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

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.

Metadata 52

Metadata Management Business Intelligence Data Governance 52

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. Teams can’t access data to build their business use cases. For example, a product data tag is basic metadata.

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

DataKitchen

AUGUST 3, 2021

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. The communication between business units and data professionals is usually incomplete and inconsistent. DDD divides a system or model into smaller subsystems called domains.

Data Architecture 246

Data Architecture Data Lake Cost-Benefit Data Warehouse 246

IBM Big Data Hub

APRIL 10, 2023

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.

Strategy 91

Strategy Data Architecture Cost-Benefit Reporting 91

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

erwin

JULY 12, 2019

It also helps enterprises put these strategic capabilities into action by: Understanding their business, technology and data architectures and their inter-relationships, aligning them with their goals and defining the people, processes and technologies required to achieve compliance. Five Steps to GDPR/CCPA Compliance.

Data Governance 110

Data Governance Management Metadata Risk Management 110

Ontotext

DECEMBER 1, 2023

Content and data management solutions based on knowledge graphs are becoming increasingly important across enterprises. from Q&A with Tim Berners-Lee ) Finally, Sumit highlighted the importance of knowledge graphs to advance semantic data architecture models that allow unified data access and empower flexible data integration.

Metadata 78

Metadata Sales Consulting Enterprise 78

erwin

FEBRUARY 28, 2024

While the essence of success in data governance is people and not technology, having the right tools at your fingertips is crucial. Technology is an enabler, and for data governance this is essentially having an excellent metadata management tool. Next to data governance, data architecture is really embedded in our DNA.

Metadata 52

Metadata Data Governance Data Quality Technology 52

Ontotext

NOVEMBER 16, 2023

Figure 1 Shows the overall idea of a data mesh with the major components: What Is a Data Mesh and How Does It Work? Think of data mesh as an operational mode for organizations with a domain-driven, decentralized data architecture. What Is a Data Product and Who Owns Them?

Metadata 52

Metadata Data-driven Data Quality Data Architecture 52

Cloudera

JUNE 7, 2022

We do that on-prem with almost 1 ZB of data under management – nearly 20% of that global total. And, as Deloitte put it: “Hybrid cloud is the de facto model”. . Companies can now capitalize on the value in all their data, by delivering a hybrid data platform for modern data architectures with data anywhere.

IT 106

IT Data Architecture Unstructured Data Big Data 106

Alation

JANUARY 9, 2023

Another agency later also published a legally binding standard to strengthen risk management for financial institutions with specific language related to data architecture and IT infrastructure. This industry standard framework helps institutions “establish, enable and sustain a mature data management discipline.”.

Risk Management 52

Risk Management Risk Management Metadata 52

AWS Big Data

APRIL 26, 2023

Having an accurate and up-to-date inventory of all technical assets helps an organization ensure it can keep track of all its resources with metadata information such as their assigned oners, last updated date, used by whom, how frequently and more. Enables a hub-and-spoke model for Data Access in multiple AWS accounts in a data mesh fashion.

Metadata 52

Metadata Dashboards Visualization Consulting 52

CIO Business Intelligence

JUNE 23, 2022

We do that on-prem with almost 1 ZB of data under management – nearly 20% of that global total. And, as Deloitte put it: “Hybrid cloud is the de facto model”. . Companies can now capitalize on the value in all their data, by delivering a hybrid data platform for modern data architectures with data anywhere.

IT 83

IT Data Architecture Unstructured Data Big Data 83

erwin

OCTOBER 16, 2020

Now to survive and thrive in the face of radical disruption requires radical transformation and new business models. There’s a common denominator in what they’re all missing, and that is data intelligence. The result is an automated, real-time, high-quality data pipeline from which accurate insights can be derived.

Internet of Things 145

Internet of Things Data-driven Uncertainty Data Governance 145

Cloudera

OCTOBER 11, 2022

Because that is how models learn. You also need to continually feed models new data to keep them up to date. Most AI apps and ML models need different types of data – real-time data from devices, equipment, and assets and traditional enterprise data – operational, customer, service records. .

Snapshot 89

Snapshot Data Science Digital Transformation Metadata 89

Cloudera

JANUARY 5, 2023

Implementing data and analytics solutions that can manage the end-to-end data life cycle — from ingesting data from multiple sources to storing, processing, serving, analyzing, and modeling it to drive actionable insights. They also address the concerns of IT teams in allowing access to organizational data. .

Cost-Benefit 52

Cost-Benefit Business Objectives Machine Learning Data Architecture 52

AWS Big Data

JANUARY 31, 2023

In this post, we are excited to summarize the features that the AWS Glue Data Catalog, AWS Glue crawler, and Lake Formation teams delivered in 2022. Whether you are a data platform builder, data engineer, data scientist, or any technology leader interested in data lake solutions, this post is for you.

