As cloud analytics platforms reshape the data landscape, many finance leaders wonder if traditional OLAP cubes still deserve a place in their technology stack. BARC's survey of 1,187 organizations reveals 89% of finance departments continue to use OLAP-derived analytics despite cloud migrations.

This article explores what OLAP cubes are, how they work, their benefits and limitations, and whether they remain relevant in today's cloud-first world of financial planning and analysis.

What is an OLAP Cube?

An OLAP cube is a data structure that organizes information into multiple dimensions for fast analysis and reporting. OLAP stands for Online Analytical Processing, a technology designed to quickly answer complex analytical queries.

What is an olap cube exactly? Think of it like a Rubik's cube where each face represents a different dimension of your data (time, geography, products), allowing you to examine information from multiple angles simultaneously.

Developed in the 1990s, OLAP cubes solved the challenge of slow analytical queries against transactional databases. They pre-aggregate data along common dimensions that finance teams need, such as comparing quarterly revenue across different product lines or regions.

• Dimensions: Categories for analysis (time periods, regions, products)

• Measures: Numerical values being analyzed (revenue, expenses, headcount)

• Hierarchies: Organizational structures for drilling down (Year > Quarter > Month)

For finance leaders, OLAP cubes provided a way to quickly analyze performance without waiting for IT to write custom reports. A data cube refers to a multi-dimensional dataset arranged in a way that optimizes analytical processing rather than transaction processing.

How OLAP Cubes Work in a Data Warehouse

OLAP cubes typically sit on top of a data warehouse as the final layer in a data processing pipeline. Raw data flows from source systems (ERP, CRM, HR platforms) into the data warehouse where it's cleaned and organized. The OLAP engine then pre-calculates aggregations and stores them in the cube structure.

This pre-aggregation process makes OLAP queries lightning-fast compared to running the same analysis directly against the data warehouse. When a finance leader wants to see Q1 marketing expenses by department, the cube already has those totals calculated and stored.

OLAP in data warehouse environments enables finance professionals to view data from different perspectives without writing code. Users can start with a high-level view of total expenses, then drill down to specific departments, expense categories, and even individual transactions if needed.

Comparing MOLAP and Other OLAP Approaches

1. MOLAP essentials

MOLAP (Multidimensional OLAP) stores data in specialized array-based structures rather than relational tables. This approach offers the fastest query performance because data is stored in a format optimized specifically for multidimensional analysis. The molap cube structure is particularly effective for complex budget versus actual comparisons and variance analysis.

The pre-computation advantage means finance teams can quickly run complex calculations during critical periods like month-end close or board meeting preparations. However, this speed comes at the cost of storage space and processing time during cube builds.

2. ROLAP and HOLAP differences

ROLAP (Relational OLAP) keeps data in traditional relational databases but creates a semantic layer that presents it as multidimensional. This approach offers greater flexibility with data volumes and more frequent updates than MOLAP. ROLAP is typically used when data freshness is more important than query speed.

HOLAP (Hybrid OLAP) combines both approaches by storing aggregations in multidimensional structures while keeping detailed data in relational tables. This balance works well for finance teams that need both summary-level reporting and the ability to drill down to transaction details.

• MOLAP: Fastest queries, longest processing time

• ROLAP: More flexible, fresher data, slower queries

• HOLAP: Good balance of speed and flexibility

3. The concept of OLAP hypercube

An OLAP hypercube extends beyond three dimensions, incorporating four or more dimensions into a single analytical structure. Finance teams might analyze revenue across time, product, region, customer segment, and sales channel simultaneously—creating a five-dimensional analysis.

This multi-dimensional capability enables sophisticated financial analysis like cohort performance tracking and multi-factor variance analysis. CFOs can use olap hypercube technology to understand how different business drivers interact to affect overall financial performance.

Key Benefits of Traditional OLAP Cubes

1. Speed through pre-aggregation

Pre-aggregation dramatically reduces query time by calculating common summaries in advance. Rather than adding up thousands of individual transactions to show quarterly department expenses, these totals are pre-calculated during cube processing.

This speed advantage was particularly valuable when computing resources were more limited and expensive. Finance teams could complete their month-end reporting packages in hours rather than days, enabling faster business decisions.

2. Simplified business intelligence cube analysis

OLAP democratized data access by allowing non-technical finance users to perform complex analyses without SQL knowledge. Finance professionals could create variance reports, trend analyses, and performance dashboards through intuitive interfaces.

The business intelligence cube approach reduced the reporting backlog for IT departments while empowering finance to be more responsive to business needs. Financial analysts could answer executive questions on the fly during meetings rather than scheduling follow-up reports.

3. Intuitive cube reporting for end users

The dimensional structure of OLAP cubes naturally aligns with how finance professionals think about their business. Users can start with company-wide results, then drill down to specific departments, products, or regions to investigate variances.

