Our data practice at a glance
- 12 data & AI services
- AI data agents
- Oracle and Microsoft first
- Legacy reporting to AI-based reporting
- Platform-agnostic delivery
- Oracle EBS & Fusion data specialists
- One version of every KPI
Data and AI that your people and your agents can trust
Lakehouse, pipelines, BI, machine learning and AI data agents from one accountable Data & Analytics Practice. Built first on Oracle AI Data Platform, Oracle Analytics Cloud and Microsoft Fabric, and on Databricks, Snowflake, Tableau and Qlik where you already run them. Every engagement works to one data model, one set of KPI definitions and one governance standard.
One data model, one standard
Every service runs on one data model, one KPI dictionary and one governance standard, whichever platform it is delivered on.
- 4 Foundation: lakehouse and warehouse, pipelines, data quality and master data, warehouse modernisation
- 3 Analytics: BI, semantic models, reporting modernisation
- 3 Intelligence: data science and ML, MLOps, AI and data agents
- 2 Assurance & specialist: governance and security, Oracle ERP analytics
One practice, 12 services, from raw data to decisions
Foundation services get data in and keep it clean. Analytics services turn it into numbers people trust. Intelligence services predict, explain and act on it. Assurance and specialist services keep it governed and connect it to Oracle ERP. You don't need several vendors.
- Foundation (4): Lakehouse and warehouse, Data pipelines and integration, Data quality and master data, Data warehouse modernisation
- Analytics (3): BI and visual analytics, Semantic models and self-service, Reporting modernisation: traditional to AI-based reporting
- Intelligence (3): Data science and machine learning, MLOps, AI and data agents
- Assurance & specialist (2): Data governance and security, Oracle ERP analytics: Oracle EBS and Oracle Fusion
Our Data & Analytics Services
Lakehouse & Warehouse
Governed lakehouses on Oracle AIDP, Microsoft Fabric, Databricks and Snowflake.
Data Warehouse Modernisation
Migration from legacy BI and ETL platforms to modern cloud architectures.
Data Pipelines & Integration
Batch, streaming and CDC pipelines from ERP, CRM, IoT and SaaS.
Data Quality & Master Data
Rules, reconciliation and golden records your auditors accept.
BI & Visual Analytics
Dashboards on Oracle Analytics Cloud, Power BI, Tableau and Qlik Sense.
Semantic Models & Self-Service
One definition of every KPI, shared by people and agents.
AI-Based Reporting Migration
From OBIEE, Discoverer, SSRS and spreadsheets to AI-based reporting platforms.
Data Science & ML
Forecasting, anomaly detection and optimisation, run in production with MLOps.
AI & Data Agents
Agents that answer business questions over governed data, with guardrails built in.
Data Governance & Security
Role-based access, data quality, lineage, auditing and secure AI adoption, with catalogue and data residency built in.
Oracle ERP Analytics
Oracle EBS and Oracle Fusion data, modelled and reconciled for analytics and AI.
Industry-Specific Analytics
Finance, procurement, HR, manufacturing, supply chain and executive analytics.
Tell us the challenge. We'll point to the service.
Pick the closest challenge below, or talk to a data expert about your decisions.
Where to start
01
Our Oracle EBS or Fusion data is hard to report on
We offer Oracle ERP analytics on Oracle AIDP, OAC or Microsoft Fabric.
Start with: ERP data readiness assessment
02
Every department has a different number
We offer semantic models, a KPI dictionary and BI rationalisation.
Start with: KPI and report inventory
03
We need to choose a data platform
We offer lakehouse design across Oracle AIDP, Microsoft Fabric, Databricks and Snowflake.
Start with: platform fit assessment
04
Our reporting is still on OBIEE, Discoverer, SSRS or spreadsheets
We offer reporting modernisation to AI-based reporting on OAC, Power BI, Tableau or Qlik Sense.
Start with: report inventory and migration assessment
05
We want people to ask data questions in plain language
We offer AI and data agents with guardrails.
Start with: data agent pilot on one domain (6–8 weeks)
06
We suspect fraud or leakage in our transactions
We offer data science, anomaly detection and alerting.
Start with: fraud rules workshop
Lakehouse and warehouse: from raw source to certified data
Every layer of the lakehouse has a named owner, so nothing falls between teams. Source owners keep the systems of record, our engineers build and run the layers, and your business data stewards certify what reaches dashboards and agents. Open formats first, quality at every layer, full lineage.
01
Source
What happened in the business system?
Owner: Application owners (ERP, CRM, IoT)
02
Bronze (raw)
Did we capture everything, unchanged?
Owner: 4i data engineers
03
Silver (cleansed)
Is the data complete, de-duplicated and conformed?
Owner: 4i data engineers, rules agreed with stewards
04
Gold (business)
Do the facts and dimensions match the business rules?
Owner: 4i engineers, signed off by data stewards
05
Semantic model
Does every team and agent use the same KPI definition?
Owner: Business data owners, with 4i support
06
Consumption
Can people and agents get the answer they need?
Owner: Business users, analysts and data agents
What each service covers
Pick a service to see what we deliver.
