CIO Business Intelligence

MARCH 24, 2023

To help understand what a framework should cover, DAMA envisions data management as a wheel, with data governance as the hub from which the following 10 data management knowledge areas radiate: Data architecture : The overall structure of data and data-related resources as an integral part of the enterprise architecture Data modeling and design: Analysis, (..)

Data Governance 142

Data Governance Management Metadata Data Quality 142

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

It is the only solution that can automatically harvest, transform and feed metadata from operational processes, business applications and data models into a central data catalog and then made accessible and understandable within the context of role-based views. Standardize data management processes through a metadata-driven approach.

Metadata 104

Metadata Management Data-driven Data Architecture 104

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

Corinium

APRIL 25, 2019

It definitely depends on the type of data, no one method is always better than the other. For a large volume of structured data, for example, a customer master or data warehouse, where there are many stakeholders in your organization who need to see different subsets, tokenization is generally better.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

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.

Big Data 44

Big Data Data Lake Internet of Things Enterprise 44

Jet Global

MAY 9, 2024

The architecture may vary depending on the specific use case and requirements, but it typically includes stages of data ingestion, transformation, and storage. Data ingestion methods can include batch ingestion (collecting data at scheduled intervals) or real-time streaming data ingestion (collecting data continuously as it is generated).

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52