Qlik Sense sets the benchmark for a new generation of data analytics. Empower users at any skill level to freely explore data with our one-of-a-kind associative engine combined with powerful AI. Bring actionable insights into every decision with the industry’s most complete platform for modern BI – on cloud or anywhere you choose.
What makes the Qlik Associative Engine different?
- Partial subsets of data
- Restricted linear exploration
- Slow performance
- Tunnel Vision
- All your data
- Explore without boundaries
- Speed to thought
- Unexpected Insights
How does this transform the analytics experience?
When business users look at dashboards or visualisations, they usually end up with more questions than answers. Making informed decisions is a continual process of questioning, evaluating, and questioning again, often in entirely new directions – until the right insight is discovered and fully understood.
Query-based tools can’t provide the flexibility and interactivity needed to support this process. Only Qlik’s Associative engine makes powerful exploration and discovery available to every user – not just data experts and analysts.
Key technology innovations behind the Associative Engine
The Associative Engine employs a number of unique technology innovations, pioneered by Qlik, designed to support non-linear, free form exploration. This all happens under the hood – and just like a high-performance sports car, you can feel the difference when you drive it.
Compressed Binary Indexing
At load time, the Qlik Associative Engine fully combines data sources, achieving a many-to-many full outer join, without data loss or inaccuracy due to executing SQL joins at load time. It creates a compressed, binary data store in-memory, and indexes all relationships between the values in your data. Join keys are built into the multi-table structure, supporting auto-joins at click time. Each unique value is stored once and only once, with binary pointers everywhere else, to minimize data footprint and optimise calculation performance.
After each click, the engine performs a two step process. The first is logical inference – the process by which it determines related and unrelated data values relative to the new selection state. The engine manages global context, and using its associative index, determines the included and excluded data set at the row-level, and applies this context to all analytics in the app.
The second step is dynamic calculation – the engine will instantly recalculate all analytics in context together, at the speed of thought, based on the datasets determined by logical inference. Calculations always use record-level data, for the deepest level of granularity and maximum flexibility. Advanced techniques such as the use of hypercubes and caching, along with optimised in-memory data, allow for instant calculation that accelerates exploration and analysis, scaling to high numbers of users and large, complex data sets.
Any questions? Please feel free to contact us!
The Insight Consulting Team