Data Lake 78

Data Lake Data Governance Data Architecture Machine Learning 78

Ontotext

APRIL 15, 2020

The FHIR RDF version of CORD-19 plans to use the PICO ontology for modeling the annotations and to store them back in GraphDB. Ontotext’s knowledge graph technology is at the core of Cochrane’s data architecture developed by our partners from Data Language. medications: 16,406 instances. procedures: 54,720 instances.

Publishing 105

Publishing Metadata Data mining Technology 105

IBM Big Data Hub

JUNE 30, 2022

The diversity of data types, data processing, integration and consumption patterns used by organizations has grown exponentially. Competition plays harder, and every day, new business models and alternatives driven by data and digitalization surface in almost every industry.

Data Strategy 57

Data Strategy Strategy Business Objectives Data-driven 57

Cloudera

MARCH 17, 2023

As the use of ChatGPT becomes more prevalent, I frequently encounter customers and data users citing ChatGPT’s responses in their discussions. I love the enthusiasm surrounding ChatGPT and the eagerness to learn about modern data architectures such as data lakehouses, data meshes, and data fabrics.

Unstructured Data 60

Unstructured Data Data Lake Data Warehouse Machine Learning 60

AWS Big Data

MARCH 10, 2023

Apache Iceberg overview Iceberg is an open-source table format that brings the power of SQL tables to big data files. It enables ACID transactions on tables, allowing for concurrent data ingestion, updates, and queries, all while using familiar SQL. The Iceberg table is synced with the AWS Glue Data Catalog.

Data Lake 98

Data Lake Sales Data Warehouse Snapshot 98

TDAN

APRIL 2, 2019

Although data modeling has been around for over 30 years, it ranks among the top areas from which database application problems arise. Today’s data modeling […]. Moreover, the severity of the problems ranges from totally incorrect functionality to freakishly miserable performance. The answer is quite unnerving.

Modeling 45

Modeling Data Architecture IT Metadata 45

Cloudera

JUNE 29, 2023

To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure. To improve the way they model and manage risk, institutions must modernize their data management and data governance practices.

Data Architecture 81

Data Architecture Risk Management Risk Management 81

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

Octopai

APRIL 20, 2023

Data mesh is an approach to data architecture that is intentionally distributed, where data is owned and governed by domain-specific teams who treat the data as a product to be consumed by other domain-specific teams. What are the principles behind data mesh architecture?

Data-driven 77

Data-driven Data Architecture Sales Modeling 77

AWS Big Data

MARCH 25, 2024

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. This scale and general-purpose adaptability are what makes FMs different from traditional ML models. FMs are multimodal; they work with different data types such as text, video, audio, and images.

Data Lake 86

Data Lake Unstructured Data Management Modeling 86

AWS Big Data

APRIL 19, 2023

Independent data products often only have value if you can connect them, join them, and correlate them to create a higher order data product that creates additional insights. A modern data architecture is critical in order to become a data-driven organization.

Technology 78

Technology Data-driven Machine Learning Sales 78

TDAN

JULY 5, 2022

Bounded Contexts / Ubiquitous Language My new book, Data Model Storytelling,[i] contains a section describing some of the most significant challenges data modelers and other Data professionals face. Like most of its predecessors, including Agile development and […].

Data-driven 102

Data-driven Modeling Data Architecture IT 102

erwin

OCTOBER 24, 2019

Untapped data, if mined, represents tremendous potential for your organization. While there has been a lot of talk about big data over the years, the real hero in unlocking the value of enterprise data is metadata , or the data about the data. Metadata Is the Heart of Data Intelligence.

Metadata 104

Metadata Management Data-driven Data Architecture 104

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

Cloudera

SEPTEMBER 28, 2020

The customer had shorter upgrade windows, which dictated the need to do an in-place upgrade by installing CDP on their existing environment, instead of a longer migration path that required data migrations, operation overhead to setup news clusters and time needed to procure and setup new hardware. on roadmap). Instead use Ranger REST API.

Testing 130

Testing Metadata Risk Data Science 130

TDAN

AUGUST 2, 2022

Canonical Data Models and Overlapping Connections In the previous article, I introduced and explained the approach to application development called ‘Domain-Driven Development’ (or DDD), explained some of the Data Management concerns with this approach, and described how a well-constructed data model can add value to a DDD project by helping to create (..)

Data-driven 59

Data-driven Modeling Management Data Architecture 59

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

JANUARY 10, 2024

In Computer Science, we are trained to use the Okham razor – the simplest model of reality that can get the job done is the best one. Limiting growth by (data integration) complexity Most operational IT systems in an enterprise have been developed to serve a single business function and they use the simplest possible model for this.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

TDAN

APRIL 4, 2023

Why would Technics Publications publish a book outside its specialty of data management? First, Graham is a world-renowned data modeler and the author of Data Modeling for Quality, and therefore many of his examples are in the field of data management. Second, and more […]

Publishing 52

Publishing Modeling Management Data Architecture 52

Peter James Thomas

AUGUST 18, 2018

Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation. Of course some architectures featured both paradigms as well. This required additional investments in metadata.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63