This intuitive navigation reduces the learning curve for new finance team members and makes it easier to train business partners on financial analysis. Cube reporting allows the familiar structure of financial statements to map cleanly to OLAP dimensions.

Common Drawbacks and Limitations of OLAP

1. Maintenance overhead and rigid modeling

Traditional OLAP cubes require extensive upfront design work to define dimensions, hierarchies, and measures. Once implemented, adding new dimensions or changing the structure requires IT involvement and often a complete cube rebuild.

This rigidity creates challenges for finance teams in fast-changing businesses. Adding a new product category, reorganizing departments, or incorporating acquisition data often requires waiting for IT resources and disrupts reporting cycles.

The maintenance burden typically falls to specialized database administrators rather than finance teams themselves. This creates a dependency that can slow down financial analysis and reporting processes.

2. Delayed data updates vs. real-time needs

Most OLAP implementations process data on a scheduled basis—typically nightly—rather than in real-time. This means finance teams are often working with yesterday's data when making decisions today.

This delay is problematic for time-sensitive financial activities like cash management, revenue forecasting, or expense monitoring. Modern finance leaders increasingly need real-time or near-real-time data to support agile business operations.

The processing time for large olap cubes can also create conflicts with other system needs. A full cube rebuild might take hours, limiting when updates can occur and potentially disrupting other business processes.

3. Scalability challenges in big data scenarios

Traditional OLAP architectures struggle with very large datasets due to exponential growth in storage and processing requirements. As dimensions and data volumes increase, performance can degrade significantly.

Concurrent user access during peak periods (like month-end close) can overwhelm OLAP servers, causing slow response times or system failures. This creates frustration for finance teams during their most critical work periods.

The in-memory nature of many OLAP solutions also creates practical limits on data granularity. Finance teams often must sacrifice detail or historical depth to maintain acceptable performance.

Emergence of Cloud Analytics and Real-Time Data

Modern columnar storage designs dramatically improve query performance without pre-aggregation. This means finance teams can analyze detailed transaction data directly without the overhead of OLAP cube processing. Snowflake's research reveals that 92% of early AI/cloud adopters achieve ROI through architectural efficiencies, with 98% increasing their investments.

So, what is olap data in the cloud era? It's still multi-dimensional information, but now it can be processed in real-time or near-real-time. This shift supports more agile financial processes and faster decision-making without the traditional constraints.

These cloud platforms maintain the concept of semantic layers—making data understandable to business users—while eliminating rigid physical structures. This provides the business-friendly interface of OLAP without the technical limitations.

Are OLAP Cubes Still Relevant Today?

Traditional OLAP cubes still provide value in specific scenarios where query patterns are highly predictable and data volumes are moderate. Organizations with significant investments in OLAP infrastructure may find the cost of migration outweighs immediate benefits.

The core concepts of dimensional modeling remain highly relevant even as technology evolves. The way finance professionals think about analyzing data—across time periods, organizational units, accounts, and other business dimensions—hasn't changed.

Modern cloud data platforms incorporate these dimensional concepts without the rigid physical structures of traditional OLAP. They provide semantic layers that make what is cube data understandable to business users while leveraging more flexible underlying technologies.

• Data volume needs: Traditional OLAP struggles with massive datasets

• Update frequency: Cloud platforms offer near-real-time vs. nightly batch

• Self-service requirements: Modern platforms provide greater flexibility

• Technical resources: Cloud solutions reduce IT dependency

• Budget constraints: Pay-as-you-go vs. high upfront investment

The ideal solution often combines the conceptual strengths of dimensional modeling with the technical advantages of modern cloud platforms. The dwh olap relationship is evolving from a rigid structure to a more flexible, scalable approach.

New Paths for Finance Teams Seeking Better Planning and Analysis

Finance teams looking to modernize their analytics approach should consider a phased transition rather than a complete overhaul. Start by identifying the most critical reporting and analysis needs that current systems don't adequately address.

Modern FP&A platforms like Abacum combine the dimensional thinking that makes cube analytics intuitive with the flexibility and real-time capabilities of cloud technology. These solutions maintain the finance-friendly interface while eliminating the technical limitations.

The key advantage of modern platforms is their ability to adapt quickly to changing business needs. Finance teams can add new dimensions, metrics, or data sources without lengthy IT projects or disruptions to existing reports.

This flexibility enables finance to become more strategic partners to the business. Rather than spending time maintaining reporting infrastructure, finance professionals can focus on providing insights and supporting decision-making.

• Faster access to current data

• Greater self-sufficiency in analysis

• More flexible modeling capabilities

• Reduced IT dependency

• Better collaboration with business partners

The evolution from traditional olap data warehouse architectures to modern analytics platforms reflects the changing role of finance itself—from backward-looking reporting to forward-looking strategic partnership. What is olap data today? It's still about multi-dimensional analysis, but now with the speed, flexibility, and scale that modern finance teams demand.