CILIP

JUNE 26, 2019

Success of AI in academic libraries depends on good underlying data. nder, scientific information specialist: Success of AI in academic libraries depends on good underlying data. Why do we hear so little in this respect from libraries on this side of the Atlantic? Q&A with Stephan Holl?nder,

Libraries 40

Libraries Unstructured data Artificial Intelligence Archiving 40

IBM Big Data Hub

DECEMBER 19, 2023

Machine learning algorithms for unstructured data include: K-means: This algorithm is a data visualization technique that processes data points through a mathematical equation with the intention of clustering similar data points. Through fast and comprehensive analysis, IBM watson.ai

Unstructured data 72

Unstructured data Artificial Intelligence Sales Manufacturing 72

Everteam

JANUARY 22, 2019

The intersection of Structured and Unstructured Data. Today there is a clear separation on how you manage structured data (database, transactional data) and unstructured data (documents, text, videos, images, email, social media, etc.). Data on legal hold = <1%. Record-worthy data = <2%.

Information governance 58

Information governance Government Unstructured data Analytics 58

erwin

JUNE 26, 2020

The data catalog is a searchable asset that enables all data – including even formerly siloed tribal knowledge – to be cataloged and more quickly exposed to users for analysis. Another classic example is the online or card catalog at a library. for analysis and integration purposes). Operational Metadata.

Metadata 132

Metadata Unstructured data Compliance Sales 132

IBM Big Data Hub

DECEMBER 16, 2022

Imagine walking into the largest library you’ve ever seen. Fortunately, the library has a computer at the front desk you can use to search its entire inventory by title, author, genre, and more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Metadata 52

Metadata Data Privacy Access Libraries 52

ARMA International

SEPTEMBER 22, 2021

Another example is when sensitive information is removed from transaction data after meeting operational requirements, but the data is kept for analytical processing such as market research and trend analysis. Managing this data and content to derive knowledge and actionable insight involves both data management and KM.

Digital transformation 52

Digital transformation Content services IT e-Discovery 52

IBM Big Data Hub

DECEMBER 1, 2023

Role of generative AI in digital transformation and core modernization Whether used in routine IT infrastructure operations, customer-facing interactions, or back-office risk analysis, underwriting and claims processing, traditional AI and generative AI are key to core modernization and digital transformation initiatives.

Insurance 107

Insurance Digital transformation Customer Experience Cloud 107