Rocket-Powered Data Science

JULY 6, 2021

What attributes of your organization’s strategies can you attribute to successful outcomes? Seriously now, what do these word games have to do with content strategy? Specifically, in the modern era of massive data collections and exploding content repositories, we can no longer simply rely on keyword searches to be sufficient.

Strategy 266

Strategy Machine Learning Metadata Knowledge Discovery 266

Ontotext

APRIL 6, 2023

Knowledge graphs are changing the game A knowledge graph is a data model that uses semantics to represent real-world entities and the relationships between them. It can apply automated reasoning to extract further knowledge and make new connections between different pieces of data. The possibilities are endless!

Manufacturing 83

Manufacturing Cost-Benefit Knowledge Discovery Optimization 83

Ontotext

MARCH 8, 2023

Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. Several factors are driving the adoption of knowledge graphs. Use Case #1: Customer 360 / Enterprise 360 Customer data is typically spread across multiple applications, departments, and regions.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Ontotext

DECEMBER 1, 2023

It enriched their understanding of the full spectrum of knowledge graph business applications and the technology partner ecosystem needed to turn data into a competitive advantage. Content and data management solutions based on knowledge graphs are becoming increasingly important across enterprises.

Metadata 78

Metadata Sales Consulting Enterprise 78

Ontotext

SEPTEMBER 15, 2020

What Makes a Data Fabric? Data Fabric’ has reached where ‘Cloud Computing’ and ‘Grid Computing’ once trod. Data Fabric hit the Gartner top ten in 2019. This multiplicity of data leads to the growth silos, which in turns increases the cost of integration. It is a buzzword.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

Domino Data Lab

MAY 20, 2021

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large. 1998) and others).

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136