5 Brutal Truths About Qlik Sense Architecture (Bad vs Good)

You didn’t build a model—you loaded tables and hoped it works.

Read time: 2.5 minutes

Here is the uncomfortable truth: The majority of Qlik Sense applications fail not at the user interface layer, but at the data model layer, long before any user has interacted with them.

A completed application will deliver dashboards and filtering options to users, but selections will not work as expected… therefore, user trust has declined.

The problem lies not with the charts, but with the underlying data model, which is causing all interactions to fail.

How the Qlik Architecture Fails and What You Can Do About It

1. You Loaded Tables, Not a Data Model.

  • No consistency in the grain.

  • Duplicate or messy keys.

  • Broken associations.

Fix:

  • Start with your design.

  • Clean keys and consistent grain.

  • Validate your associations before building visuals.


2. Your Load Script Has Become a Dumping Ground.

  • Too many joins and transforms.

  • Logic is difficult to maintain.

  • Performance is poor.


Fix:

  • Separate out the responsibilities.

  • Pipeline does the cleansing and prep.

  • Script does the modeling.

  • The app does the exploration.


3. You Created Chaos of Exploration.

  • Too many fields and selection paths.

  • No direction for users.

  • Users are confused instead of having insights.


Fix:

  • Intentionally curate fields.

  • Design clear navigation paths.

  • Limit what users have access to in order to drive their decisions.


4. Your Associations are Unreliable.

  • Synthetic keys and circular references.

  • Inconsistent behavior of selection on the same data.

  • Users do not trust your data.


Fix:

  • Have a single version of the truth.

  • Maintain a clean key structure.

  • Remove synthetic keys.


5. Insights Without Actions are Useless.

  • Users explore, but do not act.

  • No apparent outcome based on the analysis.


Fix:

  • Design your dashboards around the actions that will result from your insights.

  • Align insights with actions to deliver an outcome for users.

  • Help guide users to what matters.

💡Key Takeaway: 

When the data model is incorrect, it leads to different interpretations of every selection… eventually, people stop listening.

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