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

Ontotext

FEBRUARY 14, 2024

CEEL is an end-to-end transformer-based model that includes mention detection and uses entity types and entity descriptions to perform entity linking. Out of the box, the model links to Wikidata IDs. That’s why such models must use context in order to differentiate between entities, just as humans do.

Cost-Benefit 52

Cost-Benefit Modeling Metadata Optimization 52

Domino Data Lab

OCTOBER 9, 2019

Paco Nathan’s latest article covers data practices from the National Oceanic and Atmospheric Administration (NOAA) Environment Data Management (EDM) workshop as well as updates from the AI Conference. Metadata Challenges. The metadata must be converted into both W3C and ISO standards for publication.

Deep Learning 69

Deep Learning Metadata Machine Learning Data Science 69

datapine

JANUARY 31, 2022

Experts say that BI and data analytics makes the decision-making process 5x times faster for businesses. Renowned author Bernard Marr wrote an insightful article about Shell’s journey to become a fully data-driven company. Let’s look at our first use case.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. between 2022 and 2029.

Data Governance 52

Data Governance Marketing Machine Learning Sales 52

Ontotext

MAY 18, 2020

Such problems and the complexities related to such computationally-intensive tasks are essential in the fields of weather forecasting, molecular modeling, airplane and spacecraft aerodynamics, personalized medicine, self-driving cars. There are four types of data sources that the team will work with. will be processed.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. between 2022 and 2029.

Data Governance 40

Data Governance Marketing Machine Learning Sales 40

TDAN

APRIL 30, 2019

Data collection is getting more dispersed and voluminous every day. Enterprises create and collect information from a variety of data sources which may include websites, mobile devices, customers, vendors, and other numerous sources.

Management 69

Management Data Collection Enterprise Data Governance 69

Domino Data Lab

JULY 22, 2019

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Jet Global

MAY 1, 2023

These licensing terms are critical: Perpetual license vs subscription: Subscription is a pay-as-you-go model that provides flexibility as you evaluate a vendor. Pricing model: The pricing scale is dependent on several factors. Let’s just give our customers access to the data. Perpetual, by contrast, is paid up front.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Alation

FEBRUARY 8, 2022

In this blog, we’ll do the same with data mesh, unpacking the four key pillars, with a few notes on the role of the data catalog. While data fabric takes a product-and-tech-centric approach, data mesh takes a completely different perspective. That model has failed to support rapid data growth. Levi’s, etc.)

Data Governance 52

Data Governance Data-driven Metadata Enterprise 52

Alation

FEBRUARY 20, 2020

For many enterprises, a hybrid cloud data lake is no longer a trend, but becoming reality. With a cloud deployment, enterprises can leverage a “pay as you go” model; reducing the burden of incurring capital costs. Due to these needs, hybrid cloud data lakes emerged as a logical middle ground between the two consumption models.

Data Lake 52

Data Lake ROI Metadata Cost-Benefit 52

Alation

JANUARY 17, 2023

In this article, I will explain the modern data stack in detail, list some benefits, and discuss what the future holds. What Is the Modern Data Stack? The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

IBM Big Data Hub

AUGUST 4, 2023

When workers get their hands on the right data, it not only gives them what they need to solve problems, but also prompts them to ask, “What else can I do with data?” ” through a truly data literate organization. What is data democratization?

Data Architecture 59

Data Architecture Data Lake Machine Learning Data Governance 59

Alation

SEPTEMBER 1, 2022

What’s worse, those best equipped to help are too busy: 55% of respondents stated that, “the few available data experts with business domain expertise have neither the time for nor the inclination to prioritize this task.”. Get the new IDC Marketscape for Data Catalogs to learn more. What goes into a Data Intelligence Platform?

Reporting 52

Reporting Metadata Data Governance Interactive 52