erwin

JANUARY 9, 2020

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. So here’s why data modeling is so critical to data governance.

Data Governance 144

Data Governance Modeling Metadata Unstructured Data 144

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

NOVEMBER 6, 2019

Often these enterprises are heavily regulated, so they need a well-defined data integration model that will help avoid data discrepancies and remove barriers to enterprise business intelligence and other meaningful use. The post Metadata Management, Data Governance and Automation appeared first on erwin, Inc.

Metadata 102

Metadata Data Governance Management Cost-Benefit 102

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

erwin

NOVEMBER 19, 2020

erwin has once again been positioned as a Leader in the Gartner “2020 Magic Quadrant for Metadata Management Solutions.”. The post erwin Positioned as a Leader in Gartner’s 2020 Magic Quadrant for Metadata Management Solutions for Second Year in a Row appeared first on erwin, Inc.

Metadata 98

Metadata Management Digital Transformation Data Governance 98

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

erwin

DECEMBER 19, 2019

To help you prepare for 2020, we’ve compiled some of the most popular data governance and metadata management blog posts from the erwin Experts from this year. The Best Data Governance and Metadata Management Blog Posts of 2019. Four Use Cases Proving the Benefits of Metadata-Driven Automation.

Metadata 75

Metadata Data Governance Management Digital Transformation 75

KDnuggets

MAY 31, 2022

Add Layer to your existing ML code and quickly get a rich model and data registry with experiment tracking!

Metadata 108

Metadata Modeling 108

IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. Foundation models: The power of curated datasets Foundation models , also known as “transformers,” are modern, large-scale AI models trained on large amounts of raw, unlabeled data.

Risk 77

Risk Modeling Management Metadata 77

IBM Big Data Hub

APRIL 13, 2023

After developing a machine learning model, you need a place to run your model and serve predictions. If your company is in the early stage of its AI journey or has budget constraints, you may struggle to find a deployment system for your model. Also, a column in the dataset indicates if each flight had arrived on time or late.

Modeling 91

Modeling Statistics Machine Learning Testing 91

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. – Gartner, Inc.,

Metadata 52

Metadata Management Data-driven Data Governance 52

erwin

MAY 13, 2020

The role of data modeling (DM) has expanded to support enterprise data management, including data governance and intelligence efforts. Metadata management is the key to managing and governing your data and drawing intelligence from it. Types of Data Models: Conceptual, Logical and Physical.

Data Governance 93

Data Governance Modeling Enterprise Management 93

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

erwin

MAY 2, 2022

How to create a solid foundation for data modeling of OLTP systems. As you undertake a cloud database migration , a best practice is to perform data modeling as the foundation for well-designed OLTP databases. This makes mastering basic data modeling techniques and avoiding common pitfalls imperative. Data modeling basics.

Modeling 98

Modeling Insurance Cost-Benefit Data Warehouse 98

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

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

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 88

Modeling Consulting Metadata Enterprise 88

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 82

Machine Learning Modeling Metadata Recreation/Entertainment 82

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. Critical Application for Information Governance ” -Information Scientist, Healthcare Industry.

Metadata 52

Metadata Management Data Governance Digital Transformation 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 92

Analytics Metadata Data Strategy Optimization 92

erwin

MARCH 17, 2020

Here are a few examples specific to enterprise architecture and business process modeling, data modeling and data governance. Enterprise Architecture & Business Process Modeling. Data Modeling. erwin Data Modeler can help you find, visualize, design, deploy and standardize high-quality enterprise data assets.

Data Governance 138

Data Governance Enterprise Modeling Metadata 138

TDAN

SEPTEMBER 3, 2019

As I write this, I can almost hear you wail “No, no, we don’t have too much metadata, we don’t have nearly enough! We have several projects in flight to expand our use of metadata.” Sorry, I’m going to have to disagree with you there. You are on a fool’s errand that will just provide […].

Metadata 65

Metadata Data Governance Big Data Modeling 65

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

CIO Business Intelligence

MARCH 18, 2024

Developers of risky generative AI models are now free to release them without government approval, but are being encouraged to self-regulate by adding labels to the output of their models warning of their potential unreliability. Akshara Bassi, senior analyst at Counterpoint, weighed in on the implications for AI model developers.

Metadata 93

Metadata Visualization Modeling Risk 93

Ontotext

APRIL 10, 2024

In most enterprises data teams lack a data map and data asset inventory and are often unaware of data that exists across the organization, its associated profile, quality and associated metadata. Proliferation of data catalogs across vendors creates metadata silos and does little to connect the data semantically.

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

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 93

Data Governance Unstructured Data Metadata Data Lake 93

TDAN

JUNE 20, 2023

These days, there is much conversation about the necessity of the data model. The data model has been around for several decades now and can be classified as an artifact of an earlier day and age. But is the data model really out of date? And exactly why do we need a data model, anyway? […]

Modeling 52

Modeling Metadata Data Strategy Data Governance 52

Ontotext

APRIL 19, 2024

We mainly talked about the company’s Metadata Studio and the types of features it has that give users the options I’ve listed above. NLP and the role of metadata-oriented feedback loops Moreover, text analysis or mining can add to that meaning and contribute even more to the knowledge in the graph.

Metadata 52

Metadata Statistics Interactive Enterprise 52

Ontotext

JANUARY 29, 2024

These will include developing a better understanding of AI, recognizing the role semantic metadata plays in data fabrics, and the rapid acceleration and adoption of knowledge graphs — which will be driven by large language models (LLMs) and the convergence of labeled property graphs (LPGs) and resource description frameworks (RDFs).

Strategy 52

Strategy Management Metadata Data-driven 52

AWS Big Data

JULY 14, 2023

Defining and capturing a business capability model If an enterprise doesn’t have a system to capture the business capability model, consider defining and finding a way to capture the model for better insight and visibility, and then map it with digital assets like APIs. It keeps evolving with business requirements and usage.

Modeling 69

Modeling Visualization Reporting Measurement 69

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 When released, this will extend zero-copy data access to any open data lake or lakehouse that stores data in Iceberg or can provide Iceberg metadata for its table.

Data Integration 52

Data Integration Data Lake Metadata Data Warehouse 52

Alation

JUNE 2, 2022

These include tracking, documenting, monitoring, versioning, and controlling access to AI/ML models. Currently, models are managed by modelers and by the software tools they use, which results in a patchwork of control, but not on an enterprise level. And until recently, such governance processes have been fragmented.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

CIO Business Intelligence

MAY 10, 2023

As companies open this “pandora’s box” of new capabilities, they must be prepared to manage data inputs and outputs in secure ways or risk allowing their private data to be consumed in public AI models. AI relies upon large sets of data fed into it to help create output but is limited by the quality of data that is consumed by the model.

Metadata 98

Metadata Data Governance Modeling Technology 98

Ontotext

OCTOBER 11, 2023

These delved deep into the transformative potential of Large Language Models (LLMs) and the need for semantic technologies to ensure high-quality training data for these models. He also shared key findings on how companies use (not that responsibly) training data to work with models. Are LLMs Knowledgeable? A demonstrator.

Metadata 69

Metadata Cost-Benefit Marketing Modeling 69

Ontotext

MARCH 13, 2024

OTKG models information about Ontotext, combined with content produced by different teams inside the organization. Our standard methodology for such projects is to start by defining competency questions that would help us understand what we need to model in our graph. What is OTKG?

Marketing 72

Marketing Knowledge Discovery Metadata Data-driven 72