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The point of such dashboards is not to simplify the working environment and analysis processes since there are massive volumes of datacollected on a daily level, and companies need solutions that will bring them to the right answer at the right time. You can create your first charts and dashboard in minutes.
In order for you to really truly understand what is happening to your business, your customers and the yummy business outcomes, you need to be able to segment the data. You need to slice! You need to dice! Repeat after me: Slice, dice, drill!! Checking datacollection quality etc. You need to drill!
When deploying analytics for your company or your customers, you can feel stuck between the twin poles of “What data is available?” The key to coming up with the best insights lies in delving deeply into both questions; the answers you discover can help you get even more out of your data. Multiple methods to collect maximum data.
If you are a content site, this means the ability to slice and dice your data by author names, content type, subscribers and free-loaders, commentators and non-commentators, and so much more to bring a new layer of insights. You cannot tie this data to anything else in GA (say, Conversions or Pages per Visit etc.
" In service of analysis the job includes: Pulling data, segmentation, slicing and dicing, drilling-up, drilling-down, drilling-around, modeling, creating unique datasets, answering business questions, writing requirements for data sources and structures for Reporting Squirrels to work with IT teams to create, etc.
I am looking at the internal site searches (on this blog) and the continent from where the search is done and a set of metrics to judge performance. Dimensions allow you to group your data into different buckets and they are most frequently used to slice and dice the web analytics data. Truly heart warming.
Ten years, and the 944,357 words, are proof that I love purposeful data, collecting it, pouring smart strategies into analyzing it, and using the insights identified to transform organizations. Too many bars, inside them too many slices, odd color choices, all end up with this question: what the heck's going on here?
Image from Data Excellence: Better Data for Better AI by Lora Aroyo At the same time, the inherent noisiness of human-labeled data forces us to think about the quality of the measurement instrument, especially when collecting these types of labels at Google-scale. That’s the focus of this blog post.
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