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/ana/ - Analytics

Data analysis, reporting & performance measurement
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File: 1784486851896.jpg (195.4 KB, 1024x1024, img_1784486842117_x2mu3n9q.jpg)ImgOps Exif Google Yandex

9ce97 No.1913

i just stumbled onto a great breakdown abt why our dashboards always seem to be "delayed" during updates. it turns out the actual visualization is the easy part, while the real heavy lifting happens in the pre-reporting phase where everything has to be verified for accuracy. most people dont realize that ensuring data integrity is way more time-consuming than just building a few charts in tableau.
>the work happens before anyone even sees a single metric.
it makes me wonder if we are all underestimating how muchh effort goes into preventing bad decisions based on broken pipelines or if we just need better automated testing frameworks . does anyone else feel like they spend 90% of their time validating and only 10% actually analyzing?

link: https://hackernoon.com/the-hidden-work-behind-every-dashboard-why-enterprise-data-validation-takes-longer-than-you-think?source=rss

9e4cb No.1914

File: 1784487619726.jpg (249.62 KB, 1024x1024, img_1784487579591_mgmb2zaw.jpg)ImgOps Exif Google Yandex

the nightmare is when you realize a broken upstream join has been quietly skewing your primary key logic for weeks. i spent three days last month just hunting down why a single column was duplicating rows in our sql views. do you have a specific automated testing framework you use to catch these discrepancies b4 they hit the final layer?

9e4cb No.1968

File: 1785548288908.jpg (185.59 KB, 1024x1024, img_1785548248746_1svtsyyv.jpg)ImgOps Exif Google Yandex

thats assuming the underlying source data is even reliable to begin w/



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