article thumbnail

Are You Content with Your Organization’s Content Strategy?

Rocket-Powered Data Science

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. Labeling, indexing, ease of discovery, and ease of access are essential if end-users are to find and benefit from the collection. Data catalogs are very useful and important.

Strategy 267
article thumbnail

Understanding Social And Collaborative Business Intelligence

datapine

In this day and age, we’re all constantly hearing the terms “big data”, “data scientist”, and “in-memory analytics” being thrown around. Almost all the major software companies are continuously making use of the leading Business Intelligence (BI) and Data discovery tools available in the market to take their brand forward.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

On the Hunt for Patterns: from Hippocrates to Supercomputers

Ontotext

Ever since Hippocrates founded his school of medicine in ancient Greece some 2,500 years ago, writes Hannah Fry in her book Hello World: Being Human in the Age of Algorithms , what has been fundamental to healthcare (as she calls it “the fight to keep us healthy”) was observation, experimentation and the analysis of data.

article thumbnail

Bridging the Gap Between Industries: The Power of Knowledge Graphs – Part I

Ontotext

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. And that’s not all.

article thumbnail

Top Graph Use Cases and Enterprise Applications (with Real World Examples)

Ontotext

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.

article thumbnail

The Semantic Web: 20 Years And a Handful of Enterprise Knowledge Graphs Later

Ontotext

The Semantic Web, both as a research field and a technology stack, is seeing mainstream industry interest, especially with the knowledge graph concept emerging as a pillar for data well and efficiently managed. And what are the commercial implications of semantic technologies for enterprise data? Source: tag.ontotext.com.

article thumbnail

GraphDB and metaphactory Part II: An RDF Database and A Knowledge Graph Platform in Action

Ontotext

It demonstrates how GraphDB and metaphactory work together and how you can employ the platform’s intuitive and out-of-the-box search, visualization and authoring components to empower end users to consume data from your knowledge graph. Data normalization is an essential step in the data preparation process.