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AI & Analytics · Data Platform · Governance · MDM · Data Quality
AI Is Only as Good as the Data Behind It.
Get the Foundation Right First. Most AI and analytics programmes fail not because of the models — but because of the data. Inconsistent definitions, poor data quality, fragmented sources, and no governance framework mean that even the best ML models produce unreliable outputs. Symhas builds the cloud data platform infrastructure and governance model that gives your AI and analytics programmes reliable, trusted, governed data — in 12 weeks, integrated with your Oracle ERP and cloud environment.
47→1 Financial Services
47 data sources unified into a single Oracle Cloud data platform — $25B AUM asset manager, 12 weeks
↓81% Financial Services
Reduction in financial reporting time after data platform unification and Oracle EPM integration
35% Healthcare
Improvement in clinical processing speed after 14-system data consolidation to single Oracle platform
12wk All Sectors
Data assessment to production data platform — governed, documented, and integrated
Part of the Symhas AI & Analytics practice
47→1 Maximum data sources unified into a single Oracle Cloud platform in one Symhas engagement
100% Of data platforms deployed pass data quality SLA thresholds on first production assessment
12wk From data audit to production platform — governed, documented, and live
0 Data governance frameworks abandoned — every engagement delivers a working operating model
What We Deliver
Core Capabilities.
All Production-Ready.
Cloud Data Warehouse & Data Lake Architecture Snowflake · BigQuery · Oracle ADW · AWS Redshift
We design and implement cloud data warehouse and lakehouse architectures that bring together Oracle ERP, operational, and third-party data into a single governed, queryable platform — optimised for analytics, AI, and reporting workloads.
Data warehouse design — star schema, data vault, or lakehouse model based on use case
Oracle Autonomous Data Warehouse (ADW) implementation and optimisation
Snowflake, BigQuery, and Redshift architecture and migration
ELT/ETL pipeline design — Oracle Data Integrator, dbt, or cloud-native pipelines
Real-time streaming ingestion for operational data sources
→ Single source of truth for AI, analytics, and reporting — all consuming the same governed data
Data Quality Engineering Profiling · Cleansing · Validation · Monitoring
Systematic data quality engineering — profiling, root-cause analysis, cleansing, standardisation, and continuous monitoring — embedded into your data pipelines rather than applied as a one-off remediation.
Data profiling and quality baseline assessment across all source systems
Automated quality rules embedded in ingestion pipelines — fail-fast at source
Deduplication, standardisation, and referential integrity enforcement
Data quality dashboards — business stakeholders see quality metrics, not IT tickets
Integration with Oracle Enterprise Data Quality (EDQ)
→ Data quality issues caught at ingestion, not discovered by your analysts six weeks later
Master Data Management (MDM) Customer · Product · Supplier · Financial hierarchy
Enterprise MDM for customer, product, supplier, and financial master data — creating a single trusted record across Oracle Fusion, Oracle CX, and connected systems, with governance workflows for ongoing maintenance.
Oracle Customer Data Management (CDM) implementation
Product MDM — single product hierarchy across Oracle SCM and Commerce
Supplier and vendor master consolidation across Oracle Procurement
Golden record creation and survivorship rule design
Stewardship workflows and data quality KPIs for business owners
→ Every analytics and AI model consuming the same master data — no more conflicting customer counts
Data Governance Framework Policy · Stewardship · Lineage · Catalogue
A practical data governance operating model — not a policy document that sits in a drawer. Business data owners, stewardship workflows, a data catalogue, and lineage tracking embedded in how your organisation works with data.
Data governance operating model design — roles, responsibilities, and decision rights
Business glossary and data catalogue implementation (Oracle Data Catalog, Collibra, or Alation)
Data lineage tracking from source to report to AI model
GDPR and regulatory data classification and retention policies
Governance maturity assessment and roadmap
→ Data governance that business users actually follow — because it makes their work easier, not harder
How We Work
From Assessment to
Production in 12 Weeks.

Every engagement follows a structured four-phase delivery model with defined go/no-go gates at each milestone. Fixed price. Fixed timeline.

01
Data Audit & Architecture Design Weeks 1–3

Inventory all data sources, assess quality, and map to business use cases. Design the target platform architecture. Define governance roles and data ownership. Architecture approved by Week 3 go/no-go gate.

02
Platform Build & Pipeline Development Weeks 4–8

Build the data warehouse or lakehouse. Develop ingestion pipelines from Oracle and source systems. Implement data quality rules. Deploy data catalogue and glossary with initial population.

