Data strategy and engineering
Data foundations that make AI work.
Strategy, engineering, and governance for the data layer underneath your AI ambition. From siloed to AI-ready in measurable phases, on Databricks or AWS.
100% satisfaction guarantee. No obligation. No sales pitch. Just a 30-minute conversation.
Data platform builds
Databricks Partner
Google rating
Built on
Why data foundations decide AI outcomes
Most organisations don't know what data they have, where it lives, or how it can drive AI value. Without a clear understanding of your data landscape, AI initiatives fail before they start.
Our Data Strategy & Engineering practice gives you the foundation for credible AI. We map your current state, identify the highest-leverage opportunities, and build a phased roadmap that ties technology to business outcomes on Databricks or AWS.
of data goes unused in most organisations
of AI projects fail due to poor data foundation
faster AI deployment with proper discovery
Our Service Components
Comprehensive discovery, engineering, and strategy services tailored to your organisation
- Identify data sources across systems, databases, and cloud platforms
- Document data lineage and relationships
- Assess data quality and completeness
- Map data to business processes and use cases
- Create searchable data catalog for easy discovery
- Assess data quality and accessibility
- Evaluate technical infrastructure readiness
- Review governance and compliance frameworks
- Identify high-value AI use cases
- Benchmark against industry best practices
- Prioritise use cases by business value and feasibility
- Define success metrics and KPIs
- Create phased implementation plan
- Estimate resource requirements and timelines
- Identify quick wins and long-term initiatives
- Quantify potential ROI and business impact
- Identify cost savings and efficiency gains
- Assess competitive advantages
- Develop executive presentations
- Create investment proposals for stakeholders
Our Approach
Phase 1: Discovery
2-3 weeks
- Stakeholder interviews across business and IT
- Data source inventory and cataloging
- Current state assessment
- Use case identification workshop
Phase 2: Analysis
2-3 weeks
- Data quality and readiness analysis
- Gap analysis against AI requirements
- Technical infrastructure assessment
- Governance and compliance review
Phase 3: Strategy
2-3 weeks
- Prioritized use case roadmap
- Technology recommendations
- Resource and budget planning
- Implementation timeline and milestones
What you get when you engage us
Every engagement is scoped honestly to the work that pays back. Here is the full bill of goods.
Data inventory and catalogue
Full audit of data sources across your systems, cloud, and SaaS. Lineage, quality, ownership, and business value mapped end to end.
AI readiness assessment
Where you sit on data quality, infrastructure, governance, and skills. Gaps named clearly. Industry benchmarks. The use cases worth pursuing first.
Strategic data roadmap
Phased plan from siloed to AI-ready. Use case priority, platform shape, milestones, success metrics, and resource plan your CFO can read.
Reference architecture
Target-state architecture on Databricks lakehouse, AWS native, or hybrid. Documented data contracts, ingestion patterns, and serving layers.
Governance framework
Access control, data contracts, lineage, Unity Catalog or Lake Formation policy. Designed against your compliance obligations and signed off with your CISO.
Quick-win identification
The two or three AI use cases that can ship inside the first phase, on top of a clean slice of data, while the bigger platform comes together.
Executive presentation
Board-ready deck. Business case, investment plan, risk, and the credible AI outcomes your data can support today.
What You'll Receive
- Complete catalog of data sources
- Data quality assessment
- Lineage and relationship mapping
- Business value classification
- Current state evaluation
- Gap analysis and recommendations
- Industry benchmarking
- Risk and opportunity identification
- Prioritized use case roadmap
- Implementation timeline
- Resource and budget planning
- Success metrics and KPIs
- Business case and ROI analysis
- Investment proposal
- Risk mitigation strategies
- Next steps and recommendations
100% satisfaction guarantee
If the engagement doesn't land, we make it right. That can mean rerunning the work, refunding the engagement, or both. We back the work, not just the booking.
Honest scoping up front. If a full strategy engagement is overkill for your situation, we will tell you and point you at the smaller piece of work that actually pays back.
Common questions
Where do most data programs go wrong?
Buying a platform before mapping the data, the use cases, or the team capability. We start the other way around. We map what data you have, what AI use cases actually pay back, and what platform shape (Databricks lakehouse, AWS native, hybrid) fits your team. Then we build.
Databricks or AWS, which should we pick?
It depends on your team, your existing footprint, and your AI roadmap. Databricks lakehouse is excellent for unified analytics and AI on the same data. AWS native (S3, Glue, Redshift, SageMaker, Bedrock) suits teams already deep in AWS. We are an official Databricks Partner and run AWS-certified architects, so we will tell you straight, not push the platform we earn most on.
How long until we see AI value on top of new data foundations?
Phased. The first AI use case usually ships within the first phase, on top of a slice of clean data. The full lakehouse or modern data platform takes longer, but you should not wait for it to start delivering. Our job is to find the highest-leverage moves you can make in 90 days while the platform comes together.
Do you work with our existing data team?
Yes, that is the default. Most engagements are us alongside your team, lifting capability and shipping with them, not replacing them. We hand over what we build with documentation, runbooks, and pair-programming sessions so your team owns it cleanly.
What about governance and compliance?
Governance is built in from day one, not bolted on. We design the data contracts, lineage, access controls, and Unity Catalog or AWS Lake Formation policy in line with your compliance obligations (APRA CPS 234, Privacy Act, GDPR if you trade in Europe). The result is a data layer your CISO and legal team can sign off on.
Can you help us scope before we commit to a full engagement?
Yes. The discovery call is honest scoping. If a full data strategy engagement is overkill, we will tell you and point you at a smaller assessment or a quick targeted piece of engineering. We back the work with a 100% satisfaction guarantee.
Need help on the engineering, not just the strategy?
We do both. See our Databricks cloud architecture, AWS AI & ML, and AI-ready governance practices, or take the free AI Readiness Diagnostic to see where to start.
Ready to talk?
Three ways to start a conversation
Every path goes to a senior consultant, not a sales rep. Pick whichever suits you.
Prefer to chat now?
Our AI assistant qualifies and books a call in under 90 seconds. Available 24/7.
Call us directly
Mon to Fri, 8:30am to 5:30pm AEDT
+61 2 5300 3040No obligation. We won't add you to a marketing list.
Get the data foundations right, then ship AI.
Book a discovery call. We will sketch your data maturity journey, identify the highest-leverage moves, and tell you what AI can credibly do on your data today.
No obligation. No sales pitch. Just a 30-minute conversation.