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Data Engineering
Trusted data for decisions, models and compliance
Governed data foundations for reporting and AI, with clear ownership and minimal duplication.
Client result
90 daysfrom scattered claims data to a certified, AI-ready data product
US healthcare analytics company
Read the case studyfrom idea to impact, across the systems lifecycle, this service sits here
Data readiness for AI
Building data that AI can trust
Use case driven foundations it depends on and the gaps we usually find first.
needs
- Several years of clean sales history at the right level of detail
- External signals such as prices, promotions and weather joined on shared keys
- Agreed definitions for product, location and calendar
- Automated freshness and completeness checks
Gaps we usually find
- History split across ERP versions with changed product codes
- Promotions tracked in spreadsheets
- No named owner for the product hierarchy
Where we start
Fix the sales history and the product hierarchy before any model is trained.
Ask for a data readiness reviewneeds
- Documents with current versions and named owners
- Access rules that follow each user into retrieval
- Metadata to filter by department, region and date
- A way to retire outdated content
Gaps we usually find
- Duplicate and outdated documents across drives
- Permissions the retrieval layer ignores
- No record of which source answered a question
Where we start
Clean and tag the sources, then carry the access rules into retrieval.
Ask for a data readiness reviewneeds
- Lineage from every reported figure back to its source
- Finance and operations data that reconcile
- Change history for definitions and adjustments
- Control evidence an auditor can read
Gaps we usually find
- Manual adjustments made outside the system
- Figures that reconcile only at month end
- Lineage that lives in people’s heads
Where we start
Trace the reported figures end to end and automate the reconciliations.
Ask for a data readiness reviewneeds
- One customer identity across channels
- Consent captured and respected for each purpose
- Customer events available within minutes, not days
- Features shared between analytics and models
Gaps we usually find
- The same customer under several IDs
- Consent stored apart from the data it governs
- Batch loads that arrive a day late
Where we start
Resolve customer identity first, and link consent to it.
Ask for a data readiness reviewWhat we do
Transform data into measurable outcomes
From enterprise data architecture and governance to reporting and insight.
AI-accelerated
Data and AI governance
Ownership, quality and access rules built into pipelines, so governance happens on every load rather than in a quarterly review.
Data ownership model
A named owner for each domain and data product, with clear decision rights.
Automated quality checks
Freshness, completeness and accuracy tests that run with every pipeline.
AI-acceleratedLineage and cataloging
Where each field comes from and where it goes, kept current automatically.
AI-acceleratedPrivacy and access controls
Masking, synthetic data and role-based access for regulated data.
Enterprise data architecture
Platforms and data models designed for both reporting and AI, on the cloud and tools you already use.
Lakehouse and warehouse design
Storage and compute chosen for your workloads and budget.
Zero-copy integration
Shared access across platforms such as Salesforce Data Cloud, without copying.
Streaming and change data capture
Near-real-time feeds where the business needs them.
Data products and semantic layer
Business definitions agreed once and reused by every tool.
Data analytics
Dashboards, self-service analytics and AI-ready datasets that people actually open.
Executive and operational dashboards
A few trusted measures per audience, agreed with that audience.
Self-service analytics
Governed datasets business users can explore without breaking anything.
AI-ready datasets
Curated, labeled data for training and evaluating models.
AI-acceleratedPlain-English queries
Questions asked of governed data in everyday language, with answers that show their source.
AI-acceleratedHow we engage
From assessment to AI-ready data
Assess
We map the sources, owners and gaps against the required and applicable use cases, and agree what to fix first.
You getA ranked list of data gaps per use case
Foundation sprint
We build the first governed pipeline and data product for one use case, with checks, lineage and access rules in place.
You getOne use case running on trusted data
Data products
We extend to the next use cases, re-using the same definitions, checks and platform patterns.
You getShared data products teams can build on
Run it
Teams can now operate the platform, with monitoring, quality alerts and a backlog tied to business priorities.
You getA platform that stays trustworthy as it grows
Case study
Healthcare & Life Sciences | US healthcare analytics company
From scattered claims data to a certified AI-ready data product
Claims, repricing, provider, contract and EDI data sat in separate systems, with no governed, repeatable way to onboard data domains. A new application needed certified out-of-network claim repricing data, with HIPAA-ready controls from day one.
We built an AI-ready lakehouse foundation on Oracle AI Data Platform and OCI, using federation-first connectivity (zero-copy catalog, JDBC and GoldenGate change capture), bronze, silver and gold layers, data contracts and automated quality gates. AI accelerators helped build profiles, mappings and tests faster, with human-in-the-loop approval. The first certified data product feeds the new application and an Oracle Analytics savings view.
Start with the data your next AI project depends on
We’ll trace it from source to model, show where it breaks and fix the foundations first.