Anmut

JULY 12, 2021

Facts, events, statements, and statistics without proper context have little value and only lead to questions and confusion.?This This is true for life in general, but it’s especially applicable to the data you use to power your business. Data quality vs data condition: basic definitions & differences.

Data Quality 52

Data Quality Measurement Metrics Dashboards 52

Jen Stirrup

NOVEMBER 15, 2022

The foundation should be well structured and have essential data quality measures, monitoring and good data engineering practices. Systems thinking helps the organization frame the problems in a way that provides actionable insights by considering the overall design, not just the data on its own.

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145

O'Reilly on Data

DECEMBER 12, 2019

Residuals are a numeric measurement of model errors, essentially the difference between the model’s prediction and the known true outcome. Residual plots place input data and predictions into a two-dimensional visualization where influential outliers, data-quality problems, and other types of bugs often become plainly visible.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Andrew White

JANUARY 9, 2022

First, how we measure emissions and carbon footprint is about data design and policy. In other words, D&A plays a key role in the foundational measuring angle. This was not statistic and we have not really explored this in any greater detail since. I suspect we should.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Andrew White

JANUARY 11, 2021

See Roadmap for Data Literacy and Data-Driven Business Transformation: A Gartner Trend Insight Report and also The Future of Data and Analytics: Reengineering the Decision, 2025. measuring value, prioritizing (where to start), and data literacy? where performance and data quality is imperative?

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52