erwin

MAY 13, 2020

The role of data modeling (DM) has expanded to support enterprise data management, including data governance and intelligence efforts. After all, you can’t manage or govern what you can’t see, much less use it to make smart decisions. Types of Data Models: Conceptual, Logical and Physical.

Data Governance 93

Data Governance Modeling Enterprise Management 93

Cloudera

NOVEMBER 29, 2023

That’s why we’re excited to announce the Cloudera Model Registry as generally available, a game-changer that’s set to transform the way you manage your machine learning models in production environments. That’s where the Model Registry steps in. That’s where the Model Registry steps in.

Modeling 82

Modeling Machine Learning Metadata Metrics 82

Alation

FEBRUARY 20, 2020

Gartner predicts that “By 2020, 50% of information governance initiatives will be enacted with policies based on metadata alone.”. Magic Quadrant for Metadata Management Solutions , Guido de Simoni and Roxane Edjlali, August 10, 2017. Metadata management no longer refers to a static technical repository.

Metadata 52

Metadata Management Data-driven Data Governance 52

Alation

FEBRUARY 13, 2020

In an earlier blog, I defined a data catalog as “a collection of metadata, combined with data management and search tools, that helps analysts and other data users to find the data that they need, serves as an inventory of available data, and provides information to evaluate fitness data for intended uses.”.

Metadata 52

Metadata Management Data Lake Data Governance 52

erwin

OCTOBER 18, 2019

erwin positioned as a Leader in Gartner’s “2019 Magic Quadrant for Metadata Management Solutions”. We were excited to announce earlier today that erwin was named as a Leader in the @Gartner _inc “2019 Magic Quadrant for Metadata Management Solutions.”. GET THE REPORT NOW. This graphic was published by Gartner, Inc.

Metadata 63

Metadata Management Reporting Marketing 63

Ontotext

JANUARY 29, 2024

As companies in almost every market segment attempt to continuously enhance and modernize data management practices to drive greater business outcomes, organizations will be watching numerous trends emerge this year. I expect to see the following data and knowledge management trends emerge in 2024.

Strategy 52

Strategy Management Metadata Data-driven 52

Alation

MARCH 2, 2023

Analysis, however, requires enterprises to find and collect metadata. In fact, Gartner’s “Market Guide for Active Metadata Management” points to “ active metadata management ” as the key to continuous data analysis – which supports smarter human usage and more valuable insights. What Is Active Metadata Management?

Metadata 52

Metadata Marketing IT Data Quality 52

Octopai

APRIL 19, 2021

Aptly named, metadata management is the process in which BI and Analytics teams manage metadata, which is the data that describes other data. In other words, data is the context and metadata is the content. Without metadata, BI teams are unable to understand the data’s full story. Donna Burbank.

Metadata 52

Metadata Management Business Intelligence Data Governance 52

erwin

JANUARY 9, 2020

Enterprises are trying to manage data chaos. For decades, data modeling has been the optimal way to design and deploy new relational databases with high-quality data sources and support application development. Today’s data modeling is not your father’s data modeling software.

Data Governance 144

Data Governance Modeling Metadata Unstructured Data 144

CIO Business Intelligence

MARCH 24, 2023

It encompasses the people, processes, and technologies required to manage and protect data assets. The Data Management Association (DAMA) International defines it as the “planning, oversight, and control over management of data and the use of data and data-related sources.”

Data Governance 142

Data Governance Management Metadata Data Quality 142

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools.

Management 332

Management Machine Learning Experimentation Metrics 332

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

erwin

JANUARY 17, 2020

What is Data Modeling? Data modeling is a process that enables organizations to discover, design, visualize, standardize and deploy high-quality data assets through an intuitive, graphical interface. Data models provide visualization, create additional metadata and standardize data design across the enterprise.

Data-driven 138

Data-driven Modeling Metadata Data Governance 138

erwin

APRIL 16, 2020

We’re excited about our recognition as a March 2020 Gartner Peer Insights Customers’ Choice for Metadata Management Solutions. Metadata management is key to sustainable data governance and any other organizational effort that is data-driven.

Metadata 52

Metadata Management Data Governance Digital Transformation 52

Ontotext

FEBRUARY 1, 2024

We as a taxonomy community are facing rapid technological change and grappling with where our discipline fits into a world captivated by ChatGPT and other generative models. The challenge of complexity Knowledge models for taxonomy have responded to an increasing need for sophistication and expressiveness.

Enterprise 59

Enterprise Management Metadata Modeling 59

TDAN

APRIL 6, 2021

A metadata-driven data warehouse (MDW) offers a modern approach that is designed to make EDW development much more simplified and faster. It makes use of metadata (data about your data) as its foundation and combines data modeling and ETL functionalities to build data warehouses.

Data Warehouse 52

Data Warehouse Metadata Data-driven Modeling 52

erwin

JULY 2, 2020

Users discuss how they are putting erwin’s data modeling, enterprise architecture, business process modeling, and data intelligences solutions to work. IT Central Station members using erwin solutions are realizing the benefits of enterprise modeling and data intelligence. Data Modeling with erwin Data Modeler.

Enterprise 122

Enterprise Modeling Metadata Data Governance 122

Ontotext

AUGUST 19, 2020

Metadata used to be a secret shared between system programmers and the data. Metadata described the data in terms of cardinality, data types such as strings vs integers, and primary or foreign key relationships. Inevitably, the information that could and needed to be expressed by metadata increased in complexity.

Metadata 59

Metadata Knowledge Discovery Cost-Benefit Data Governance 59

CIO Business Intelligence

APRIL 25, 2024

The zero-copy pattern helps customers map the data from external platforms into the Salesforce metadata model, providing a virtual object definition for that object. “It It’s a much more seamless process for customers than having to purchase a third-party reverse ETL tool or manage some sort of pipeline back into Salesforce.”

