IBM Big Data Hub

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 78

Data Science Data Analytics Prescriptive Analytics Analytics 78

CIO Business Intelligence

SEPTEMBER 7, 2022

Dubbed Cropin Cloud, the suite comes with the ability to ingest and process data, run machine learning models for quick analysis and decision making, and several applications specific to the industry’s needs. In the US, Aggio, founded in 2016, offers a cloud-based sales and market-intelligence platform.

B2B 86

B2B Contextual Data IoT Deep Learning 86

Alation

JUNE 28, 2022

Track data lineage: Document data origins, record data transformation and movement, and visualize flow throughout the entire data lifecycle. Enhance the user experience: Create a shared source of truth for all users to build confidence in data. Supports Strong Data Culture.

Metadata 52

Metadata Management Data Governance Machine Learning 52

Alation

FEBRUARY 20, 2020

It’s a truism that data is the most important asset in the 21 st century economy. But, today too many enterprises exist in a data fog, with poorly contextualized data scattered across millions of tables. Dispelling this data fog is one of the key challenges for the next generation enterprise.

Contextual Data 52

Contextual Data Data Lake Metadata Enterprise 52

IBM Big Data Hub

DECEMBER 16, 2022

Because Alex can use a data catalog to search all data assets across the company, she has access to the most relevant and up-to-date information. She can search structured or unstructured data, visualizations and dashboards, machine learning models, and database connections. Meaningful business context.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

Ontotext

AUGUST 31, 2023

While a knowledge graph can exist without an ontology, an ontology is often represented in a knowledge graph because of the natural human desire to organize data—visually or in structure. Machine-interpretable: Designed to be processed, analyzed, and interpreted by humans and machines.

Modeling 64

Modeling Optimization Knowledge Discovery Enterprise 64