With the Gartner Business Intelligence and Analytics Summit happening this week, there were many discussions of how to best leverage one’s data for maximum value. All the discussions inspired me to write a 3-part series explaining the value of Narratives for Qlik®.
In part 1 of this 3-part series, I’m going to explain the benefits of having narratives alongside your Qlik Sense® visualizations and the power of Advanced NLG to automatically provide actionable insights. In part 2 and part 3, we'll take a deeper dive into the features of Narratives for Qlik and how you can best use them for your own data discovery needs.
Narratives Immediately Enhance the Visualization
As an example, let’s start with this line chart that measures overall rides on Chicago’s ‘L’ Transit system by station, segmenting out ride totals for the top 20 stations. I’ve created this visualization with the goal of trying to pinpoint notable station performance. However, the visualization alone doesn’t reveal any obvious answers without some serious digging and examination.
And that is exactly why Narratives for Qlik is an essential companion for guiding analysis and providing context. Once a narrative is integrated into this worksheet, the desired information is immediately obvious but let’s take a closer look at the narrative.
Breakdown of the Narrative
First, look at the opening paragraph (found below the line graph in the image above). The extension knows that not all the series are complete. It wouldn’t be statistically ‘appropriate’ to compare lines to one another with some containing null values so the narrative automatically filters those out.
Next, the second paragraph’s opener gets right the crux of this chart’s communication goal. “The 11 entities increased from 2001 to 2014 with Lake/State rising the most (60.62%) and Jackson/State rising the least (0.43%) over that span.”
You might be able to see with your own eyes (maybe) that all the stations experienced an overall increase from 2001-2014, but the narrative immediately informs you which station had the biggest increase in traffic and which station had the smallest increase.
Upon further reading, you see some of the analytic power of narratives being brought to bear. In the third sentence of the opening paragraph, the narrative states, “Lake/State, with the highest relative standard deviation, experienced the most volatility, while Midway Airport was the most stable.”
In less than a second, the extension was able to:
- assess the chart
- calculate the volatility of each line
- communicate the most and least volatile series
Finding the Stories in your Data
Lastly, when we dig into some of the individual series on the chart, the stories are communicating things that would be difficult to find in the charts alone.
Specifically, consider the Clark/Lake station explanation:
The largest net growth was from 2004 to 2012, when rides improved 1,584,715 (38.97%). The four periods of consecutive improvement from 2004 to 2008 when rides increased 29.38% significantly impacted this net growth.
Here, we’re able to find a segment of the series which had the largest net improvement of any possible segment in the chart. And even further, we’re able to identify the span within that segment that really drove the movement. In just a few moments, Narratives for Qlik was able to uncover the most notable station performances.
Stay tuned for Part 2 during which I’ll share my favorite features and explain how you can best use them. Also, be sure to check out the extension at the link below: