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 363

Data Quality Metadata Data Governance Publishing 363

Rocket-Powered Data Science

FEBRUARY 15, 2023

Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). These changes may include requirements drift, data drift, model drift, or concept drift. Love thy data: data are never perfect, but all the data may produce value, though not immediately.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

O'Reilly on Data

MARCH 31, 2020

Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself. You might have millions of short videos , with user ratings and limited metadata about the creators or content. Models within AI products change the same world they try to predict.

Management 363

Management Machine Learning Experimentation Metrics 363

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 169

Testing Data Quality Predictive Modeling Metrics 169

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

CIO Business Intelligence

MARCH 29, 2022

. • You have data but don’t use it. Why does valuable data so often go unused? Lack of annotation with the right metadata is a contributing factor. An even larger issue is that people may not know how to see value in data. Recognizing what data can tell you is an acquired skill for people beyond just data scientists.

Cost-Benefit 52

Cost-Benefit Machine Learning Metadata Data Quality 52

Domino Data Lab

JULY 22, 2019

One is data quality, cleaning up data, the lack of labelled data. They learned about a lot of process that requires that you get rid of uncertainty. They’re being told they have to embrace uncertainty. They’re years away from being up to that point. You know what? How could that make sense?

Data Science 109

Data Science Machine Learning Data Governance Modeling 109