OLAP Analysis — Multidimensional Data Analysis

OLAP (Online Analytical Processing) describes a multidimensional approach to data analysis that pre-aggregates business data into cubes, allowing users to slice, drill down and pivot across dimensions in sub-second response time. OLAP sits between the operational ERP system and ad-hoc reporting, and is the data-engine behind most business intelligence dashboards and management cockpits in mid-market companies.

How OLAP works

OLAP organises business data along dimensions (time, customer, product, region, organisational unit) and measures (revenue, quantity, margin, cost). The intersection of all dimensions forms a multidimensional cube. Users navigate by slicing (selecting a single value on one dimension), dicing (selecting a sub-cube across multiple dimensions), drill-down (moving from a coarse level — year — to finer levels — quarter, month, day) and pivot (rotating dimensions in the view).

Two technical approaches dominate. MOLAP (multidimensional OLAP) stores the cube in a dedicated multidimensional database, pre-aggregated for speed — classic implementations: Microsoft Analysis Services, IBM Cognos TM1, Oracle Essbase. ROLAP (relational OLAP) stores data in a relational warehouse and computes aggregates on-the-fly via SQL — common in modern cloud BI tools (Tableau, Power BI on top of Snowflake or BigQuery).

OLAP vs. OLTP

The ERP system itself is an OLTP system (Online Transaction Processing), optimised for high-volume short transactions: posting an invoice, updating a stock level, creating a purchase order. OLAP is the opposite design point: optimised for complex analytical queries over historical data spanning many transactions. Running analytical queries directly on the OLTP database is technically possible but kills operational performance. The standard pattern: ETL/ELT moves data from the ERP into a data warehouse, OLAP cubes are built from the warehouse, BI dashboards consume the cubes.

Typical use cases

Sales analysis (revenue by region, customer segment, product category, sales rep over time), purchasing analysis (spend by vendor, category and commodity), financial reporting (P&L by cost centre and business unit), inventory analysis (stock turn, slow-movers, fast-movers by location), HR analytics (headcount, attrition, payroll by department) and operational KPIs across the organisation. Decision-support cockpits for management typically combine 4–8 OLAP cubes covering the most-watched business areas.

Frequently Asked Questions

Is OLAP still relevant in 2026?

Yes, although the implementation has shifted. Classic dedicated MOLAP servers (Essbase, TM1) are still in production at large enterprises but no longer the default choice for new projects. Modern setups use cloud data warehouses (Snowflake, BigQuery, Databricks) with semantic layers (dbt, Cube.dev, Looker) that expose OLAP-style dimensional models to BI tools. The conceptual model — dimensions, measures, hierarchies, slicing, drill-down — remains the same; the storage and compute have moved to the cloud.

Does our ERP system include OLAP?

Larger ERPs ship with embedded analytics that internally use OLAP-style aggregation: SAP S/4HANA Embedded Analytics on top of HANA, Microsoft Dynamics 365 with Power BI integration, Oracle Fusion ERP with Oracle Analytics Cloud. Mid-market ERPs typically expose a data export interface and rely on an external BI tool. For complex cross-system analytics — ERP plus CRM plus web shop plus accounting — an independent data warehouse with its own OLAP layer is the standard architecture.

MOLAP, ROLAP or HOLAP — which one for our project?

MOLAP gives the fastest query response and works well for stable, well-understood KPI cubes with limited data volume (under a few hundred GB). ROLAP scales further, handles billions of rows in modern cloud warehouses and avoids cube-rebuild times, but pays a per-query compute cost. HOLAP combines both: pre-aggregated summaries in MOLAP for the hot metrics, detailed drill-through hitting the ROLAP warehouse on demand. For most mid-market projects in 2026 the simplest answer is ROLAP on a cloud warehouse with a semantic-layer tool — the operational complexity of separate MOLAP infrastructure is rarely worth it.