The best practice is to maintain a normalized, star-schema-like structure where dimension tables are linked to facts via keys. Keeping Product and Product_Category as separate, linked tables preserves data integrity, reduces redundancy, and leverages Qlik’s associative engine efficiently. Flattening (Option B) is often an anti-pattern, synthetic keys (Option C) should be avoided, and a link table (Option D) is unnecessary for a simple, clean hierarchy.

When a chart is empty despite data being present, the fastest diagnostic step is to create a simple table object using the fields in question. This bypasses master items, chart settings, and associations to confirm the raw values are present and accessible in the app’s final data model. It’s a direct, practical troubleshooting technique. The other options are valid checks but are not typically the most immediate first step for this specific symptom.

Using a wildcard pattern (e.g., Q*.csv) is the most efficient and maintainable approach. It automatically includes all matching files in the specified location, making the script robust for future additions without modification. Separate LOADs (A) are not maintainable. A FOR EACH loop (C) is over-engineered for this simple pattern match. CONCATENATE (D) works but requires explicitly naming each file, which is less maintainable than a wildcard.

Master items are the core Qlik Sense feature for promoting reusability and consistency. Defining measures as master items allows them to be used in any sheet, ensures a single point of maintenance, and can be organized with tags for discoverability. Script variables (A) are for backend logic, not front-end measures. Bookmarks (C) save a chart’s state, not reusable definitions. Documentation (D) does not enforce consistency within the app itself.

The correct technique for comparing two metrics with the same granularity (like actuals vs. budget) is to concatenate the fact tables. This adds a field (e.g., ‘Type’ with values ‘Sales’ and ‘Budget’) to distinguish the source, allowing you to create measures like Sum(Sales) and Sum(Budget) that are correctly associated with the common dimensions (Month, Product). Linking them (A or C) would create a synthetic key or complex model. ApplyMap (D) is for enriching a table, not for creating a comparable fact structure.

For a mobile, glanceable experience, large KPI objects are ideal as they present a single, clear number. Adding trend sparklines within the KPI provides immediate historical context without taking extra space. This combination maximizes information density and readability on a small screen. Complex charts (A, B) or tables (D) require more space and interaction to interpret, which is less optimal for a quick mobile check.

The core principle of Qlik’s associative engine is that selections only affect data connected through linked key fields. If the Region table is an ‘island’ with no key field (like a common RegionID) linking it to the Sales/Product data model, no association exists, and selections will not flow. Set Analysis (A) can override but not cause a complete lack of association. A field as a dimension (C) would show it in the chart. Different sheets (D) do not break associations.

The ‘Rename Field’ feature in the Data model viewer allows you to change the display name of a field for the entire front-end (sheets, master items, filters) without altering the underlying script or data structure. This is the most efficient and maintainable approach for a terminology change. Renaming in the script (A) is also valid but is a backend change. Creating a new field (C) is redundant. A text box (D) is a poor practice that leads to confusion.

To lock the denominator to the grand total irrespective of selections, you must use Set Analysis. The expression `Sum({1} Sales)` calculates the sum of Sales over the full set of data (set identifier 1), ignoring all selections. Using `TOTAL` (C) would still respect the dimensional context of the chart. `Count(DISTINCT …)` (B) is nonsensical for a sum. `AGGR()` (D) is for nested aggregation, not for ignoring selections.

The best practice is to maintain a normalized, star-schema-like structure where dimension tables are linked to facts via keys. Keeping Product and Product_Category as separate, linked tables preserves data integrity, reduces redundancy, and leverages Qlik’s associative engine efficiently. Flattening (Option B) is often an anti-pattern, synthetic keys (Option C) should be avoided, and a link table (Option D) is unnecessary for a simple, clean hierarchy.0