Data Solutions

Data Unification and Integration Strategy for a Petroleum Enterprise

Challenge

A leading APAC petroleum company sought to unify disparate data sources across both its Business and Finance functions.
Over time, multiple systems (ERP, CRM, production tracking, supply chain, and finance reporting) had evolved in isolation, resulting in inconsistent reporting, duplicated effort, and significant manual reconciliation overhead.

The finance division required a scalable, cost-efficient data unification strategy that:

• Minimized implementation time and disruption,

• Reduced reliance on manual processes,

• Supported increasing data volumes and analytics complexity, and

• Provided a strong foundation for governance, reporting, and future AI enablement.

Harmonic Strategy was engaged to perform a comparative analysis of modern data-integration architectures and recommend the optimal approach based on the client’s operational maturity, time constraints, and future scalability goals.

Solution

Harmonic assessed five possible architectures — Data Mart, Data Lake, Data Virtualisation, Data Fabric, and Data Mesh — using a two-dimensional comparison of Technology Dependency vs. Cultural Change Requirements, supported by time-to-deploy, governance capability, and scalability ratings.

Evaluation Summary

Approach Cultural Change Tech Dependency Deployment Speed Scalability Best Fit

Data Mart Low Medium Fast Moderate Tactical BI reporting Data Lake Medium Medium Moderate High Advanced analytics & ML Data Virtualisation Low High Very Fast High Real-time unified access Data Fabric Low–Medium Very High Moderate Very High Strategic integration platform Data Mesh High Medium–High Slow Very High Long-term federated model

Key Findings

• Fastest deployment: Data Virtualisation and Data Mart (weeks, not months).

• Highest integration depth: Data Fabric (metadata-driven automation and governance).

• Greatest flexibility for future AI/analytics: Data Lake or Fabric.

• Most cultural change required: Data Mesh (domain ownership model).

Given the petroleum firm’s limited appetite for long change programs but strong need for governance and scalability, the analysis concluded that a hybrid Data Fabric + Data Virtualisation model was the optimal solution.

Chosen Option — Hybrid Data Fabric with Virtualisation Layer

Why Selected

• Fast Implementation:
  Data Virtualisation tools (e.g., Denodo, Starburst, Dremio) provide rapid unification without moving data, ideal for immediate visibility across ERP, finance, and operations.

• Scalable Architecture:
  Data Fabric solutions (e.g., Informatica IDMC, IBM Cloud Pak, Talend) offer a governed integration backbone with automated metadata discovery, data lineage, and AI-assisted cataloging.

• Strong Governance and Security:
  Built-in policy enforcement, lineage tracking, and role-based access controls enable regulatory-grade transparency.

• Future-Ready Design:
  Supports hybrid/multi-cloud data expansion, enabling the finance function to later integrate real-time analytics, ESG reporting, and AI-driven forecasting.

Implementation Roadmap

1️⃣ Phase 1 — Diagnostic and Architecture Blueprint (4 weeks)

• Map existing data sources across Business & Finance.

• Identify critical integration points (ERP, CRM, Treasury, Trading, Production).

• Define logical architecture: virtual layer + metadata catalogue + governance services.

2️⃣ Phase 2 — Pilot Deployment (6–8 weeks)

• Deploy Data Virtualisation Layer for Finance reporting consolidation.

• Integrate 3–5 core data sources using Denodo or equivalent platform.

• Implement data catalog (Collibra / Atlan) for visibility and governance.

3️⃣ Phase 3 — Scale to Enterprise Fabric (3–6 months)

• Extend to Business, Supply Chain, and ESG data.

• Implement Data Fabric orchestration for metadata automation, quality rules, and AI-driven lineage.

• Introduce standard APIs for downstream analytics and dashboards.

4️⃣ Phase 4 — Continuous Optimisation

• Add monitoring dashboards for data quality KPIs.

• Gradually introduce predictive models (ML-ready structure).

• Establish Data Stewardship roles and governance council.

Key Deliverables

• Data Architecture Blueprint (Hybrid Fabric + Virtualisation model)

• Vendor Evaluation Matrix with total cost of ownership (TCO)

• Data Governance Framework (policies, catalog, lineage)

• Pilot Implementation Playbook for Finance integration

• Scalability & AI Enablement Roadmap

Client Benefits

• Immediate efficiency gains: Unified reporting achieved within weeks without heavy ETL builds.

• Streamlined governance: Centralised visibility of data lineage and ownership.

• Scalable architecture: Seamless growth path as data volumes increase.

• Reduced operational risk: Eliminated reconciliation errors and redundant manual processes.

• Future-proofing: Foundation laid for predictive analytics, ESG metrics, and AI-driven insights.