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 311

Data Quality Metadata Data Governance Publishing 311

CIO Business Intelligence

APRIL 1, 2024

However such fear, uncertainty, and doubt (FUD) can make it harder for IT to secure the necessary budget and resources to build services. Data preparation, including anonymizing, labeling, and normalizing data across sources, is key. Right-size your model(s). Choose a workload location. 2024 Artificial Intelligence

Data Lake 122

Data Lake Consulting Uncertainty Risk 122

DataKitchen

OCTOBER 2, 2023

Whether the Data Ingestion Team struggles with fragmented database ownership and volatile data environments or the End-to-End Data Product Team grapples with real-time data observability issues, the article provides actionable recommendations. ’ What’s a Data Journey?

Data Quality 100

Data Quality Testing Uncertainty Data Enablement 100

erwin

MAY 6, 2020

In these times of great uncertainty and massive disruption, is your enterprise data helping you drive better business outcomes? The COVID-19 pandemic has forced organizations to tactically adjust their business models, work practices and revenue projections for the short term. Ensure company-wide data compliance (reduce risks).

Cost-Benefit 94

Cost-Benefit Data Governance Uncertainty Data-driven 94

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

CIO Business Intelligence

MARCH 29, 2022

Fortunately, learning-based projects typically use data collected for other purposes. . It’s also important to go back to raw data to ask new questions and train new models, particularly as the world is constantly changing. You’ve lost that opportunity if the data has been thrown away. . • You have data but don’t use it.

Cost-Benefit 52

Cost-Benefit Machine Learning Metadata Data Quality 52

Domino Data Lab

JULY 22, 2019

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109