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

FEBRUARY 12, 2021

Shockingly, a lot of organizations, even today, manage this through, either homemade tools or documents, checklists, Excel files, custom-made databases and so on and so forth. Traditionally, these are manually documented, monitored and managed. Processes produce, process and consume data –information captured in the metadata layer.

Risk 98

Risk Slice and Dice Management Enterprise 98

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

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 79

Risk Modeling Management Metadata 79

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

O'Reilly on Data

DECEMBER 12, 2023

If the output of a model can’t be owned by a human, who (or what) is responsible if that output infringes existing copyright? In an article in The New Yorker , Jaron Lanier introduces the idea of data dignity, which implicitly distinguishes between training a model and generating output using a model.

Modeling 253

Modeling Software Sales Statistics 253

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

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.”. The Gartner document is available upon request from www.erwin.com/GartnerMMMQleader.

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

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 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 94

Modeling Statistics Machine Learning Testing 94

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

Rocket-Powered Data Science

JULY 6, 2021

This is accomplished through tags, annotations, and metadata (TAM). So, there must be a strategy regarding who, what, when, where, why, and how is the organization’s content to be indexed, stored, accessed, delivered, used, and documented. Smart content includes labeled (tagged, annotated) metadata (TAM).

Strategy 266

Strategy Machine Learning Metadata Knowledge Discovery 266

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

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

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

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. Of course, unstructured enterprise data consists primarily of content in document form, and if it’s not discoverable or reusable, it’s useless. Ivo’s been at Ontotext for over 14 years.

Metadata 52

Metadata Statistics Interactive Enterprise 52

Rocket-Powered Data Science

FEBRUARY 15, 2023

While generative AI has been around for several years , the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities. So, if you have 1 trillion data points (g.,

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

IBM Big Data Hub

OCTOBER 4, 2022

Multiple unsupported tools for building and deploying models. According to Gartner 54% of models are stuck in pre-production because there is not an automated process to manage these pipelines and there is a need to ensure the AI models can be trusted.” Manual processes that introduce risk and make it hard to scale.

Metadata 103

Metadata Risk Risk Management Experimentation 103

AWS Big Data

JULY 14, 2023

The only document given to Bob was PowerPoint slides and the names of the head of each department to get more information. To collect that information, Bob gets in touch with the head of each department, who in turn refer him to their development leads, who in turn give him a bunch of technical documents that explain how APIs are being used.

Modeling 67

Modeling Visualization Reporting Measurement 67

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

IBM Big Data Hub

JANUARY 8, 2024

The judiciary, like the legal system in general, is considered one of the largest “text processing industries” Language, documents, and texts are the raw material of legal and judicial work. As such, the judiciary has long been a field ripe for the use of technologies like automation to support the processing of documents.

Management 94

Management IT Digital Transformation Metadata 94

Ontotext

JULY 11, 2019

It provides the core infrastructure for solutions where modelling agility, data integration, relationship exploration, cross-enterprise data publishing and consumption are critical. . GraphDB: Semantic Text Similarity for Identifying Related Terms & Documents. Databases are very good for answering precise and complex questions.

Statistics 40

Statistics Modeling Metadata Enterprise 40

erwin

OCTOBER 8, 2020

But even with the “need for speed” to market, new applications must be modeled and documented for compliance, transparency and stakeholder literacy. With all these diverse metadata sources, it is difficult to understand the complicated web they form much less get a simple visual flow of data lineage and impact analysis.

Data Governance 145

Data Governance Metadata Testing Data Lake 145

Ontotext

MARCH 15, 2024

Moreover, the complexity of medical language and concepts requires QA models to have a deep understanding of context, making it challenging to develop robust systems that can accurately interpret queries and provide relevant answers across languages. QA fine-tuning The next step in the training pipeline is task-specific fine-tuning.

Modeling 59

Modeling Metadata Testing Optimization 59

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. However, governance processes are equally important. from cognilytica.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 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

Ontotext

JULY 11, 2019

The core idea behind semantic vector models is that words and concepts are represented by points in a mathematical space. So, words that co-occur frequently, documents composed by similar words, and concepts with similar or related meaning are placed close to one another in that space. Preparation.

Statistics 40

Statistics Modeling Metadata IT 40

CIO Business Intelligence

MARCH 24, 2023

The Data Governance Institute defines it as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”

Data Governance 142

Data Governance Management Metadata Data Quality 142

CIO Business Intelligence

DECEMBER 13, 2023

and applying and enriching metadata helps organizations take a big step toward innovating with generative AI. Digitizing relevant physical assets and objects, such as those core samples, IT equipment, office equipment, etc.

Unstructured Data 98

Unstructured Data IoT Metadata Manufacturing 98

AWS Big Data

JANUARY 9, 2024

Now users seek methods that allow them to get even more relevant results through semantic understanding or even search through image visual similarities instead of textual search of metadata. Lexical search In lexical search, the search engine compares the words in the search query to the words in the documents, matching word for word.

Cost-Benefit 88

Cost-Benefit Visualization Modeling Machine Learning 88

CIO Business Intelligence

OCTOBER 13, 2023

Application data architect: The application data architect designs and implements data models for specific software applications. Data scientists are experts in applying computer science, mathematics, and statistics to building models.

Data Architecture 132

Data Architecture Data Warehouse Statistics Visualization 132

erwin

MARCH 12, 2020

Almost 70 percent of CEOs say they expect their companies to change their business models in the next three years, and 62 percent report they have management initiatives or transformation programs underway to make their businesses more digital, according to Gartner. A change in the source-column header may impact 1,500 design mappings.

Metadata 140

Metadata Digital Transformation Data-driven Enterprise 140

erwin

SEPTEMBER 3, 2020

It documents your data assets from end to end for business understanding and clear data lineage with traceability. Data governance and EA also provide many of the same benefits of enterprise architecture or business process modeling projects: reducing risk, optimizing operations, and increasing the use of trusted data.

Data Governance 144

Data Governance Enterprise Risk Data Lake 144

Ontotext

DECEMBER 16, 2021

Organizations in this sector often deal with multiple repositories of millions of documents on top of proprietary data from labs and other internal sources. Domain experts can document their knowledge of terms and concepts within the graph and then connect the nodes to data stored in any number of external sources. See figure 1.).

Metadata 98

Metadata Reporting Unstructured Data Risk 98

erwin

JANUARY 14, 2021

These surveys helped IDC develop a model that describes the five stages of enterprise recovery , aligning business focus with the economic situation: When the COVID-19 crisis hit, organizations focused on business continuity. Technical metadata is what makes up database schema and table definitions.

Digital Transformation 143

Digital Transformation Metadata Big Data Data-driven 143