2010 Experimentation Reporting 
CIO Business Intelligence
AUGUST 14, 2024
Though eager to get on the Copilot beta, the airline spent 10 weeks analyzing data security using tools like Purview and Sharegate to look at every document and artefact in their Office 365 tenant, documenting what permissions were set on them in a data leakage report before enabling Copilot. Don’t do it straight across the enterprise.
The Unofficial Google Data Science Blog
JULY 22, 2020
Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. When there are changing assignment weights and time-based confounders, this complication must be considered either in the analysis or the experimental design.
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Occam's Razor
SEPTEMBER 15, 2014
In blue is how much time we spent in 2010 and in blue the time spent in 2014. was the dramatic shift between 2010 to 2014 to mobile content consumption. Dive into Mobile Reporting and Analysis. Dive into Mobile Reporting and Analysis. Media-Mix Modeling/Experimentation. Dive into Mobile Reporting and Analysis.
Occam's Razor
JULY 26, 2010
We lovingly craft reports every day. My hope in this post is to share some simple tips with you that might make your reports and analysis speak to you a bit more. Look at your most important work / report / dashboard. We do reports / dashboards like this one all the time: Ok great. And so on and so forth. I need someone.
CIO Business Intelligence
FEBRUARY 14, 2024
The same issues were revealed when Microsoft launched Delve, and before that when the FAST integration brought powerful search to SharePoint in 2010. At the beginning of 2023, Gartner reported only 15% of organizations already have data storage management solutions that classify and optimize data. But it was.
Occam's Razor
MARCH 23, 2010
When it comes to proving which campaigns are better and which numbers to report to the management what will you do? I realize for some HiPPO's old habits die hard, they won't even let you run a report without seeing a case study. You are far too busy actually reporting and analyzing to keep pace with all the wonderful evolution.
The Unofficial Google Data Science Blog
OCTOBER 12, 2017
We data scientists now have access to tools that allow us to run a large numbers of experiments, and then to slice experimental populations by any combination of dimensions collected. Make experimentation cheap and understand the cost of bad decisions. This leads to the proliferation of post hoc hypotheses. Consider your loss function.
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