Ontotext

MARCH 27, 2024

For example, GPS, social media, cell phone handoffs are modeled as graphs while data catalogs, data lineage and MDM tools leverage knowledge graphs for linking metadata with semantics. Knowledge graphs model knowledge of a domain as a graph with a network of entities and relationships.

Modeling 69

Modeling Metadata Data Quality Enterprise 69

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

APRIL 16, 2024

In this blog, we’ll delve into the critical role of governance and data modeling tools in supporting a seamless data mesh implementation and explore how erwin tools can be used in that role. erwin also provides data governance, metadata management and data lineage software called erwin Data Intelligence by Quest.

Metadata 52

Metadata Data Quality Data Governance Modeling 52

erwin

AUGUST 31, 2023

Q: Is data modeling cool again? Amidst the evolving technological landscape, one constant remains despite the ongoing attacks from nay-sayers: the importance of data modeling as a foundational step in the delivery of data to these forward-thinking organizations. A: It always was and is getting cooler!!

Data-driven 98

Data-driven Modeling Enterprise Structured Data 98

TDAN

AUGUST 18, 2020

Standards exist for naming conventions, abbreviations and other pertinent metadata properties. Consistent business meaning is important because distinctions between business terms are not typically well defined or documented. What are the standards for writing […].

Management 96

Management Metadata Data Architecture IT 96

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 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

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 Data Kits are containers of Data Cloud metadata that can be packaged and installed from the Salesforce AppExchange.

Data Integration 105

Data Integration Data Lake Metadata Data Warehouse 105

erwin

SEPTEMBER 25, 2020

Understanding the benefits of data modeling is more important than ever. Data modeling is the process of creating a data model to communicate data requirements, documenting data structures and entity types. In this post: What Is a Data Model? Why Is Data Modeling Important? What’s the Best Data Modeling Tool?

Modeling 111

Modeling Cost-Benefit Visualization Metadata 111

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 83

Modeling Consulting Metadata Enterprise 83

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 84

Modeling Statistics Machine Learning Testing 84

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 96

Metadata Data Governance Modeling Technology 96

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

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

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

DataKitchen

FEBRUARY 2, 2023

Most data governance tools today start with the slow, waterfall building of metadata with data stewards and then hope to use that metadata to drive code that runs in production. In reality, the ‘active metadata’ is just a written specification for a data developer to write their code.

Metadata 130

Metadata Testing Risk Data Science 130

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.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

AWS Big Data

OCTOBER 4, 2023

Business-driven domains – A DataZone domain represents the distinct boundary of a line of business (LOB) or a business area within an organization that can manage its own data, including its own data assets, its own definition of data or business terminology, and may have its own governing standards.

Metadata 102

Metadata Data Lake Publishing Data Governance 102

CIO Business Intelligence

MARCH 24, 2023

Data governance definition Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. Programs must support proactive and reactive change management activities for reference data values and the structure/use of master data and metadata.

Data Governance 142

Data Governance Management Metadata Data Quality 142

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

Ontotext

AUGUST 22, 2023

Without metadata management and other data-related operations with semantic technologies, organizations often struggle to connect data sets and achieve a unified view of their enterprise data. Enter the Semantic Edge Era: How to Derive Value from Semantic Metadata The problem with Big Data is not the data itself.

Metadata 52

Metadata Knowledge Discovery Big Data Enterprise 52

erwin

DECEMBER 3, 2020

The answer is radical transformation, made possible by an intelligent, data-driven approach to: New business models. Being able to integrate all data touchpoints, including erwin DM for data modeling, Denodo for data visualization, and Jira for ticketing, has been key. New products and services. Hyper-competition. Market expansion.

Metadata 141

Metadata Data Governance Data-driven Digital Transformation 141

Ontotext

DECEMBER 7, 2023

Semantics utilizes a number of representation and modeling instruments to express and interpret the data of a knowledge graph. Relation types can also have formal definitions. Knowledge graphs in RDF One of the common graph data models is the Resource Description Framework (RDF).

Metadata 64

Metadata Modeling Software Statistics 64

CIO Business Intelligence

OCTOBER 26, 2023

Through use of data analytics, data visualization, and data modeling techniques and technologies, BI analysts can identify trends that can help other departments, managers, and executives make business decisions to modernize and improve processes in the organization.

