1. Technology

 

The common BI solutions in the Danish market consist of multiple technologies connected in one solution, such as databases, integration services, and presentation technologies to achieve the core of BI: ETL - Extract - Transform - Load.

 

In a Qlik-based solution, all these functions are performed within one comprehensive technology that covers data storage, transformation, and presentation. This provides a simpler - and therefore faster and cheaper - installation and maintenance, as well as a much more flexible solution where it is easy and quick to add new functionalities and queries.

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2. Data architecture

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Most common BI solutions are based on a data architecture where data is collected in multidimensional cubes (for example, dimensions of sales - product - city), and each query in the system is linked to a corresponding cube. Each time a query is made, the formula is calculated across the dimensions in the cube, and if new queries need to be added or a dimension changed, the data cubes must be redefined and regenerated before the answer - and therefore the important knowledge - is available.

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In a Qlik-based solution, each query is based on relationships between data - the formulas that should provide answers pull data directly from the source systems without the need for lengthy and heavy construction of cubes. Additions or changes to queries are made with just a few clicks, and the business-critical knowledge and real-time insights into the company are available immediately after the user has made the query in their dashboard.

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3. Data structure

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Cube-based systems work with dimensions and expressions. In Qlik, everything is essentially fields, which can all be used as filters - this makes a Qlik solution much more flexible.

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4. Performance and scaling

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A cube-based BI solution becomes heavy and slow to work with over time.

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In a Qlik-based BI solution, relational data models are built, which can always make queries directly into the company's data. Such a solution is therefore immensely scalable and can quickly be prepared to handle even enormous amounts of data.

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5. Analyzing/controlling

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Analysis and filtering across cubes is difficult because filters by default only work on one cube. This means that time-consuming rebuilding and regeneration of cubes are required if queries need to be changed, or if cross-cutting analyses are desired to find causal relationships in the data behind the presented data.

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In a Qlik-based BI solution, filters work across the entire data model because it is relational instead of defined in fixed cubes. This provides great flexibility and the ability to perform analyses and queries across data - or to change the dimensions of the query with just a single click, for example, from monthly to weekly basis.

 

6. Build time and implementation

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Developing and implementing a standard BI solution is often a time and resource-consuming process. The complexity of the tool and the cube-based technology means that each cube must be defined, built, and maintained before the solution can be put into use.

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In a Qlik-based BI solution, the solution is laid as a layer on top of the company's existing business systems; queries are made directly in the data, and implementation time can be as little as three months; but the first department in the company can be using the solution after just a few weeks, as implementation and rollout in the company are ongoing.