Data that arrives on time and adds up. Pipelines move the data. Quality rules prove it is right. Master data makes sure a customer, supplier or item means the same thing in every system.
01
Data pipelines
Batch, streaming and change data capture. Orchestration, retries and alerting on every run, and pipelines as code, deployed through CI/CD.
02
Data quality
Checked as data moves, not after. Failed records are quarantined, not silently dropped, and data contracts are agreed with source owners.
03
Master data
One customer, one supplier, one item. Golden records are published back to ERP and CRM, with stewardship workflows for exceptions.
Fewer reports, better decisions. Most organisations don't need more dashboards. They need fewer, trusted ones. Our BI work starts with a report inventory and a KPI dictionary, and only then builds.
01
What we build
Assess, build, prove
Every engagement starts with an agreed data strategy and KPI dictionary, so scope, sources, platforms and acceptance criteria are clear before any pipeline is built. Release cycle: source onboarding, bronze and silver, gold and semantic model, dashboards and agents, reconciliation, UAT and data sign-off.
- 1
Assess
Sources, data quality, current reports and the decisions that matter.
- 2
Design
Target architecture, semantic model, security model and roadmap.
- 3
Build
Pipelines, models, dashboards and agents in short releases.
- 4
Prove
Reconciliation to source, user acceptance and data sign-off before go-live.
- 5
Operate
Monitoring, cost optimisation and a backlog of new questions.
Gates, incidents and standards
Acceptance criteria are agreed up front. Data incidents carry response targets, and a daily stand-up confirms severity and ownership during build and hypercare.
All thresholds are typical baselines and are agreed per engagement. Critical: wrong figures in an executive or regulatory report. High: a pipeline failure delaying a daily report. Response times are triage targets; fix times depend on the issue owner.
01
Data load gate
All planned sources loaded; row counts and control totals reconcile to source.
02
UAT gate
Business users confirm KPIs against agreed test questions, with no open Critical issues.
03
Go-live gate
Data owner sign-off and a recorded Go/No-Go.
04
Critical incidents
Response: same day.
05
High incidents
Response: within 1 working day.
06
Medium and Low incidents
Medium within the cycle, Low in a planned release.
What you receive in every engagement.
01
Data strategy and roadmap
Where the data practice is going and in what order.
02
Target architecture
Platforms, layers and how they connect.
03
Source-to-target mapping
Every field traced from source to report.
04
KPI dictionary and semantic model
One definition of every number.
Why organisations choose our Data & Analytics Practice
- Oracle and Microsoft depth: Oracle Cloud Select Partner and Microsoft AI Cloud Partner, with certified architects on AIDP, OAC, Fabric and Power BI.
- ERP-native data skills: Two decades of Oracle EBS and Fusion delivery, so extracts reconcile to the ledger.
- Agents in production: Claude Certified Partner, building MCP servers and data agents that act safely in enterprise systems.
- Platform-agnostic: We recommend what fits your estate, including Databricks, Snowflake, Tableau or Qlik.
- Governance built in: Security, lineage, residency and audit designed in from the first sprint.
- Knowledge transfer: Training, runbooks and documentation so your team owns the platform.
Frequently Asked Questions
Which data and analytics services do you offer?
Twelve services across four groups: four foundation (lakehouse and warehouse, pipelines and integration, data quality and master data, data warehouse modernisation), three analytics (BI and visual analytics, semantic models and self-service, migration from traditional to AI-based reporting), three intelligence (data science and ML, MLOps, AI and data agents), and two assurance and specialist (data governance and security, Oracle ERP analytics).
Which platform should we choose: Oracle AIDP, Microsoft Fabric, Databricks or Snowflake?
Oracle EBS and Fusion customers, especially on OCI, should start with Oracle AI Data Platform and Oracle Analytics Cloud. Microsoft-centric estates often suit Fabric and Power BI. Databricks and Snowflake fit organisations already invested in them or running heavy data science at scale. Our platform fit assessment compares the options on your actual workloads.
What is a data agent, and is it safe?
A data agent is an AI assistant connected to your governed data. It turns a question such as "why did margin drop in Q3?" into queries, runs them under your security rules and shows its reasoning. Agents see only what the asking user may see, act only within approved permissions, stop for human approval before changing data, and log every query.
Will you push one platform on us?
No. Oracle and Microsoft are our core alliances, but we recommend what fits your estate, skills and budget, including Databricks, Snowflake, Tableau or Qlik Sense when they are the better fit.
How do you migrate from traditional reporting to AI-based reporting?
In five steps. We inventory every report and its usage, retire duplicates and unused reports, rebuild the business logic once in a governed semantic model, convert the remaining reports with agent-assisted tooling and reconcile old against new, then switch on AI assistants and data agents and retire the legacy tool. Both run in parallel until outputs match and users sign off.
Let's talk about your data goals
Tell us the decision you need to make faster, the systems your data lives in and the platform you run today. We'll propose the right-sized roadmap. Discovery call: decisions, data sources and platforms. Rapid assessment: data readiness, platform fit and effort estimate. Proposal: right-sized roadmap, often starting with a data agent pilot.