Business Intelligence 125

Business Intelligence Data-driven Statistics Data Warehouse 125

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

IBM Big Data Hub

APRIL 11, 2024

In the context of AI, it can refer to the safety and ethics guardrails of AI tools and systems, policies concerning data access and model usage or the government-mandated regulation itself. Therefore, we see national and international guidelines address these overlapping and intersecting definitions in a variety of ways.

Risk 76

Risk Consulting Modeling Data Processing 76

erwin

JULY 17, 2019

Part Two of the Digital Transformation Journey … In our last blog on driving digital transformation , we explored how enterprise architecture (EA) and business process (BP) modeling are pivotal factors in a viable digital transformation strategy. Ultimately, data is the foundation of the new digital business model.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

IBM Big Data Hub

OCTOBER 4, 2023

Typically, on their own, data warehouses can be restricted by high storage costs that limit AI and ML model collaboration and deployments, while data lakes can result in low-performing data science workloads. Also, a lakehouse can introduce definitional metadata to ensure clarity and consistency, which enables more trustworthy, governed data.

Data Lake 80

Data Lake Metadata Cost-Benefit Data Warehouse 80

DataKitchen

OCTOBER 11, 2021

Second, you must establish a definition of “done.” In DataOps, the definition of done includes more than just some working code. Definition of Done. Based on business rules, additional data quality tests check the dimensional model after the ETL job completes. Monitoring Job Metadata. When can you declare it done?

Testing 152

Testing Metadata Dashboards Statistics 152

AWS Big Data

NOVEMBER 29, 2023

It enables data engineers, data scientists, and analytics engineers to define the business logic with SQL select statements and eliminates the need to write boilerplate data manipulation language (DML) and data definition language (DDL) expressions. Models – dbt models are primarily written as a SELECT statement and saved as a.sql file.

Data Lake 105

Data Lake Management Metrics Data Warehouse 105

Cloudera

JUNE 7, 2023

By now, almost everyone across the tech landscape has heard of the Zero Trust (ZT) security model, which assumes that every device, application, or user attempting to access a network is not to be trusted (see NIST definitions below). But as models go, the idea is easier than the execution.

Metadata 81

Metadata Data Lake Optimization Modeling 81

Cloudera

MARCH 9, 2021

In this blog, we’ll highlight the key CDP aspects that provide data governance and lineage and show how they can be extended to incorporate metadata for non-CDP systems from across the enterprise. Atlas provides open metadata management and governance capabilities to build a catalog of all assets, and also classify and govern these assets.

Data Governance 95

Data Governance Metadata Enterprise Data Processing 95

erwin

JUNE 22, 2020

As the 80/20 rule suggests, getting through hundreds, or perhaps thousands of individual business terms using this one-hour meeting model can take … a … long … time. Now that pulling stakeholders into a room has been disrupted … what if we could use this as 40 opportunities to update the metadata PER DAY?

Metadata 105

Metadata Data Governance Digital Transformation Measurement 105

Cloudera

SEPTEMBER 29, 2022

Each of these trends claim to be complete models for their data architectures to solve the “everything everywhere all at once” problem. The data product is not just the data itself, but a bunch of metadata that surrounds it — the simple stuff like schema is a given. What are the different definitions of hybrid architectures?

Data Architecture 74

Data Architecture Metadata Data Warehouse Sales 74

Jen Stirrup

MARCH 29, 2023

They need a solid data foundation to support primary data initiatives such as data integration, cataloging, semantic modeling, warehousing, federation, and virtualization through a unified interface to get value. Why is this interesting?

Metadata 121

Metadata Data Warehouse Data Integration Business Intelligence 121

Alation

MAY 31, 2023

Maintain data models and documentation. Creating and maintaining data models In order to understand the structure and relationships of an organization’s data assets, data models play a significant role. They curate metadata about assets and develop technical solutions. Ensure data security and compliance.

Data Governance 52

Data Governance Metadata Data Architecture Data Quality 52

DataKitchen

AUGUST 8, 2023

However, the market for Data Observability is fragmented and lacks a standard accepted definition, leading to confusion and tool adoption issues. Data Observability leverages five critical technologies to create a data awareness AI engine: data profiling, active metadata analysis, machine learning, data monitoring, and data lineage.

Data Quality 130

Data Quality Testing Snapshot Reporting 130