Simplify Your Data Modeling with QlikView!

In QlikView, data modelling refers to the process of designing and structuring the data used in a QlikView application. A well-designed data model is essential for creating accurate and meaningful visualizations and analyses.

There are a few key considerations when building a data model in QlikView:

1. Data sources: QlikView can connect to a wide and different range of data sources, including databases, spreadsheets, and cloud-based data stores. It is important to consider the structure and quality of the data sources when building the data model.
2. Data relationships: QlikView allows users to define relationships between different data tables. These relationships help QlikView understand how the data is related and enable it to create accurate and meaningful visualizations.
3. Data granularity: The granularity of the data refers to the level of detail it contains. It is important to consider the granularity of the data when building the data model, as it can affect the accuracy and usefulness of the visualizations and analyses created with the data.
4. Data transformation: QlikView includes an ETL (extract, transform, load) tool for cleansing, transforming, and integrating data from many sources. When developing a data model, it is critical to address the data transformation requirements.

Overall, data modeling in QlikView involves designing and structuring the data used in a QlikView application to ensure that it is accurate, meaningful, and easy to work with.

There are several key benefits of using QlikView’s data modeling capabilities:

1. Data association: The associative data model allows users to easily associate data from different data sources, creating a single, unified view of the data. This makes it easy to drill down into the data and discover new insights, regardless of where the data comes from.
2. Data integrity: QlikView’s data modeling capabilities help to ensure that the data is accurate and consistent. This is accomplished by using data validation and data transformation tools, which can be used to clean and transform the data before it is loaded into QlikView.
3. Data security: QlikView’s data modeling capabilities also help to ensure that the data is secure, by providing data governance features, such as data lineage and data integrity, that can be used to manage, secure and govern the data.
4. Flexibility: QlikView’s data modeling capabilities are flexible, allowing users to easily add new data sources, make changes to the data model and adjust the data as needed.
5. Performance: Data modeling helps to optimize performance by reducing the amount of data that needs to be loaded into QlikView, which can improve the speed and performance of the tool.
6. Scalability: QlikView’s data modeling capabilities also help to ensure that the tool is scalable, allowing it to handle large amounts of data, and the ability to add more users as the organization grows.

Overall, QlikView’s data modeling capabilities are an important aspect of the tool, as they help to structure and organize the data in a way that is easy to understand and analyze. These capabilities make it easy to create a single, unified view of the data, improve data integrity and security, and optimize performance and scalability.