Learn to run – best practices for Qlik application users

From the article you will learn:

1. How to design a data model that enhances the performance and scalability of Qlik applications.
2. Which data loading techniques can speed up processing and improve analysis efficiency.
3. What are the best practices in visualization and security for creating an intuitive and secure analytical environment.

From first steps to full mastery – get to know Qlik

The process of learning and adaptation is much like a child’s development—starting with basic skills like crawling and walking, then gradually gaining independence. Each stage marks a breakthrough to the next level, from early attempts to refining movements until full mastery is achieved. A key part of this journey is learning from experience and applying proven methods and best practices. Just like in technical fields – proper apps and tools make this learning process easier, helping individuals progress and grow effectively in a natural way.

Working with a new tool follows a similar path to learning how to walk—while it may seem challenging at first, each step leads to greater proficiency and efficiency. Implementing best practices from the very beginning ensures that working with Qlik is not only intuitive but also highly optimized. That’s why we’ve compiled a set of proven strategies to help users fully unlock the power of Qlik applications.

Where do these principles come from? Those are the results of many years of experience from both Qlik’s creators and its users. Developed through deep expertise in application design and development, these best practices help optimize system performance, enhance data security, and reduce load times. The result? More efficient applications, intuitive user interfaces, and solutions ready to scale for the future.

Key principles for effective work with Qlik

1. Simplify and optimize data models

Qlik operates on in-memory technology, making it crucial to design simple and efficient data models. This means including only essential fields and tables while eliminating unnecessary data—especially important under Qlik’s new licensing model.

2. Use optimal data loading techniques

Utilize optimized loading scripts, minimize joins, avoid nested loops, and apply appropriate data modeling techniques. These practices help reduce load times and improve performance.

3. Optimize data modeling

Leverage suitable modeling techniques such as the star schema or snowflake schema, depending on analytical needs and data structures. Qlik’s data model optimization tools can help identify potential performance bottlenecks.

4. Implement incremental loading

Incremental loading allows you to import only new or modified data, significantly reducing processing time and system load when working with large datasets.

5. Utilize Qlik’s associative model

Qlik’s associative model enables users to freely explore data without rigidly defining relationships between tables. This flexibility enhances self-service analytics, reducing the need for continuous dashboard modifications. Plan for this from the design phase to maximize usability and empower end users.

6. Optimize user interface

Design intuitive and easy-to-navigate interfaces. Focus on a clear, simple layout that highlights key insights, ensuring users can quickly find the information they need.

7. Create reusable objects

Develop reusable dimensions, measures, and charts that can be applied across multiple sheets and applications. This approach improves consistency, simplifies data management, and reduces application maintenance costs.

8. Follow security best practices

Ensure that only authorized users have access to relevant data. Use access control mechanisms such as section access to protect sensitive information and maintain data security.

9. Optimize data visualizations

Create clear, intuitive visualizations that emphasize key insights. Select appropriate chart types, colors, and labels to enhance data interpretation and improve decision-making.

10. Maintain proper documentation

Regularly document the Qlik application structure to simplify understanding and maintenance. Add comments to loading scripts, describe objects and fields, and store key information about data organization. This makes future modifications more efficient.

11. Precalculate measures in the load script

Perform key calculations, such as sums or averages, during data loading rather than on the dashboard. This reduces the computational load on the front end, improves application responsiveness, and enhances user experience.

12. Use set analysis

Set Analysis allows for advanced data comparisons—such as year-over-year analysis—independent of current filters. This provides users with more flexible and detailed insights without requiring manual filter adjustments.

13. Maintain a consistent layout for filters and buttons

Place filters, buttons, and other interface elements in the same positions across all dashboard pages. This consistency improves navigation and enhances the user experience.

14. Prioritize minimalism and key visualizations

Focus on straightforward visualizations that highlight the most critical data. Use a minimalistic color scheme to draw attention to essential insights and improve readability.

Following these principles ensures that working with Qlik is not only seamless but also highly effective and intuitive. By adopting best practices, Qlik users can transition from basic proficiency—akin to learning how to walk—to running a full-scale analytics marathon.

Implementing and optimizing Qlik can be a complex process, but you don’t have to tackle it alone. For over 10 years, we have been helping companies enhance their analytics, optimize processes, and fully unlock the potential of Business Intelligence tools.

With our extensive expertise and deep knowledge gained from years of working with Qlik, we have compiled invaluable insights to help you maximize the tool’s capabilities. These best practices will prove indispensable for everyday use and for building powerful analytical applications.

Data model optimization in Qlik – key guidelines

To ensure optimal performance and ease of use in Qlik applications, special attention should be paid to properly designing the data model and loading processes. Best practices in this area not only speed up application performance but also enable future scalability and easier maintenance. The following points present proven techniques and principles that will help maximize Qlik’s potential, improving both data loading speed and analysis quality. Applying these methods will simplify users’ daily work and allow for the development of more advanced and efficient analytical solutions.

• Choose the right data model

Match the data model to the specific use case. The star schema is suitable for simpler analyses, while the snowflake schema is better for handling more complex data structures.

• Minimize the number of tables and fields

Avoid unnecessary tables and fields that may slow down data loading and impact performance. Include only those essential for analysis.

• Optimize data loading

Use optimized loading scripts, minimize the number of joins, avoid nested loops, and implement incremental loading whenever possible.

• Use data model optimization tools

Leverage Qlik’s built-in tools to analyze data models and identify potential issues, such as inefficient joins or redundant fields that could affect performance.

• Optimize field types

Select appropriate field types, such as numerical fields for numeric data and date fields for time-related data. This helps optimize queries and improves performance.

• Avoid circular references

Prevent circular references, which can create loops in the data model. Such loops can slow down queries and reduce overall efficiency.

• Optimize the use of synthetic keys

Synthetic keys, resulting from multiple identical fields in different tables, can impact performance. Avoid them by applying proper data modeling techniques.

• Use proper aggregation functions

Apply appropriate aggregation functions like sum() or count() to optimize queries and improve performance.

• Optimize set analysis usage

Set analysis is a powerful tool, but improper use can slow down an application. Use simple syntax and avoid overly complex expressions when possible.

• Monitor and optimize performance

Regularly monitor application performance, analyze bottlenecks, and optimize the data model. Qlik’s performance monitoring tools can help identify and resolve issues.

• Use mapping functions

Mapping functions automatically assign values, preventing inconsistencies and improving data quality.

• Organize tables using crosstable or generic load

The crosstable function facilitates transforming wide tables into a more structured format, while generic load enhances the organization of multi-dimensional data.

• Structure dimensions by hierarchy

Arrange dimensions into logical hierarchies, enabling smooth transitions between general and detailed levels of analysis (e.g., Country > Region > City).

• Load data from QVD using optimized load

Optimized load speeds up data loading from QVD files, reducing processing time and enhancing performance, especially for large datasets.

• Create date field calendars with a consistent format

Establish uniform calendars for time-related data to facilitate time-based analysis and maintain a consistent format in reports.

By following these practices, you can fully leverage Qlik’s capabilities, enhancing both performance and efficiency in working with applications. Keep in mind that implementing and maintaining optimal solutions requires continuous monitoring and adaptation to evolving business needs.

If you need support with implementing or optimizing Qlik applications, our team of experts is ready to help you achieve success and maximize the tool’s potential. Contact us by filling out the form on our website: Qlik, and together we will create a solution perfectly tailored to your needs.