A modernisation approach that eliminates silos, accelerates insights, and scales without breaking your budget or disrupting operations.
Most organizations aren't drowning in data – they're drowning in the complexity of making it useful. True Data Modernisation isn't about ripping out everything and starting over. It's about creating connected, reliable data flows that turn information into decisions faster than your competition can react. At Fornax, we modernise data architectures the way engineers think: start with outcomes, design for reality, and build bridges from where you are to where you need to be. Our approach combines tactical wins with strategic architecture – getting you quick value while laying foundations that compound over time.
We've learned that successful modernisation balances three tensions: speed vs. stability, innovation vs. compliance, and centralisation vs. autonomy. The result: a data platform that accelerates decision-making, reduces operational overhead, and creates a foundation your teams actually want to build on.
How do we modernise without breaking what's working today?
Which systems should we replace first, and which can wait?
How do we get our teams to trust and use the new platform?
What's the real timeline and cost to see measurable improvement?
How do we balance innovation with regulatory requirements?
Honest evaluation of what's working, what's broken, and the true cost of staying put vs. moving forward.
Modern patterns (lakehouse, data mesh, real-time streaming) that integrate with your existing systems rather than replacing everything at once.
A phased approach that delivers value every quarter while building toward your target state – no big-bang transformations.
Right-sizing infrastructure, optimising query patterns, and implementing cost controls that scale with usage.
Automated monitoring, validation, and lineage tracking that prevents bad data from poisoning downstream decisions.
Upskilling plans, workflow redesigns, and adoption strategies that get your people excited about the new capabilities.
How we modernise your data
Diagnose Reality & Define Success
Audit current data flows, identify pain points, and establish measurable outcomes that matter to your business.
Design the Bridge Architecture
With unified, accessible data across the organization.Create transition patterns that deliver value immediately while building toward your modern target state.
Pilot with Production Workloads
Prove the approach with real business use cases, not toy examples, to build confidence and momentum.
Scale with Governance Built-In
Expand successful patterns across the organization with quality, security, and compliance baked into every layer.
Optimise & Evolve
Continuous performance tuning, cost management, and capability expansion as your needs mature.
Transparent TCO modeling
We map the full cost of modernisation – including hidden expenses like training, downtime, and ongoing maintenance – so you can plan realistic budgets.
Value realization tracking
Instead of abstract efficiency gains, we measure concrete improvements: faster report generation, reduced manual work, better decision speed. Every modernisation investment ties to business impact.
Incremental ROI approach
Each phase delivers measurable value while building toward larger goals. Your modernisation pays for itself progressively, not all at the end.
Risk-aware migration
Critical systems get extra protection; experimental workloads move fast. We calibrate governance to match business impact.
Compliance by design
Privacy, retention, and audit requirements are engineered into data flows from day one, not retrofitted later.
Change management integration
Technical modernisation includes process updates, training programs, and success metrics that ensure adoption sticks.
Future-proof standards
Your architecture evolves with changing regulations, vendor landscapes, and business requirements
Explore All Capabilities
Strategy and Transformation
We help leaders build strategies that don’t sit in decks, but those that scale, adapt, and deliver measurable value.
Data Foundation
A modern data foundation gives you one source of truth for analytics, AI, and decision-making - engineered for reliability, speed, and scale.
Advanced Analytics & Insights
We build analytics platforms and production models so leaders make faster, confident decisions at scale.
AI / ML Innovation
From robust AI engineering to production-grade LLM solutions and ML platforms, Fornax turns experimentation into scalable impact.
Start with a data virtualization layer that creates modern APIs over existing systems. This lets you build new analytics and applications while keeping core operations untouched. Migrate gradually by workload risk and business value, proving each step before expanding. Most successful modernisations happen in 12-18 month phases, not multi-year big-bang projects.
Focus on automation and elimination first – remove manual data tasks before adding new capabilities. Begin with high-impact, low-complexity wins that free up time for bigger changes. We typically recommend starting with data quality automation and self-service analytics that reduce daily firefighting. Success here creates bandwidth for larger architectural changes.
Track decision speed metrics: time from question to answer, frequency of data-driven decisions, and business outcome improvements. Measure reduced manual effort in hours saved, faster monthly close cycles, or eliminated urgent data requests. The strongest ROI comes from enabling decisions you couldn't make before, not just making current processes faster.
Most regulated industries settle on hybrid patterns: sensitive/regulated data stays on-premises with strict controls, while analytics and development workloads leverage cloud elasticity. The key is designing consistent governance and seamless data movement between environments. Your architecture should optimize for compliance requirements first, then performance and cost.
Implement data contracts and standardized integration patterns from day one. Each new system must publish its data through agreed-upon interfaces and follow common security/quality standards. This prevents the "integration spaghetti" that happens when teams build point-to-point connections. Think of it as microservices for data – loosely coupled but highly cohesive.
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