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Uncertainties: Statistical, Representational, Interventional

The Unofficial Google Data Science Blog

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

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Measuring Validity and Reliability of Human Ratings

The Unofficial Google Data Science Blog

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. Researchers and practitioners have been using human-labeled data for many years, trying to understand all sorts of abstract concepts that we could not measure otherwise. That’s the focus of this blog post.

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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., Crucially, it takes into account the uncertainty inherent in our experiments. the fraction of video recommendations resulted in positive user experiences).

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Why Construction Businesses Should Track KPIs to Conquer Economic Uncertainty

Jet Global

As a result, measuring success by financials alone isn’t enough for construction and engineering professionals. Due to the Infrastructure Investment and Jobs Act of 2022 in the United States, nonresidential construction is expected to continue expanding despite expected uncertainty in 2023. trillion worldwide by 2030.

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20 Best Logistics KPIs and Metric Examples for 2022 Reporting

Jet Global

A logistics key performance indicator (KPI) is a quantitative tool used by businesses to measure performance within their logistics department. Logistics KPIs can measure a variety of metrics, most of which pertain to purchasing, warehousing, transportation, delivery of goods, and financials. Choose SMART KPIs.

Metrics 84
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You Can’t Regulate What You Don’t Understand

O'Reilly on Data

If we want prosocial outcomes, we need to design and report on the metrics that explicitly aim for those outcomes and measure the extent to which they have been achieved. But alignment will be impossible without robust institutions for disclosure and auditing. That is a crucial first step, and we should take it immediately.

Metrics 283
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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

The uncertainty of not knowing where data issues will crop up next and the tiresome game of ‘who’s to blame’ when pinpointing the failure. Moreover, advanced metrics like Percentage Regional Sales Growth can provide nuanced insights into business performance. One of the primary sources of tension?

Testing 176