DataKitchen

SEPTEMBER 21, 2023

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream. What is Data in Use?

Testing 176

Testing Data Quality Predictive Modeling Metrics 176

O'Reilly on Data

FEBRUARY 11, 2020

We suspected that data quality was a topic brimming with interest. The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. Key survey results: The C-suite is engaged with data quality. Adopting AI can help data quality.

Data Quality 304

Data Quality Metadata Data Governance Publishing 304

Octopai

APRIL 16, 2020

Some data teams working remotely are making the most of the situation with advanced metadata management tools that help them deliver faster and more accurately, ensuring business as usual, even during coronavirus. Smarter Business Intelligence is an Asset During Uncertainty.

Metadata 59

Metadata Business Intelligence Uncertainty Data Quality 59

Anmut

OCTOBER 29, 2020

On the 29th of October 2020, the WHO reported 44,002,003 confirmed cases. Worldometer which John Hopkins base their figures on, reported 44,860,215 cases. The role of data governance. This large gap between reported figures raises tough questions on the reliability of COVID-19 tracking data.

Data Governance 52

Data Governance Data Collection Data-driven Statistics 52

CIO Business Intelligence

MARCH 29, 2022

A common data habit that results in missed opportunity is assuming data has no further value once it’s been used for the particular purpose. Data is ingested, processed, transformed (perhaps for a specific report or to be stored in a traditional database), and then the raw or partially processed data is discarded.

Cost-Benefit 52

Cost-Benefit Machine Learning Metadata Data Quality 52

Domino Data Lab

JULY 22, 2019

If you look into the middle bucket, they have three things that they report in common. One is data quality, cleaning up data, the lack of labelled data. Now, working down to the mature part of this, they report two things in common. What do they report in common? You know what?

Data Science 109

Data Science Machine Learning Data Governance Modeling 109