AutoDriving Real-Time Intelligence

The Challenge: Data-Rich, Insight-Poor

Despite NetSuite's rich ecosystem, the client faced major issues that were affecting business performance.

• Limited data retention and accessibility outside the NetSuite instance.
• Significant performance bottlenecks affecting reports and dashboards.
• No unified view of daily operational and financial performance across the company and its clients.
• Frequent order mismatches and missed shipment deadlines.
• Lack of visibility into customer behaviours and sales trends.
• Challenges in tracking stock availability, demand and procurement needs.
• Inability to track shipment dates or get real-time supply chain insights.
• Complex data architecture within NetSuite (which uses deeply embedded views rather than transparent table structures).

Their internal teams struggled to extract actionable insights using NetSuite's native reporting tools.

The integration of a second NetSuite instance post-acquisition heightened the challenges, prompting them to turn to Voco.

Our Approach: Building an Enterprise-Grade Data & Reporting Architecture

We approached this project with a data-first, cloud native mindset, combining modern integration tools with sound data modelling and reporting practises. Our solution and transformation for the client were as follows:

1. Data Extraction & Integration with CData

NetSuite does not expose its database tables directly in a traditional relational model. Instead, it works heavily on views. To access the raw and processed data from NetSuite:

• We used CData connectors to synchronise NetSuite data into Azure SQL Database
• This approach gave us a complete control over the data, including historical and transactional information, across all modules (finance, CRM inventory, etc)
• The Sync process involved a complete extraction of custom objects and stand records, requiring relationships to be reconstructed.

2. Azure SQL: The Cloud-Hosted Analytical Backbone

We deployed a Microsoft SQL Server instance in Azure to serve as the single source of truth for reporting and analytics:

• Tables were structured and normalised to match the NetSuite schema.
• Custom business logic from NetSuite (e.g. tax calculations, order status logic) was reverse-engineered and replicated using T-SQL procedures and views.
• We created data pipelines and scheduled jobs to keep this Azure SQL repository in sync with NetSuite

3. Transformations, Deduplication, and Business Logic Modelling

Extracted data required heavy lifting before it could be visualised.

• Deduplication of transactional and customer records.
• Merging of related tables (e.g. orders, shipments, inventory stock levels, etc).
• Date mapping for tracking delivery windows and order delays.
• Key business rules implemented to ensure accuracy across metrics.

This formed the foundations of reliable, decision-grade reporting

4. Dashboarding with Power BI

We built a suite of Power BI dashboards tailored to different business functions:

• Operational Dashboard: Displays real-time orders, pending shipments, supplier status, and warehouse KPI's.
• Finance Dashboards: Summarises revenue, outstanding payments, cash flow and cost centres.
• Sales and CRM Insights: Tracks customer engagement, repeat purchases, sales rep performance, and opportunity pipeline.
• Inventory Heatmap: Visualises stock movements, shortages and reorder points.

Each dashboard was powered by complex T-SQL queries that joined multiple NetSuite-derived tables to produce 260-degree business views.