OLAP: A Deep Dive into Online Analytical Processing

OLAP (Online Analytical Processing), a buzzword in the ever-changing business intelligence landscape, has become a key concept in data analysis and reporting. While companies are looking for advanced data-driven decision-making tools, they also turn to OLAP as an alternative solution for their data usage. Consequently, this paper is going to provide a detailed exploration of OLAP as well as its relevance in empowering businesses’ higher management which includes chief people officers, managing directors and country managers through actionable insights.

Key Characteristics of OLAP

An interactive dimensionality and multidimensional analysis tool called Online Analytical Processing (OLAP) is widely employed today. Unlike Online Transactional Processing (OLTP) which deals with transactions only, OLAP is designed to handle complex queries and reports. In these models, the data is organized into multidimensional structures that facilitate efficient and dynamic modeling.

• Multidimensionality: The approach adopted by OLAP systems involves organizing information in dimensions and hierarchies thereby creating a multi-dimensional view suitable for various analyses. This enables users to drill down or slice through the data on different levels thus getting deeper insights.
• Aggregation: Aggregation functionality allows users to roll up or drill down into details at different levels of granularity. Flexibility is inevitable because executives need both comprehensive overviewing and deep insight perspectives.
• Interactivity:  This feature makes it possible for business executives to do real-time manipulation of the corporation’s primary data applied when making decisions. It is especially useful when managers have to make quick decisions based on multiple scenarios they must go through before making final choices.

OLAP Models

Online Analytical Processing (OLAP) models form the backbone of interactive and multidimensional data analysis. In this section, we delve into the various OLAP models, each offering unique characteristics to cater to the diverse needs of businesses.

MOLAP (Multidimensional OLAP)

The MOLAP model stores data in multidimensional cubes which enable a structured and efficient way of analyzing it. This approach has fast query performance, hence, is ideal for cases where response time is critical.

Key Features

• Cube Structure: Data is stored in a cube format, facilitating easy navigation.
• High Performance: MOLAP systems are optimized for fast query retrieval.
• Examples: Microsoft Analysis Services, IBM Cognos TM1.

ROLAP (Relational OLAP)

Data is saved in relational databases by ROLAP systems which make them more scalable and flexible. In particular, this type of model can be used effectively with large datasets that have complex associations between them.

Key Features

• Relational Storage: Data is stored in relational databases, ensuring flexibility.
• Scalability: ROLAP systems can handle vast amounts of data effectively.
• Examples: Oracle OLAP, SAP BW.

HOLAP (Hybrid OLAP)

HOLAP introduces a balance between the performance and scalability trade-offs found in MOLAP and ROLAP respectively. The best combination includes the use of multidimensional storage aspects and involvement of relational databases combined together into one approach termed as HOLAP modeling strategy.

Key Features

• Hybrid Approach: HOLAP systems leverage both cube and relational storage methods.
• Optimal Performance: Balances performance considerations for diverse analytical needs.
• Examples: Microsoft SQL Server Analysis Services.

Understanding the nuances of each OLAP model is crucial for businesses seeking to align their data analysis capabilities with specific requirements and objectives. Whether prioritizing speed, scalability, or a hybrid approach, selecting the right OLAP model is integral to unlocking the full potential of multidimensional data analysis.

OLAP in Data Warehouse Architecture

In a living BI landscape, a strong olap data warehouse architecture is crucial to sound decision making. The center of this architecture is OLAP (Online Analytical Processing), a powerful tool that converts raw data into actional insights.

The Data Warehouse Foundation

As of 2021, the global online analytical processing market was valued at approximately $3.8 billion, with a compound annual growth rate (CAGR) of around 8%. Before getting into OLAP, it’s important to understand what makes up data warehousing. A data warehouse is where all types of organizational information are pooled from different sources together. This brings out a complete structured dataset and constitutes an essential platform for better analysis. In most cases, significant features that define the notion of a data warehouse encompass:

• Centralized Storage: Data warehouses provide a single, centralized location for storing data. This eliminates data silos, ensuring that all relevant information is accessible from a unified source. This centralized storage is crucial for streamlined analysis for businesses with diverse datasets.
• Historical Data: Unlike traditional databases focusing on current data, data warehouses store historical data over time. This historical perspective allows businesses to analyze trends, track performance, and make informed decisions based on a comprehensive understanding of their data.

