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#297 - The Draw Of Over-Engineering Analytics Data Collection Solutions
A new implementation can be exciting! It’s a fresh canvas to work with. A new client can present opportunities to try out ideas you’ve only thought of. These are just two of the scenarios that analytics implementation engineers are presented with that create the pull to collect all of the data.
- While this can be fun, exciting, and provide unique sets of data, there are problems that can result from an over-engineered analytics data collection solution. Problems such as:
Many times the solution designer has ideas for novel data that the analytics team doesn’t find useful so there is data going unused.
Development time that could have been spent on other more useful work has been wasted.
The team now has a complex solution that requires more maintenance than it should.
How can engineers and architects resist the urge to “tag everything”? What are steps that analytics teams can put in place to ensure the implementation work is focused on what is truly needed and valuable?
On this week’s episode of the 33 Tangents podcast, Jim and Jenn discuss their history with implementing analytics solutions and how they’ve fallen into this trap before. They talk through their thoughts on a less is more approach and how that applies to the current state of the digital analytics & martech space.
THANK YOU
We know your time is limited, so it means a lot to us that you would spend some of your time with us. If you have found this episode to be valuable, we would appreciate if you would share it.
And if we are getting you hooked, don’t forget to subscribe, like, and recommend on your favorite podcast platform.
WHERE TO LISTEN
The 33 Tangents video simulcast is now available on YouTube
Subscribe on Apple Podcasts
Subscribe on Google Podcasts
Listen on TuneIn
Listen on Amazon Music
WHERE TO FIND US
Website: www.33sticks.com
Email: [email protected]
Twitter: https://twitter.com/33Sticks
Facebook: https://www.facebook.com/33sticks/
YouTube: https://www.youtube.com/channel/UC8KUpp_LygXotCrKgR9ZoBg
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By 33 Sticks
4.9
1717 ratings
A new implementation can be exciting! It’s a fresh canvas to work with. A new client can present opportunities to try out ideas you’ve only thought of. These are just two of the scenarios that analytics implementation engineers are presented with that create the pull to collect all of the data.
- While this can be fun, exciting, and provide unique sets of data, there are problems that can result from an over-engineered analytics data collection solution. Problems such as:
Many times the solution designer has ideas for novel data that the analytics team doesn’t find useful so there is data going unused.
Development time that could have been spent on other more useful work has been wasted.
The team now has a complex solution that requires more maintenance than it should.
How can engineers and architects resist the urge to “tag everything”? What are steps that analytics teams can put in place to ensure the implementation work is focused on what is truly needed and valuable?
On this week’s episode of the 33 Tangents podcast, Jim and Jenn discuss their history with implementing analytics solutions and how they’ve fallen into this trap before. They talk through their thoughts on a less is more approach and how that applies to the current state of the digital analytics & martech space.
THANK YOU
We know your time is limited, so it means a lot to us that you would spend some of your time with us. If you have found this episode to be valuable, we would appreciate if you would share it.
And if we are getting you hooked, don’t forget to subscribe, like, and recommend on your favorite podcast platform.
WHERE TO LISTEN
The 33 Tangents video simulcast is now available on YouTube
Subscribe on Apple Podcasts
Subscribe on Google Podcasts
Listen on TuneIn
Listen on Amazon Music
WHERE TO FIND US
Website: www.33sticks.com
Email: [email protected]
Twitter: https://twitter.com/33Sticks
Facebook: https://www.facebook.com/33sticks/
YouTube: https://www.youtube.com/channel/UC8KUpp_LygXotCrKgR9ZoBg