03
Governance Activation & Data Loading Weeks 9–10

Full historical data load with quality validation. Governance workflows activated and business owners trained. Stewardship dashboards live. Initial data quality SLA baseline established.

04
Handover & Operating Model Weeks 11–12

Data engineering team certified on platform operations and pipeline management. Governance operating procedures documented. Symhas moves to quarterly advisory. Platform SLAs active.

Financial Services · Data Unification $25B AUM Asset Manager.
47 Data Sources. One Platform. 81% Less Reporting Time.

A global asset management firm was operating with 47 distinct data sources — trading systems, risk platforms, market data feeds, and Oracle Fusion Finance — with no unified data platform, no master data governance, and a financial reporting process that took 8 days each month to complete.

Symhas built a unified Oracle Cloud data platform — ingesting all 47 sources, establishing master data governance for fund, counterparty, and instrument hierarchies, and integrating with Oracle FCCS and PBCS for automated financial close.

47→1 Data sources unified
↓81% Reporting time
8d→1.5d Financial close cycle
12wk Audit to production
Discuss Your Programme
What was delivered

Oracle Cloud Data Platform — Financial Services Production Deployment

Oracle Autonomous Data Warehouse — central platform for all 47 source feeds
Real-time ingestion for trading and market data; batch ingestion for risk and reference data
Fund, counterparty, and instrument master data governance — golden record per entity
Oracle Data Integrator pipelines with automated data quality validation gates
Oracle FCCS integration — automated financial consolidation from unified data layer
Data lineage from source feed to management account — full regulatory audit trail

"We used to spend the first week of every month just finding out what our data said. Now it tells us automatically — and the first week is for analysis, not aggregation."

— CFO, Global Asset Management Firm
Technology Stack
Platform-Agnostic.
Best Tool for the Job.

We are not tied to any vendor. We select and implement the right technology for your environment, your data, and your team.

Oracle Oracle ADW Autonomous Data Warehouse — managed, self-tuning
Oracle Oracle Data Integrator ELT pipelines from Oracle source systems
Oracle Oracle Data Catalog Enterprise data catalogue and lineage
Cloud DW Snowflake Multi-cloud data platform and data sharing
Cloud DW BigQuery / Redshift GCP and AWS native data warehousing
Transform dbt (data build tool) Modular SQL transformations and data quality tests
Governance Collibra / Alation Enterprise data catalogue and stewardship
Quality Great Expectations / Soda Automated data quality validation in pipelines
Why Symhas
AI Expertise Built from
Production Deployments.
Governance That Business Users Actually Use Most data governance programmes produce frameworks that sit in SharePoint. Symhas designs governance for how people actually work — embedded in existing Oracle workflows, surfaced in dashboards business users check daily, owned by business data stewards who care about the outcome.
Oracle-Native Data Architecture Expertise Oracle ERP data has specific structural patterns — complex GL hierarchies, multi-org setups, flexible field usage. Symhas understands Oracle data models from the inside — having implemented Oracle Fusion across 45+ enterprise programmes.
AI-Ready From Day One The data platform is designed with AI consumption in mind — not retrofitted for ML later. Feature stores, vector embeddings, and model training data pipelines are part of the design, not afterthoughts added when the AI programme starts.
Quality Embedded, Not Bolted On Data quality rules run at ingestion — not as a downstream remediation layer. Bad data fails fast, at source, with business owners alerted immediately. Quality is a property of the pipeline, not a project you run once a year.
Regulatory and Privacy by Design GDPR, HIPAA, and financial regulatory data requirements are built into the governance framework from the start. Data classification, retention policies, and access controls are not retrofitted — they are part of the platform architecture.
Your Team Owns the Platform By Week 12, your data engineering team is certified on platform operations, pipeline management, and governance processes. Symhas steps back to quarterly advisory. No ongoing dependency. No perpetual engagement.
Also in AI & Analytics
Explore the Other
AI & Analytics Capabilities.
Predictive Analytics & Forecasting

AI-driven demand forecasting, financial planning models, and risk prediction built on your governed data platform.
AI & ML Implementation

Custom ML models, LLM integration, and MLOps infrastructure — deployed on the data platform we build together.
Business Intelligence & Reporting

Self-service BI and executive dashboards consuming the governed data your platform produces.
Next Step
Tell Us What Your Data Landscape Looks Like.
We'll Tell You What It Will Take to Fix It. A 30-minute data assessment with a Symhas data architect. We will review your current data sources, quality issues, and governance gaps — and give you an honest view of what a production data platform would require, cost, and deliver.
Book an AI Readiness Assessment View Client Outcomes
No pitch deck. No sales process. An honest conversation about your data and AI programme.