Enhancing Analytical Capabilities

A TDWI survey indicated that over 60% of surveyed companies have implemented OLAP in their data warehousing strategy. After the establishment of the basis for the development of your future dwh you should think about olap technologies usage as they make it possible to realize its potential entirely. Online Analytical Processing serves as an analytic engine enabling interactive dynamic analysis operations on multi-dimensional arrays or cubes stored in compatible database management systems.

• Cube Creation: OLAP organizes information according to dimensionality structures referred to as cubes. It is a full representation of data involving multiple dimensions and hierarchies. Cube building takes into account the identification of the relevant dimensions to the data which helps in making subtle analysis possible.
• Integration with ETL Processes: To populate a data warehouse with information and ensure its update, organizations have to use the Extract Transform Load (ETL) process. OLAP is closely tied to these ETL operations so that ever changing warehouse data is always ready for analysis by it. This integration establishes a dynamic relationship between OLAP and the data warehouse, allowing real-time insights.

OLAP Models in Data Warehouse Architecture

Studies by Forrester Research highlight that organizations leveraging OLAP in their data warehousing architecture experience, on average, a 15% improvement in decision-making processes and a 20% reduction in time spent on data analysis. OLAP comes in various models, each with its strengths and use cases. Understanding these models is crucial for optimizing analytical processes within the data warehouse.

• MOLAP (Multidimensional OLAP): MOLAP systems store data in a multidimensional cube format. This storage structure is highly efficient for quick query performance, making it ideal for scenarios where rapid analysis is paramount.
• ROLAP (Relational OLAP): ROLAP systems store data in relational databases. This model offers greater scalability and flexibility, making it suitable for large-scale data warehousing scenarios.
• HOLAP (Hybrid OLAP): HOLAP combines elements of both MOLAP and ROLAP, offering a balanced approach that prioritizes performance and scalability. This model benefits organizations seeking a middle ground between speed and adaptability.

OLAP Analysis Techniques

Among businesses implementing OLAP, the distribution between MOLAP, ROLAP, and HOLAP models is approximately 40%, 35%, and 25% respectively. OLAP’s true strength lies in enabling users to analyze multidimensional data interactively. Several analysis techniques empower users to derive meaningful insights:

• Slice and Dice: OLAP allows users to “slice” the data by selecting a specific dimension and “dice” it by choosing subsets. This technique provides a granular data view, allowing detailed analysis based on specific parameters.
• Pivot: The pivot function enables users to rotate the axes of the cube, providing different perspectives of the data. This dynamic feature is valuable for decision-makers who need diverse angles for strategic decision-making.

OLAP Reporting

About 70% of large enterprises were integrating OLAP capabilities with big data analytics solutions to handle the increasing volumes of data. OLAP goes beyond analysis by facilitating the creation of comprehensive OLAP reports. Reporting features are crucial for delivering actionable insights to stakeholders across the organization.

• Customized Dashboards: OLAP tools enable the creation of customized dashboards that present key performance indicators (KPIs) in a visually appealing and easily understandable format. These dashboards provide a consolidated view of critical metrics, supporting faster decision-making.
• Ad-hoc Reporting: The flexibility of online analytical processing allows users to generate ad-hoc reports on the fly. This capability is invaluable for scenarios where immediate insights are required, empowering decision-makers with the information they need without delay.

OLAP Data Modeling

The migration to cloud-based data warehousing solutions is on the rise, with over 50% of enterprises planning or already moving their data warehousing infrastructure to the cloud. Data modeling is crucial to leveraging OLAP effectively within the data warehouse architecture. Dimensional modeling, in particular, is pivotal in optimizing OLAP analysis.

• Star Schema: One prevalent dimensional modeling technique is the star schema. In this model, a central fact table is surrounded by dimension tables. This schema simplifies querying and enhances performance by creating a structure that facilitates efficient data retrieval.
• Snowflake Schema: A snowflake schema, where dimension tables are normalized, is sometimes employed. While this approach ensures data integrity, it may require more complex queries than the star schema.

OLAP and Big Data

A case study conducted by IBM reported that organizations implementing OLAP solutions experienced, on average, a 20% improvement in return on investment (ROI) within the first year. As organizations grapple with the influx of big data, OLAP systems must adapt to handle vast datasets. The intersection of OLAP and big data opens new possibilities for scalable and high-performance analytics.

• Scalability: Scalable OLAP solutions accommodate the growing data demands faced by enterprises dealing with massive datasets. This scalability ensures that OLAP remains a reliable tool for businesses seeking to harness the potential of big data.
• Integration with Advanced Analytics: The integration of OLAP with advanced analytics tools enhances its capability to derive actionable insights from vast datasets. This synergy positions OLAP as a strategic asset for businesses looking to stay ahead in a competitive landscape.

