Sisense

OCTOBER 6, 2020

Let’s consider the differences between the two, and why they’re both important to the success of data-driven organizations. Digging into quantitative data. This is quantitative data. It’s “hard,” structured data that answers questions such as “how many?” The challenge comes when the data becomes huge and fast-changing.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

CIO Business Intelligence

JUNE 28, 2022

For example, they may not be easy to apply or simple to comprehend but thanks to bench scientists and mathematicians alike, companies now have a range of logistical frameworks for analyzing data and coming to conclusions. More importantly, we also have statistical models that draw error bars that delineate the limits of our analysis.

Management 133

Management Internet of Things Statistics Data-driven 133

CIO Business Intelligence

JUNE 28, 2023

These circumstances have induced uncertainty across our entire business value chain,” says Venkat Gopalan, chief digital, data and technology officer, Belcorp. “As The R&D laboratories produced large volumes of unstructured data, which were stored in various formats, making it difficult to access and trace.

Digital Transformation 87

Digital Transformation Cost-Benefit Informatics Data mining 87

O'Reilly on Data

FEBRUARY 11, 2020

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. They’re still struggling with the basics: tagging and labeling data, creating (and managing) metadata, managing unstructured data, etc.

Data Quality 304

Data Quality Metadata Data Governance Publishing 304