Alerting On Drastic Changes

Rob Collie has a post on using Power BI to spot outliers:

The basic idea here is “alert me if something has changed dramatically.”  If there’s a corner of my business that has spiked or crashed in a big way, I want to know.  If something has dramatically improved in a particular region, I may want to dive into that and see if it’s something we can replicate elsewhere.  And if something has fallen off a cliff, well, I need to know that for obvious reasons too.  And both kinds of dramatic change, positive and negative, can easily be obscured by overall aggregate values (so in some sense this is a similar theme to “Sara Problem?”)

So the first inclination is to evaluate distance from average performance.  And maybe that would be fine with high-volume situations, but when we’re subdividing our business into hundreds or perhaps thousands of micro-segments, we end up looking at smaller and smaller sample sizes, and “normal” variation can be legitimately more random than we expect.

This looks really cool.  If you read the comments, Rob notes that performance does break down at some point.  If you start hitting that point, I’d think about shifting this to R.

Related Posts

The Evolution Of Hadoop

Holden Ackerman has an interesting analysis of Qubole customers’ adoption of Hadoop 2: In Qubole’s 2018 Data Activation Report, we did a deep-dive analysis of how companies are adopting and using different big data engines. As part of this research, we found some fascinating details about Hadoop that we will detail in the rest of this […]

Read More

DISTINCT, GROUP BY, And Transaction Isolation Levels

Rob Farley has an interesting post where two similar-looking queries can provide different outputs given certain transaction isolation levels: Now, it’s been pointed out, including by Adam Machanic (@adammachanic) in a tweet referencing Aaron’s post about GROUP BY v DISTINCT that the two queries are essentially different, that one is actually asking for the set of distinct combinations on the results […]

Read More

Categories

June 2016
MTWTFSS
« May Jul »
 12345
6789101112
13141516171819
20212223242526
27282930