And we shouldn't have calculators because we may forget the relationships between numbers?
I use analytics to do significant A/A testing on every configuration the sites users are actually using to determine what will work for my A/B testing later...
Should I maintain a separate realtime analytics or delay deployments by 24 hours when I would like a little more assurance? This is not a rhetorical question, whether I should keep maintaining separate tracking for the 20% of the time where google analytics is unfit is an open problem for me.
Similarly, I would like to know if there is a sudden plummet in some demographic the second I start a test. It usually isn't significant, but the client panic will be. It is better to cancel the test and do a post-mortem before restarting.. A B test doesn't have to get its day in court.
Giving delayed numbers for routine reports is perfectly valid, dressing up that pig is luddism.
Presumably anything along the lines of the "demographics drop" you mention would be in live operational metrics if it is something that is an effective metric for monitoring the health of the system.
I use analytics to do significant A/A testing on every configuration the sites users are actually using to determine what will work for my A/B testing later... Should I maintain a separate realtime analytics or delay deployments by 24 hours when I would like a little more assurance? This is not a rhetorical question, whether I should keep maintaining separate tracking for the 20% of the time where google analytics is unfit is an open problem for me.
Similarly, I would like to know if there is a sudden plummet in some demographic the second I start a test. It usually isn't significant, but the client panic will be. It is better to cancel the test and do a post-mortem before restarting.. A B test doesn't have to get its day in court.
Giving delayed numbers for routine reports is perfectly valid, dressing up that pig is luddism.