Ontotext

JULY 6, 2023

This shift of both a technical and an outcome mindset allows them to establish a centralized metadata hub for their data assets and effortlessly access information from diverse systems that previously had limited interaction. There are four groups of data that are naturally siloed: Structured data (e.g., Stay ahead!

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

Corinium

APRIL 25, 2019

Ahead of the Chief Data Analytics Officers & Influencers, Insurance event we caught up with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity to discuss how the industry is evolving. I am head of Products here, which comprises of R&D, Product Management and Global Customer support.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

Ontotext

MAY 5, 2023

Here, the ability of knowledge graphs to integrate diverse data from multiple sources is of high relevance. As you can see from the slide below, knowledge graphs can provide a single access point for various types of data such as structured data, knowledge organization systems, transactional data and signals from unstructured content.

Data Collection 64

Data Collection Risk Data-driven Interactive 64

erwin

OCTOBER 24, 2019

Metadata is generated every time data is captured at a source, accessed by users, moves through an organization, and then is profiled, cleansed, aggregated, augmented and used for analytics to guide operational or strategic decision-making. Standardize data management processes through a metadata-driven approach.

Metadata 104

Metadata Management Data-driven Data Architecture 104

Jet Global

MAY 9, 2024

A data pipeline is a series of processes that move raw data from one or more sources to one or more destinations, often transforming and processing the data along the way. Data pipelines support data science and business intelligence projects by providing data engineers with high-quality, consistent, and easily accessible data.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Cloudera

JANUARY 22, 2019

Today’s data landscape is characterized by exponentially increasing volumes of data, comprising a variety of structured, unstructured, and semi-structured data types originating from an expanding number of disparate data sources located on-premises, in the cloud, and at the edge. High-value Analytics.

Big Data 44

Big Data Data Lake Internet of Things Enterprise 44

IBM Big Data Hub

AUGUST 28, 2023

Data extraction Once you’ve assigned numerical values, you will apply one or more text-mining techniques to the structured data to extract insights from social media data. Crisis management and risk management: Text mining serves as an invaluable tool for identifying potential crises and managing risks.

Data mining 55

Data mining Machine Learning Deep Learning Marketing 55