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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. That metric is tied to a KPI.

Metrics 156
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Teaching AI to Smell by Using DataRobot

DataRobot

It was introduced in 1980 but open-sourced in 2007, which created its widespread use. DataRobot also provides per-label metrics so that metrics per class can be compared. Below are the per-label metrics provided by DataRobot for model evaluation purposes. Each molecule has a combination of multiple smells.

Metrics 52
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Make Every Sprint Count with DevOps Analytics

Sisense

DevOps first came about in 2007-2008 to fix problems in the software industry and bring with it continuous improvement and greater efficiencies. You should have at least one KPI for every part of your product cycle; planning, development, testing, deployment, release, and monitoring. But is that really true? Getting Started.

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Knowledge

Occam's Razor

" ~ Web Metrics: "What is a KPI? " + Standard Metrics Revisited Series. "Engagement" Is Not A Metric, It's An Excuse. Defining a "Master Metric", + a Framework to Gain a Competitive Advantage in Web Analytics. The Awesome Power of Visualization 2 -> Death and Taxes 2007.

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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

A/B testing is used widely in information technology companies to guide product development and improvements. For questions as disparate as website design and UI, prediction algorithms, or user flows within apps, live traffic tests help developers understand what works well for users and the business, and what doesn’t.

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To Balance or Not to Balance?

The Unofficial Google Data Science Blog

A naïve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means. 2007): Propose a finite collection $mathcal L={hat e_k:k=1,ldots,K}$ of estimation algorithms. This fact is well documented by Kang & Schafer (2007).

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.