OLAP: A deep dive into online analytical processing

OLAP (Online Analytical Processing) has become a cornerstone in the evolving business intelligence landscape. As companies seek advanced data-driven decision-making tools, OLAP offers an effective solution for leveraging data. This article provides a detailed exploration of OLAP and its relevance in empowering senior management—including chief people officers, managing directors, and country managers—through actionable insights.

Key characteristics of OLAP

OLAP is an interactive tool for multidimensional analysis widely used in business intelligence. Unlike Online Transactional Processing (OLTP), which focuses on transactions, OLAP handles complex queries and reporting. The data is structured into multidimensional models, enabling dynamic and efficient analysis.

• Multidimensionality: OLAP organizes information into dimensions and hierarchies, creating a multidimensional view suitable for various analyses. This allows users to drill down or slice through data at multiple levels for deeper insights.
• Aggregation: Users can roll up or drill down into details at different levels of granularity. Consequently, executives gain both a comprehensive overview and detailed perspectives.
• Interactivity: OLAP allows business executives to manipulate primary data in real time when making decisions. This capability is especially useful for managers evaluating multiple scenarios before finalizing choices.

OLAP models

OLAP models form the foundation of multidimensional data analysis. Each model provides unique features to address diverse business needs.

MOLAP (multidimensional OLAP)

MOLAP stores data in multidimensional cubes, providing a structured and efficient approach to analysis. Its fast query performance makes it ideal for situations requiring rapid results.

Key features

• Cube structure: Data is stored in cube format, facilitating easy navigation.
• High performance: MOLAP systems optimize query retrieval for speed.
• Examples: Microsoft Analysis Services, IBM Cognos TM1.

ROLAP (relational OLAP)

ROLAP stores data in relational databases, enhancing scalability and flexibility. This model works well with large datasets containing complex relationships.

Key features

• Relational storage: Data resides in relational databases, ensuring adaptability.
• Scalability: ROLAP can efficiently handle large volumes of data.
• Examples: Oracle OLAP, SAP BW.

HOLAP (hybrid OLAP)

HOLAP balances performance and scalability by combining multidimensional storage with relational databases. This hybrid approach allows businesses to optimize both speed and data volume handling.

Key features

• Hybrid approach: HOLAP leverages both cube and relational storage.
• Optimal performance: It balances efficiency and flexibility for various analytical needs.
• Examples: Microsoft SQL Server Analysis Services.

Understanding the nuances of each OLAP model is essential for businesses aiming to align analysis capabilities with objectives. Selecting the appropriate model unlocks the full potential of multidimensional data analysis.

OLAP in data warehouse architecture

In today’s business intelligence environment, a robust data warehouse architecture is crucial for sound decision-making. At its core, OLAP converts raw data into actionable insights.

The data warehouse foundation

As of 2021, the global OLAP market was valued at $3.8 billion with a CAGR of approximately 8%. A data warehouse consolidates organizational information from multiple sources, creating a structured dataset. This centralized structure supports accurate and efficient analysis. Key features include:

• Centralized storage: A data warehouse provides a single location for data, eliminating silos and enabling unified analysis across departments.
• Historical data: Data warehouses store historical information, allowing businesses to identify trends, monitor performance, and make informed decisions over time.

Enhancing analytical capabilities

According to a TDWI survey, over 60% of companies have integrated OLAP into their data warehouse strategy. After establishing a robust foundation, businesses can use OLAP to realize the full potential of their data. OLAP functions as an analytical engine, enabling interactive operations on multidimensional cubes stored in compatible database systems.

• Cube creation: OLAP organizes data into dimensional structures called cubes. These cubes include multiple hierarchies, allowing detailed and subtle analysis.
• Integration with ETL processes: OLAP works closely with Extract, Transform, Load (ETL) workflows to maintain up-to-date warehouse data, ensuring real-time insights.

OLAP models in data warehouse architecture

Forrester Research shows that organizations using OLAP experience, on average, a 15% improvement in decision-making and a 20% reduction in analysis time. OLAP models include:

• MOLAP: Efficient cube-based storage, ideal for rapid queries.
• ROLAP: Relational storage for scalable and flexible data management.
• HOLAP: Combines MOLAP and ROLAP for balanced performance and adaptability.

OLAP analysis techniques

The adoption rates for MOLAP, ROLAP, and HOLAP are roughly 40%, 35%, and 25% respectively. OLAP enables interactive multidimensional analysis. Key techniques include:

• Slice and dice: Allows selection and filtering of data dimensions to examine specific subsets.
• Pivot: Rotates cube axes to provide different data perspectives for strategic decision-making.

OLAP reporting

Approximately 70% of large enterprises integrate OLAP with big data solutions to manage increasing data volumes. OLAP facilitates comprehensive reporting by enabling:

• Customized dashboards: Present KPIs visually to support fast, informed decisions.
• Ad-hoc reporting: Generate immediate reports for quick analysis.

OLAP data modeling

More than 50% of enterprises are moving to cloud-based data warehousing. Effective OLAP requires careful data modeling. Dimensional modeling optimizes analysis through:

• Star schema: Central fact table with dimension tables simplifies queries and improves performance.
• Snowflake schema: Normalized dimension tables maintain data integrity but require more complex queries.

OLAP and big data

IBM reports a 20% ROI improvement in the first year for organizations using OLAP. To handle large datasets, OLAP integrates with advanced analytics, offering:

• Scalability: Supports growing data demands across large enterprises.
• Integration with advanced analytics: Combines OLAP with AI and machine learning for predictive insights.

OLAP data modeling

OLAP data modeling organizes information for intuitive analysis. Unlike transactional databases (OLTP), OLAP provides a multidimensional view of data for comprehensive understanding.

• Centralized metrics: Facts represent critical KPIs such as sales, revenue, or units sold.
• Organized structures: Hierarchies, such as year → quarter → month → day, allow drill-down or roll-up analysis.
• Quantifiable attributes: Measures provide additional numeric details, like unit price or discount.
• Centralized fact table: Star schema simplifies queries by linking fact and dimension tables.
• Snowflake schema: Normalized dimension tables maintain integrity but require more joins.
• Collaborative approach: Work with stakeholders to align metrics and dimensions with organizational goals.
• Focus on relevance: Identify KPIs critical for decision-makers to ensure strategic insights.

How can Brickclay help?

Brickclay provides business intelligence services that help organizations leverage OLAP effectively. Here’s how:

Customized OLAP solutions

Brickclay tailors OLAP systems, including MOLAP, ROLAP, and HOLAP, to align with each organization’s data and analytical requirements.

Data warehousing expertise

They design robust architectures, centralize data, implement ETL processes, and ensure integrity for effective OLAP analysis.

Persona-centric OLAP implementation

By engaging higher management, HR leaders, and directors, Brickclay configures OLAP to deliver actionable insights relevant to each role.

Training and support

Brickclay provides training for all users, helping executives perform scenario analysis and HR teams navigate workforce analytics efficiently.

Dashboard development

They create intuitive dashboards presenting key metrics for informed decision-making by leadership teams.

Scalable OLAP solutions for growth

Brickclay ensures OLAP solutions scale with expanding operations and growing data volumes.

Integration with advanced analytics

The company integrates OLAP with AI and machine learning for predictive insights and strategic foresight.

Cloud-based OLAP solutions

Brickclay implements cloud-based systems, enhancing scalability while optimizing infrastructure costs.

Continuous innovation and future-proofing

They stay ahead of technological trends, such as natural language processing, equipping clients with advanced analytical tools.

Reach out via contact us to explore expert OLAP solutions tailored to your business. Start making informed decisions today with Brickclay’s guidance.