OLAP Data Modeling

At its core, OLAP data modeling is about organizing data to facilitate intuitive and efficient analysis. Unlike traditional relational databases optimized for transactional processing (OLTP), OLAP data modeling focuses on providing a multidimensional view of the data, allowing users to navigate through various dimensions for a comprehensive understanding.

• Centralized Metrics: Facts in OLAP data models represent the numerical data or metrics that businesses want to analyze. These could include sales figures, revenue, quantities sold, or any other measurable KPIs central to the organization.
• Organized Structures: Hierarchies define the relationships within dimensions. For example, a time dimension hierarchy could include levels like year, quarter, month, and day. Hierarchies enhance the ability to drill down or roll up through different levels of granularity.
• Quantifiable Attributes: Measures are additional quantitative attributes associated with dimensions. They provide further granularity to the analysis. For instance, within the “product” dimension, measures could include a unit price or discount percentage.
• Centralized Fact Table: The star schema is one of the most widely used OLAP data modeling techniques. In this model, a central fact table is surrounded by dimension tables, forming a star-like structure. This simplifies queries and enhances performance by reducing the number of joins needed.
• Snowflake Schema: Alternatively, the snowflake schema extends the star schema by normalizing dimension tables. While this maintains data integrity, it can result in more complex queries due to the need for additional joins.
• Collaborative Approach: Work closely with stakeholders, including higher management, to understand the metrics and dimensions critical for decision-making. This collaboration ensures the OLAP data model is tailored to meet organizational needs.
• Focus on Relevance: Identify the KPIs that align with organizational goals. This step is crucial for managing directors and executives who require high-level strategic insights.

How can Brickclay Help?

Brickclay, as a leading provider of business intelligence services, is well-positioned to assist organizations in leveraging the power of OLAP for their data analysis and decision-making needs. Here’s how Brickclay can specifically help businesses, addressing the requirements of higher management, chief people officers, managing directors, and country managers:

• Customized OLAP Solutions: Brickclay specializes in tailoring OLAP solutions to meet the unique needs of businesses. Whether implementing MOLAP, ROLAP, or HOLAP models, the company ensures that the chosen OLAP system aligns seamlessly with the organization’s data structure and analytical requirements.
• Data Warehousing Expertise: With a deep understanding of data warehousing, Brickclay can assist in designing and implementing robust data warehouse architectures. This includes centralizing data from various sources, establishing efficient ETL processes, and ensuring data integrity – all essential components for effective OLAP analysis.
• Persona-Centric OLAP Implementation: Brickclay adopts a persona-centric approach to OLAP implementation. The company works closely with higher management, chief people officers, managing directors, and country managers to understand their specific analytical needs. This ensures that the OLAP system is configured to deliver actionable insights tailored to each persona’s requirements.
• Training and Support: Understanding the importance of user adoption, Brickclay provides comprehensive training sessions for employees at all levels. Whether it’s teaching executives how to perform scenario analyses or guiding HR teams through workforce analytics, the aim is to empower users to make the most of OLAP tools.
• Dashboard Development: Brickclay excels in creating intuitive and visually appealing dashboards that cater to the diverse needs of higher management, chief people officers, managing directors, and country managers. These dashboards offer quick access to key metrics, supporting informed decision-making.
• Scalable OLAP Solutions for Growth: Recognizing that businesses evolve, Brickclay ensures that the implemented OLAP solutions are scalable. This scalability is crucial for managing directors overseeing expanding operations and dealing with increasing data volumes.
• Integration with Advanced Analytics: As the landscape of business intelligence evolves, Brickclay is at the forefront of integrating OLAP with advanced analytics, including AI and machine learning. This ensures businesses can benefit from predictive analytics, gaining insights into future trends.
• Cloud-Based OLAP Solutions: Embracing the cloud computing trend, Brickclay offers expertise in implementing cloud-based OLAP solutions. This provides increased scalability and cost-efficient alternatives for businesses looking to optimize their infrastructure investments.
• Continuous Innovation and Future-Proofing: Brickclay is committed to staying ahead of the curve, keeping clients at the forefront of technological advancements in OLAP. The company actively explores emerging trends, such as enhanced natural language processing, to ensure that clients are equipped with the latest tools for data analysis.

Feel free to contact us at Brickclay for unparalleled expertise in business intelligence and personalized solutions tailored to your data needs. Contact our dedicated team today to embark on a journey of insightful decision-making. Your success starts with a conversation – connect with us now.