Qlik Sense
Key Info:
Qlik Sense is a business intelligence and analytics product for combining data, exploring associations and building interactive visualisations and dashboards. Qlik presents it within its cloud analytics offering, with self-service analysis, augmented analytics, reporting, alerts, mobile use and embedded experiences. Its associative engine is intended to keep related and excluded values visible while a user explores data.
Details
Associative exploration
Qlik Sense uses an associative engine that keeps relationships between data values available during analysis. Selections update the visible context while also showing values that are related or excluded. This can help users investigate a question from several directions instead of following only predefined drill paths. The benefit depends on a well-designed data model and clear field naming.
Dashboards and augmented analysis
Authors can build sheets with charts, tables, maps, filters and custom calculations. Search and Insight Advisor features help generate or suggest analyses from available data. Dashboards are interactive and can be distributed for browser and mobile use. Reporting, alerting and notes extend the product beyond a static dashboard, while embedded analytics can place Qlik content in another application.
Data preparation and automation
The platform documents connectors, data preparation, cataloguing and application automation. These capabilities can reduce manual hand-offs, but they also create dependencies that must be monitored. Administrators should understand reload schedules, lineage, space permissions, app ownership and what happens when a source schema or credential changes.
Who should evaluate it
Qlik Sense can suit organisations that need governed dashboards and broad self-service exploration across complex data. It may be useful where analysts frequently need to follow unexpected relationships rather than consume a fixed report. Smaller teams should compare the administration and modelling effort with the number of genuine analytical users.
Evaluation notes
Use a realistic dataset with several related tables and known data-quality problems. Ask users to answer an unplanned question through selections, then verify the result against the source. Test reload failure, row-level access, an alert and a mobile view. Confirm deployment responsibilities, capacity limits, extension governance, API access and the features included in the intended edition before selecting an architecture. Record the time required to trace a dashboard value back through the app model and reload process. Compare two alternative model designs and note whether excluded values genuinely help users discover a relationship that a conventional filter path would hide.