IBM Big Data Hub

FEBRUARY 14, 2024

Other uses include Netflix offering viewing recommendations powered by models that process data sets collected from viewing history; LinkedIn uses ML to filter items in a newsfeed, making employment recommendations and suggestions on who to connect with; and Spotify uses ML models to generate its song recommendations.

Cost-Benefit 83

Cost-Benefit Insurance Unstructured Data Machine Learning 83

AWS Big Data

OCTOBER 25, 2023

Unstructured data is information that doesn’t conform to a predefined schema or isn’t organized according to a preset data model. Unstructured information may have a little or a lot of structure but in ways that are unexpected or inconsistent.

Unstructured Data 120

Unstructured Data Metadata Management Analytics 120

IBM Big Data Hub

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

Ontotext

DECEMBER 16, 2020

In its third generation, Ontotext Platform enables organizations to build, use and evolve knowledge graphs as a hub for data, metadata and content. Landing at number two is this post about GraphDB ‘s contribution to the fight against the COVID-19 pandemic and how it helps the scientific community to make sense of messy data.

Metadata 52

Metadata Unstructured Data Visualization Enterprise 52

AWS Big Data

MARCH 25, 2024

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. This scale and general-purpose adaptability are what makes FMs different from traditional ML models. FMs are multimodal; they work with different data types such as text, video, audio, and images.

Data Lake 91

Data Lake Unstructured Data Management Modeling 91

Cloudera

SEPTEMBER 1, 2020

DDE also makes it much easier for application developers or data workers to self-service and get started with building insight applications or exploration services based on text or other unstructured data (i.e. data best served through Apache Solr). Coordinates distribution of data and metadata, also known as shards.

Snapshot 71

Snapshot Unstructured Data Dashboards Interactive 71

erwin

JUNE 27, 2019

The need for an effective data modeling tool is more significant than ever. For decades, data modeling has provided the optimal way to design and deploy new relational databases with high-quality data sources and support application development. Evaluating a Data Modeling Tool – Key Features.

Measurement 81

Measurement Modeling Unstructured Data Metadata 81

Smart Data Collective

SEPTEMBER 26, 2021

Data mining and knowledge go hand in hand, providing insightful information to create applications that can make predictions, identify patterns, and, last but not least, facilitate decision-making. Working with massive structured and unstructured data sets can turn out to be complicated. It’s a good idea to record metadata.

Metadata 98

Metadata Visualization Unstructured Data Data mining 98

Ontotext

FEBRUARY 26, 2024

From a technological perspective, RED combines a sophisticated knowledge graph with large language models (LLM) for improved natural language processing (NLP), data integration, search and information discovery, built on top of the metaphactory platform. Here’s how our tool makes it work.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

FineReport

FEBRUARY 5, 2020

Metadata management. Users can centrally manage metadata, including searching, extracting, processing, storing, sharing metadata, and publishing metadata externally. The metadata here is focused on the dimensions, indicators, hierarchies, measures and other data required for business analysis. of BI pages.

Metadata 98

Metadata Dashboards Informatics Visualization 98

Domino Data Lab

JANUARY 21, 2021

Producing insights from raw data is a time-consuming process. Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. Results become the basis for understanding the solution space (or, ‘the realm of the possible’) for a given modeling task.

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

CIO Business Intelligence

JUNE 14, 2023

Admittedly, it’s still pretty difficult to visualize this difference. Additionally, it is vital to be able to execute computing operations on the 1000+ PB within a multi-parallel processing distributed system, considering that the data remains dynamic, constantly undergoing updates, deletions, movements, and growth.

Unstructured Data 62

Unstructured Data IT Manufacturing Visualization 62

Ontotext

JANUARY 20, 2022

We’ve already discussed that enterprise knowledge graphs bring together and harmonize all-important organizational knowledge and metadata. They focus on business-specific information needs and how to properly source the needed data rather than analyze preexisting application models. Analyzing Unstructured Data with GraphDB 9.8.

Enterprise 52

Enterprise Unstructured Data Modeling Visualization 52

Sisense

AUGUST 21, 2020

One result is that systems become much more intuitive: Users can take advantage of the “Simply Ask” feature to check “what are my sales next two months” and receive chatbot messages with projected visualizations and suggestions for further exploration routes. My take: The world is wider than the traditional BI tabular data.

Analytics 112

Analytics Machine Learning Dashboards Visualization 112

Cloudera

SEPTEMBER 6, 2018

Provide audit and data lineage information to facilitate regulatory reviews. Adapt to changes in the business and regulatory requirements by facilitating the provisioning of new data and modeling of new scenarios. It supports a variety of storage engines that can handle raw files, structured data (tables), and unstructured data.

Machine Learning 40

Machine Learning Big Data Risk Unstructured Data 40

Smart Data Collective

OCTOBER 13, 2021

There are countless examples of big data transforming many different industries. It can be used for something as visual as reducing traffic jams, to personalizing products and services, to improving the experience in multiplayer video games. We would like to talk about data visualization and its role in the big data movement.

Visualization 135

Visualization Cost-Benefit Big Data Prescriptive Analytics 135

CIO Business Intelligence

MARCH 6, 2023

Advancements in analytics and AI as well as support for unstructured data in centralized data lakes are key benefits of doing business in the cloud, and Shutterstock is capitalizing on its cloud foundation, creating new revenue streams and business models using the cloud and data lakes as key components of its innovation platform.

Data Lake 88

Data Lake Cost-Benefit Recreation/Entertainment Experimentation 88

Rocket-Powered Data Science

MARCH 20, 2023

Content includes reports, documents, articles, presentations, visualizations, video, and audio representations of the insights and knowledge that have been extracted from data. We could further refine our opening statement to say that our business users are too often in a state of being data-rich, but insights-poor, and content-hungry.

Data Warehouse 325

Data Warehouse Metadata Digital Transformation Unstructured Data 325

Cloudera

APRIL 1, 2024

We scored the highest in hybrid, intercloud, and multi-cloud capabilities because we are the only vendor in the market with a true hybrid data platform that can run on any cloud including private cloud to deliver a seamless, unified experience for all data, wherever it lies. Unlike software, ML models need continuous tuning.

Unstructured Data 109

Unstructured Data Cost-Benefit Metadata Machine Learning 109

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. Limited flexibility to use more complex hosting models (e.g.,

Data Processing 97

Data Processing Data Warehouse Enterprise Visualization 97