CIO Business Intelligence

FEBRUARY 14, 2024

Introduce gen AI capabilities without thinking about data hygiene, he warns, and people will be disillusioned when they haven’t done the pre work to get it to perform optimally. The same issues were revealed when Microsoft launched Delve, and before that when the FAST integration brought powerful search to SharePoint in 2010.

IT 140

IT Metadata Data-driven Management 140

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.

Experimentation 95

Experimentation Statistics Testing Strategy 95

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.

Metrics 141

Metrics Reporting Analytics Marketing 141

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.

Metrics 96

Metrics Measurement Dashboards Reporting 96

Occam's Razor

MARCH 23, 2010

My problem with these mistruths and FUD is that they result in a ton of practitioners and companies making profoundly sub optimal choices, which in turn results in not just much longer slogs but also spectacular career implosions and the entire web analytics industry suffering. This is sad. Even a little frustrating. Likely not.

Analytics 118

Analytics Consulting Metrics Experimentation 118

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.

Experimentation 61

Experimentation Testing Statistics Metrics 61