Anmut

OCTOBER 29, 2020

As a result, concerns of data governance and data quality were ignored. The direct consequence of bad quality data is misinformed decision making based on inaccurate information; the quality of the solutions is driven by the quality of the data. COVID-19 exposes shortcomings in data management.

Data Governance 52

Data Governance Data Collection Data-driven Statistics 52

Depict Data Studio

MAY 12, 2020

Amanda said, “There are different points in which we make decisions about how and what we visualize, and then how we publish and share. Amanda went through some of the top considerations, from data quality, to data collection, to remembering the people behind the data, to color choices.

Visualization 52

Visualization Uncertainty Data Collection Data Quality 52

O'Reilly on Data

MARCH 31, 2020

Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself. You might establish a baseline by replicating collaborative filtering models published by teams that built recommenders for MovieLens, Netflix, and Amazon. If you can’t walk, you’re unlikely to run.

Management 332

Management Machine Learning Experimentation Metrics 332

CIO Business Intelligence

MARCH 29, 2022

Lowering the entry cost by re-using data and infrastructure already in place for other projects makes trying many different approaches feasible. Fortunately, learning-based projects typically use data collected for other purposes. . And the problem is not just a matter of too many copies of data.

Cost-Benefit 52

Cost-Benefit Machine Learning Metadata Data Quality 52

The Unofficial Google Data Science Blog

JULY 18, 2023

Editor's note : The relationship between reliability and validity are somewhat analogous to that between the notions of statistical uncertainty and representational uncertainty introduced in an earlier post. Measurement challenges Assessing reliability is essentially a process of data collection and analysis.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Domino Data Lab

JULY 22, 2019

One is data quality, cleaning up data, the lack of labelled data. Frankly, leading data science teams early on, you almost always had to struggle against the BI teams. It was also the year, 2001, when “ Agile Manifesto ” was published. They’re being told they have to embrace uncertainty.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109