In the ever-evolving landscape of Business Intelligence (BI), it’s essential to understand how different tools stack up against one another. Qlik, with its innovative data visualization and analysis solutions, has marked a significant departure from traditional BI systems and OLAP (Online Analytical Processing) models. Here, we explore how Qlik stands apart from these conventional systems.

Associative Data Model: A Paradigm Shift

The most significant differentiator that sets Qlik apart from traditional BI systems and OLAP models is its underlying technology – the Associative Data Model. Unlike traditional BI and OLAP, which follow a query-based approach requiring predefined pathways for data exploration, Qlik’s Associative Data Model allows users to explore data freely across multiple dimensions.

This model provides the flexibility to make inquiries on the fly and see the relationships between various data sets, giving users an unrestricted and comprehensive view of their data. This fundamental shift enables an exploratory, user-driven approach to data analysis, rather than a rigid, pre-defined one.

Data Accessibility: Democratization vs. Specialization

Traditional BI systems and OLAP tools typically require a significant level of technical expertise for data modeling and manipulation, often limiting data accessibility to IT specialists or data professionals. Qlik, however, is designed with a self-service model that empowers all users, irrespective of their technical prowess, to interact with and extract insights from data.

This democratization of data not only enhances the speed of decision-making but also fosters a data-driven culture within organizations, where all members can contribute to and benefit from data insights.

Real-Time Data Analysis: From Batch Processing to Instant Insights

Traditional BI systems and OLAP models usually rely on batch processing, where data is analyzed at scheduled intervals. This approach, while useful for regular reporting, often fails to provide real-time insights. Qlik, with its in-memory data processing capabilities, allows for real-time data analysis, ensuring users have the most up-to-date information at their fingertips.

Data Visualization: Static Reports vs. Interactive Dashboards

Where traditional BI systems and OLAP may provide static, tabular reports, Qlik takes data visualization to another level with interactive, personalized dashboards and reports. This approach allows users to manipulate data visually, enabling a deeper understanding of data trends, patterns, and outliers.

Integration and Scalability: From Rigidity to Flexibility

Traditional BI systems and OLAP models often struggle with integrating diverse data sources and can face issues with scalability as the volume of data increases. Qlik’s platform, however, is designed to easily integrate multiple data sources, regardless of their structure or size. Furthermore, Qlik’s robust architecture ensures efficient scalability, capable of supporting businesses as their data needs grow.

The Qlik Difference

Modern business intelligence platforms, also known as Business Discovery platforms, differentiate themselves from OLAP-based BI systems in several key ways:

Transactional Data Storage:

Business Discovery platforms house transactional data, eliminating the need to incessantly modify the data model/architecture. This means business problems are resolved, and new reporting is created at the data visualization level.

Redundant Data:

OLAP cubes tend to contain redundant data (zeros), which are non-existent in Business Discovery platforms.

Data Linking and Dimension Addition:

Linking OLAP cubes can be complicated, and expanding the dimensions of a cube can lead to rapid size growth, which doesn’t occur with platforms like Qlik Sense.

Cubing Issues:

When business users request new information, a new data cube is often required. Moreover, cubes store sums, and as a result, new cubes need to be built for new data cuts.

Fast Data Model Development:

For example, while the creation of an OLAP cube for SAP BW may take approximately five days, resolving the same problem with Qlik Sense might only take five minutes.

Data Field Flexibility:

In Qlik Sense, any field can act as an indicator and a measurement concurrently.

User-Friendly Analysis:

From the business user’s perspective in Qlik Sense, each subsequent analysis is built upon previous ones.

Support Simplicity:

A Qlik Sense solution can be maintained by a single person, encompassing ETL, data model, storage, and visualization. In contrast, larger systems often require at least three people.

Product Responsibility:

In conventional business intelligence systems, several products exist, and an individual person is often responsible for each one.

Despite theoretically providing maximum query processing speed, OLAP-based solutions have significant drawbacks such as massive data “explosion”, slow data loading, rigid cube structure, and limited user queries.Support Simplicity: A Qlik Sense solution can be maintained by a single person, encompassing ETL, data model, storage, and visualization. In contrast, larger systems often require at least three people.

The Advantages of Qlik Sense

Without delving too deeply, there are several areas where Qlik Sense could offer productivity enhancements over traditional OLAP-based analytics tools:


Qlik Sense independently constructs indexes on the tables, freeing architects and administrators from managing data schemas.

High Degree of Data Compression:

Qlik Sense can offer data compression up to 20 times compared to its original state. As a result, 20TB of data could be compressed to 1TB during migration.

New Types of Business Applications:

Utilizing Qlik Sense as an analytical tool provides opportunities to develop entirely new types of applications, such as loan analysis, customer segmentation in commerce, online risk assessment, and more.

Bundle-by-Bundle Data Storage:

There’s no need to construct aggregates beforehand, thus making it easy to analyze temporal sequences. This allows all types of analysis to be performed virtually on the fly.

In-Memory Data Storage:

As the data is immediately available, lightning-fast analysis options are possible.

Consolidation of Transactional and Analytical Data:

Merging transactional and analytical data in a single repository on a single hardware/network infrastructure can significantly reduce the cost of system ownership.

Qlik’s approach to BI presents a stark contrast to traditional BI systems and OLAP models. By prioritizing user-driven data exploration, real-time insights, data democratization, dynamic visualizations, and flexible scalability, Qlik not only simplifies the BI process but also amplifies its potential impact within organizations. This modern approach to BI empowers businesses to leverage their data more effectively, leading to more informed decision-making and enhanced business performance.Self-indexing: Qlik Sense independently constructs indexes on the tables, freeing architects and administrators from managing data schemas.