabric typically changes.
How we approach it
The most common reason data platform projects run over time and budget isn’t the technology — it’s discovering mid-project what the current environment actually contains. Dependencies nobody documented. Data quality problems nobody knew about. Downstream systems with undocumented dependencies on a query that’s about to be retired. We surface all of that before a line of new code is written.
We spend two to three weeks doing a thorough inventory of your current data estate — every source system, every pipeline, every warehouse or database, and the reporting layer that sits on top. We’re looking for what works, what doesn’t, what’s depended on by other things, and what’s going to cause problems in a migration if it isn’t addressed first.
The Assessment also covers your Microsoft Fabric readiness: existing Azure and Microsoft 365 licences, your current Power BI environment, and the governance and security posture that will apply to the new platform. It’s not a superficial health check — it’s a thorough technical and architectural review.
What you get at the end is a written deliverable that gives you everything you need to make an informed decision about what to build, in what order, and what it will take.
We stand up the core Fabric environment — provisioning the Fabric workspace and OneLake structure, establishing the medallion lakehouse architecture (raw, curated, and serving layers), configuring the priority data integrations, and building the initial Power BI semantic model and dashboards for the highest-value reporting use cases.
The goal of the Foundation Build is to get something real into production as quickly as possible — not a proof of concept, but a working platform with actual data that your team can use and trust. We run this in parallel with your existing environment until you’ve validated the output and are confident in the new platform.
Cutover is explicit and planned. We don’t pull the old platform out until the new one has been validated in production and your team is comfortable with it.
After the Foundation Build, we work through the remaining phases of the modernisation roadmap in prioritised sprints — bringing additional data domains onto the platform, implementing governance frameworks via Microsoft Purview, adding master data management where it’s needed, and progressively decommissioning legacy infrastructure as the new platform takes over each workload.
Each sprint is scoped and prioritised against your roadmap. You decide what’s most valuable next. We bring the technical execution, the architecture guidance, and the knowledge of what’s upstream and downstream of each change.
We document as we build — architecture decision records, pipeline documentation, data dictionaries, and runbooks. We run knowledge transfer sessions with your data team at each major phase milestone, not as an afterthought at the end of the engagement. Our goal is for your team to understand what has been built and to be able to extend it without needing us for every change.
If you want ongoing support after the build — platform monitoring, new data source onboarding, pipeline maintenance, and continued development — we offer a Managed Data Service. But it should be a choice your team makes from a position of competence, not a dependency created by a gap in documentation or knowledge transfer.
Data platforms aren’t a project you finish and walk away from. New systems get added. Business requirements change. The Microsoft Fabric roadmap keeps moving — and with it, new capabilities that are worth incorporating. Our Managed Data Service provides a dedicated data engineer who knows your platform, working in fortnightly sprints, with regular roadmap reviews to ensure the platform continues to serve where the business is heading — not just where it was when the build was completed.
The Data Platform Assessment
The Assessment is a fixed-fee engagement. Two to three weeks. A small team from Antares working alongside your data and IT team. At the end, you have a written deliverable that covers everything you need to make a well-informed decision about your modernisation programme — including an honest view of complexity, risk, and what would need to change to reduce it.
The Assessment is structured as a discovery and analysis engagement, not a sales process. We ask to see your actual environment — query plans, pipeline code, schema diagrams, refresh logs, support tickets, whatever exists — not just a self-reported summary. The things that tend to cause the most problems in modernisation projects are the things that don’t appear on architecture diagrams.
We work collaboratively with your data team throughout. The output should be a document they recognise as accurate and are prepared to act on — not a generic maturity assessment with recommendations that don’t fit your context.
“The most expensive part of a data platform project is usually not the build — it’s discovering halfway through that something is more complicated than the scoping assumed.”
From our delivery team — based on two decades of data platform projects across Australian organisationsBuilt to grow with you
One of the most frustrating patterns in data platform work is delivering a platform that solves the problems of two years ago. By the time a large modernisation is complete, the business has moved, new systems have been added, and the reporting requirements have changed. We plan explicitly to avoid this — by building for extensibility, not just for the current scope.
We build every Fabric lakehouse on a medallion architecture — separate raw, curated, and serving layers with defined transformation logic at each stage. This makes the platform predictable to extend: adding a new source system means adding it at the raw layer and writing the transformation logic, not unpicking how the previous data engineer wired things together. New team members can understand the structure within hours, not weeks.
Governance isn’t something we layer on at the end of a modernisation — it’s structural decisions made during the Foundation Build. Data ownership is defined in the architecture. Purview is configured as part of the platform, not added later. Sensitivity labels and access policies are aligned to your existing Entra ID groups. Retrofitting governance onto a platform that wasn’t built with it in mind is one of the most common and most expensive post-go-live problems we see.
Every platform we build is designed with AI readiness as a current requirement, not a future aspiration. That means consistent semantic definitions, trustworthy data in OneLake, governance metadata that AI agents can rely on, and a serving layer structured for the kind of retrieval that AI workloads perform. If you’re planning to deploy AI agents or Microsoft Copilot against your organisational data, the data platform is where that journey starts — and we build it accordingly.
Every engagement includes a Centre of Excellence component — a framework for how your data team governs, extends, and operates the platform independently. This includes documentation standards, change management processes, a semantic model governance policy, and a training programme for both data engineers and business-facing Power BI authors. A modern platform only stays modern if the team responsible for it understands how to maintain and extend it.
