Did that KPI Move Enough for Me to Care?
This post really… is just the setup for an embedded 6-minute video. But, it actually hits on quite a number of topics.
At the core:
- Using a statistical method to objectively determine if movement in a KPI looks “real” or, rather, if it’s likely just due to noise
- Providing a name for said statistical method: Holt-Winters forecasting
- Illustrating time-series decomposition, which I have yet to find an analyst who, when first exposed to it, doesn’t feel like their mind is blown just a bit
- Demonstrating that “moving enough to care” is also another way of saying “anomaly detection”
- Calling out that this is actually what Adobe Analytics uses for anomaly detection and intelligent alerts.
- (Conceptually, this is also a serviceable approach for pre/post analysis…but that’s not called out explicitly in the video.)
On top of the core, there’s a whole other level of somewhat intriguing aspects of the mechanics and tools that went into the making of the video:
- It’s real data that was pulled and processed and visualized using R
- The slides were actually generated with R, too… using RMarkdown
- The video was generated using an R package called ari (Automated R Instructor)
- That package, in turn, relies on Amazon Polly, a text-to-speech service from Amazon Web Services (AWS)
- Thus… rather than my dopey-sounding voice, I used “Brian”… who is British!
Neat, right? Give it a watch!
If you want to see the code behind all of this — and maybe even download it and give it a go with your data — it’s available on Github.