Data Integration 98

Data Integration Data Lake Metadata Data Warehouse 98

datapine

SEPTEMBER 29, 2022

1) What Is Data Quality Management? However, with all good things comes many challenges and businesses often struggle with managing their information in the correct way. Enters data quality management. What Is Data Quality Management (DQM)? Why Do You Need Data Quality Management? Table of Contents.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Alation

JANUARY 9, 2023

But data leaders must work quickly, and use the right tools, to understand, manage, and protect data while complying with related regulations and standards. The Increasing Focus On Data Risk Management. The Australian Prudential Regulation Authority (APRA) released nonbinding standards covering data risk management.

Risk Management 52

Risk Management Risk Management Metadata 52

Cloudera

APRIL 18, 2024

With the arrival of the latest AI-powered technologies like large language models (LLMs) and generative AI (GenAI), there’s a vast amount of opportunities for innovation, growth, and improved business outcomes right around the corner. Do I trust the models to give me useful insights? Do I trust my data to be ready for use in AI?

Analytics 104

Analytics Metadata Data Strategy Optimization 104

DataRobot Blog

MAY 10, 2022

Addressing the Key Mandates of a Modern Model Risk Management Framework (MRM) When Leveraging Machine Learning . The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States.

Risk 64

Risk Modeling Machine Learning Data Quality 64

erwin

JULY 12, 2019

In light of recent, high-profile data breaches, it’s past-time we re-examined strategic data governance and its role in managing regulatory requirements. Creating and automating a curated enterprise data catalog , complete with physical assets, data models, data movement, data quality and on-demand lineage. How erwin Can Help.

Data Governance 110

Data Governance Management Metadata Risk Management 110

Octopai

MAY 31, 2022

Why on earth do you want to manage your data? The need for data management is a given. Data management for BI is different than data management for other reasons and, correspondingly, needs a different set of tools. These two types of data management (i.e. In contrast, data management for BI is asset management.

Management 52

Management Metadata Business Intelligence ROI 52

Ontotext

JUNE 27, 2019

It provides the core infrastructure for solutions where modeling agility, data integration, relationship exploration, cross-enterprise data publishing and consumption are critical. GraphDB: MongoDB Document Store Integration for Large-scale Metadata Management. Choose The Best RDF Database for Metadata Management.

Metadata 40

Metadata Management Enterprise Optimization 40

erwin

MARCH 17, 2020

Teams need to urgently respond to everything from massive changes in workforce access and management to what-if planning for a variety of grim scenarios, in addition to building and documenting new applications and providing fast, accurate access to data for smart decision-making. Enterprise Architecture & Business Process Modeling.

Data Governance 138

Data Governance Enterprise Modeling Metadata 138

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

AWS Big Data

FEBRUARY 29, 2024

Working with large language models (LLMs) for enterprise use cases requires the implementation of quality and privacy considerations to drive responsible AI. Enterprises are doing this by using proprietary data with approaches like Retrieval Augmented Generation (RAG), fine-tuning, and continued pre-training with foundation models.

Data Governance 91

Data Governance Unstructured Data Metadata Data Lake 91

Ontotext

JUNE 27, 2019

For example, it’s data generated as a result of text mining algorithms that includes document metadata attributes and annotations with links to the first type of information. It sends requests to MongoDB queries and then transforms the extracted data to an RDF model. Choose The Best RDF Database for Metadata Management.

Metadata 40

Metadata Management Optimization Enterprise 40

IBM Big Data Hub

APRIL 4, 2023

We taught an AI model to analyze Masters video and produce highlight reels for every player, minutes after their round is complete. We built models that generate scoring predictions for every player on every hole. The second need is closely related, and really applies to any AI model used in a corporate setting.

Modeling 85

Modeling Consulting Metadata Enterprise 85

Ontotext

APRIL 10, 2024

This is a “ Datastrophe “ Data managed correctly can turn assets into actionable insights, left unmanaged it can be the uranium, causing massive damage to enterprises with data and security breaches being rampant. It serves a hub for data, metadata, and content, thus making existing data interlinked and contextualized.

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

Ontotext

OCTOBER 12, 2023

Did you know that, if you add “take a deep breath” to a prompt, chances are you will get more accurate results from Large Language Models (LLMs)? Do Knowledge Graphs Dream of Large Language Models? I didn’t either. He shared the need for more research at the intersection of LLMs and knowledge graphs.

Modeling 75

Modeling Recreation/Entertainment Data Processing Metadata 75

IBM Big Data Hub

JANUARY 8, 2024

With digitization adopted by law firms and court systems, a trove of data in the form of court opinions, statutes, regulations, books, practice guides, law reviews, legal white papers and news reports are available to be used to train both traditional and generative AI foundation models by judicial agencies.

Management 89

Management IT Digital Transformation Metadata 89

Alation

DECEMBER 21, 2022

Master Data Management (MDM) and data catalog growth are accelerating because organizations must integrate more systems, comply with privacy regulations, and address data quality concerns. What Is Master Data Management (MDM)? Data Catalog and Master Data Management. MDM Model Objects.

Management 52

Management Data Quality Metadata Testing 52

Ontotext

MAY 2, 2024

Another challenge is the data models that represent how insights and analytics teams see the world. These models are as important to companies as their frontline products and determine how data is managed, consumed, combined, joined, and analyzed. Here we talk about metadata management, catalog of catalogs, and so on.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

AWS Big Data

JULY 14, 2023

The traditional portfolio management tools within the organization need to be augmented with additional components that can manage the complexity of the services ecosystem. The model should be able to showcase to LOBs their categories and capabilities. The following example shows a table structure to capture this information.

Modeling 68

Modeling Visualization Reporting Measurement 68