We don’t design programmes that require a complete build before anything is usable. The Foundation Build is deliberately scoped to get a working, trusted platform into production within weeks, covering the two or three reporting domains that matter most to the business. Each subsequent phase adds further coverage while the business is already getting value from what’s already been delivered.
Microsoft is investing heavily in Fabric — new capabilities ship on a monthly cadence. We keep across the Fabric roadmap as part of our practice, and in the Managed Data Service, we bring relevant updates to your quarterly roadmap reviews. The platform you build today should benefit from Microsoft’s ongoing investment — that only happens if someone is watching what’s coming and applying it to your environment.
Get started
A fixed-fee, two-to-three-week engagement. At the end, you have a clear picture of your current environment, a structured view of the work required, and a sequenced modernisation roadmap you can take straight to a board or investment committee — before committing to any build work.
Full inventory of your current data estate — sources, pipelines, warehouses, and reporting layer
Data quality assessment across your priority domains
Microsoft Fabric readiness and licensing assessment
Power BI environment review — migrate, rebuild, or retire
Quick-win recommendations you can act on immediately
Prioritised modernisation roadmap — phased, sequenced, and honest about complexity
We’ll be in touch within one business day.
Frequently Asked Questions
A data platform modernisation involves assessing your current data estate — including all source systems, existing pipelines, warehouses, reporting layers, and governance gaps — and then planning and building a replacement that addresses those gaps. For most Australian organisations, that destination is Microsoft Fabric: a unified platform built on OneLake that consolidates data engineering, warehousing, real-time analytics, and Power BI in one governed environment. Antares structures every modernisation with a fixed-fee Data Platform Assessment before any build work begins, ensuring the roadmap reflects your actual environment and not assumptions.
Data migration is the technical process of moving existing data and workloads from one system to another. Data platform modernisation is the broader programme that includes migration but also addresses architecture, governance, data quality, integration design, and the reporting and analytics layer that sits on top. A migration gets your data to a new location. A modernisation ensures that new location actually solves the problems you had with the old one — and that it’s structured to grow with the business rather than accumulate the same technical debt again over time.
Yes — and this is one of the most important things we plan for in every modernisation. We run the legacy and new platforms in parallel for a defined period, with agreed validation milestones and explicit cutover criteria. Existing reports continue to function on the old platform while the new platform is being validated against the same data. Nothing is decommissioned until the new environment has been proven in production. This requires a thorough inventory of all downstream dependencies in the Assessment phase — which is one of the reasons the Assessment is non-negotiable before any build work begins.
The Data Platform Assessment takes two to three weeks. A Foundation Build — establishing the core Fabric environment, priority integrations, and initial Power BI semantic models — typically takes eight to sixteen weeks depending on the complexity of your current environment. Full modernisation across all data domains is usually delivered across multiple phases over six to eighteen months. The phased approach is deliberate: it gets something working and trusted in production early, builds confidence in the new platform, and allows each phase to incorporate lessons from the previous one. You do not need to commit to the full programme before completing the Assessment.
Microsoft Fabric supersedes Azure Synapse as Microsoft’s primary data platform — ongoing innovation and investment is in Fabric, not Synapse. Synapse continues to be supported, but the direction is clear. Fabric offers a meaningfully simpler architecture: OneLake replaces the need for separate storage accounts, and the integrated Fabric workloads replace the combination of Synapse, Data Factory, Databricks, and Power BI Premium that many Synapse environments end up requiring. Whether to migrate — and when — depends on your current workloads, technical debt, and the cost of your existing setup. The Assessment will give you a clear picture. We don’t recommend a migration unless the economics and the architectural improvement justify it.
An AI-ready data platform is one where data is unified, governed, trusted, and accessible to AI workloads without requiring significant additional preparation. In practice this means: a centralised data store (OneLake in Fabric) holding clean, well-structured data; a defined semantic layer with consistent business definitions; data quality controls applied at ingestion, not just at reporting; and governance metadata — ownership, classification, lineage — that AI systems and their operators can rely on. Most organisations struggling to get AI into production find that the blocker isn’t the AI model — it’s the absence of a trustworthy data foundation underneath it. Building that foundation is what this programme is for.
We build every modernisation programme with knowledge transfer as an explicit deliverable — not as an afterthought. The goal is for your data team to understand what has been built and to be able to operate and extend it independently. For organisations that want ongoing support, we offer a Managed Data Service: a dedicated data engineer working in fortnightly sprints, covering platform monitoring, pipeline maintenance, new source onboarding, and continued development. Quarterly roadmap reviews ensure the platform evolves with the business and incorporates relevant updates from the Microsoft Fabric release cycle. The managed service is a choice — not a dependency we create by under-documenting the build.
Related services
Data platform modernisation rarely stands alone. These are the services that most commonly run alongside or follow a modernisation programme — each with its own dedicated page.
The structured technical process of moving existing workloads — from Synapse, SQL Server, on-premises warehouses, or other platforms — into Microsoft Fabric. Planned to maintain reporting continuity throughout.
Learn more →
Framework design and implementation using Microsoft Purview — data cataloguing, classification, lineage tracking, and access governance across your entire Fabric data estate. Governance built in, not bolted on.
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Design and build of the semantic models, reports, and dashboards that sit on top of the new platform — grounded in reliable data, built for performance, and structured for self-service exploration.
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Ready to start?
A Data Platform Assessment takes two to three weeks and gives you a clear, honest view of where you are and what it would take to get to where you need to be. Fixed fee. No commitment to a build required.
