What's New in Narratives for Qlik?

March 1, 2016 Mary Grace Glascott

BI Narratives

It has been an exciting first month for Narratives for Qlik!  In just a few weeks, thousands of people have interacted with the extension on our website and downloaded the product into Qlik Sense. Forbes even proclaimed that with Narratives for Qlik, "Management dashboards and analytics will never be the same again."

The new release includes increased functionality regarding how the narratives tell stories (more chart types), ways the users can interact with the product (more ways to customize), and opportunities to identify insights not obvious in the visualization alone (more advanced analytics).

With the new release, you can now:

Generate narratives for additional charts

We now support combo charts, as well as the ability to generate narratives for linked charts, even if the visualization resides on a different sheet.

What's a Combo Chart? The combo chart is suitable for comparing two sets of measure values that are usually hard to compare because of the differences in scale. A typical example is when you have a bar chart with sales figures and want to combine these figures with the margin values (in percent). With Narratives for Qlik, the insights gleaned from a combo chart can now be transformed into insightful explanations. 

Customize your narratives in more ways

Add context to your narratives by specifying if larger measures are "good" or "bad," adjust values and formatting for percentages, and characterize additional inputs for more intuitive readability.

Why does customization matter? Let's say you'd like to chart your golf scores. It'd be important to instruct the extension that high scores are actually "bad" and low scores "good," so that the narrative is relevant to your particular use case.

Gain deeper discovery with enhanced analytics

For continuous series analysis, your narratives now identify spans of interesting trends not always obvious when looking at the visualization; for entity distribution analysis, your narratives now identify interesting clusters of entities with similar values and can classify distribution of entities as normal.

Can you give me an example of normal distribution? In a chart that’s measuring revenue, your narrative may read something like this: “The revenue follows a normal distribution with an average revenue of $100 and a standard deviation of $20. Therefore, you can expect revenue to be between $60 and $140 about 95% of the time.”


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