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

Octopai

MARCH 23, 2022

If today, federal inspections and authorizations of meatpacking plants are made with some level of reliance on data systems (and they almost certainly are), then dirty data could even lead directly to dirty meat. Cleaning Up Dirty Data. Data Supervision. Everyone agrees that data quality is important.

Data Quality 91

Data Quality Reporting Modeling Interactive 91

AWS Big Data

FEBRUARY 29, 2024

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

Data Governance 91

Data Governance Unstructured Data Metadata Data Lake 91

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

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

Bad data tax is rampant in most organizations. Currently, every organization is blindly chasing the GenAI race, often forgetting that data quality and semantics is one of the fundamentals to achieving AI success. Sadly, data quality is losing to data quantity, resulting in “ Infobesity ”. “Any

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

Octopai

OCTOBER 26, 2022

DataOps is an approach to best practices for data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. The same goes for data analytics products. Let’s take a look.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

O'Reilly on Data

FEBRUARY 11, 2020

We suspected that data quality was a topic brimming with interest. The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. Key survey results: The C-suite is engaged with data quality. Data quality might get worse before it gets better.

Data Quality 304

Data Quality Metadata Data Governance Publishing 304

Ontotext

MAY 26, 2021

This happens through the process of semantic annotation , where documents are tagged with relevant concepts and enriched with metadata , i.e., references that link the content to concepts, described in a knowledge graph. Evaluation is for AI systems what quality assurance (QA) is for software systems.

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

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

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 289

Strategy Experimentation Uncertainty Machine Learning 289

DataKitchen

OCTOBER 20, 2023

Deploying a Data Journey Instance unique to each customer’s payload is vital to fill this gap. Such an instance answers the critical question of ‘Dude, Where is my data?’ ’ while maintaining operational efficiency and ensuring data quality—thus preserving customer satisfaction and the team’s credibility.

Insurance 176

Insurance Metadata Data-driven Data Quality 176

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

MAY 2, 2024

Every new dataset and new user adds a little more friction that hits the core metric of the velocity of data and brings it down to zero. Another challenge is the data models that represent how insights and analytics teams see the world. Here we talk about metadata management, catalog of catalogs, and so on.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

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. However, organizations need to be aware that these may be nothing more than bolted-on Band-Aids.

Strategy 52

Strategy Management Metadata Data-driven 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

IBM Big Data Hub

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

TDAN

DECEMBER 20, 2022

Over the past few months, my team in Castlebridge and I have been working with clients delivering training to business and IT teams on data management skills like data governance, data quality management, data modelling, and metadata management.

Metadata 52

Metadata Data Governance Data Quality Management 52

erwin

DECEMBER 3, 2020

The answer is radical transformation, made possible by an intelligent, data-driven approach to: New business models. One Customer’s Journey to Controlling Data Chaos. Being able to integrate all data touchpoints, including erwin DM for data modeling, Denodo for data visualization, and Jira for ticketing, has been key.

Metadata 141

Metadata Data Governance Data-driven Digital Transformation 141

Octopai

JUNE 19, 2023

These policies ensure the data catalog remains accurate, up-to-date, and secure. It involves defining data standards, access controls, and data quality measures. promoting coherence with other systems and data sources. promoting coherence with other systems and data sources.

Metadata 88

Metadata Data Governance Measurement Risk Management 88

Timo Elliott

JANUARY 6, 2022

The data you’ve collected and saved over the years isn’t free. If storage costs are escalating in a particular area, you may have found a good source of dark data. Analyze your metadata. If you’ve yet to implement data governance, this is another great reason to get moving quickly. Data analysis and exploration.

IT 133

IT Metadata Data-driven Data Governance 133

erwin

MAY 18, 2022

First, erwin Data Intelligence 12.0 delivers new integrated data quality capabilities for customers. Customers then use the erwin Data Quality platform to automate the data discovery, profiling and quality assessment of their data sources. Figure 1: erwin Data Intelligence 12.0

Data Quality 52

Data Quality Metadata Data Governance Software 52

Ontotext

NOVEMBER 16, 2023

Data mesh solves this by promoting data autonomy, allowing users to make decisions about domains without a centralized gatekeeper. It also improves development velocity with better data governance and access with improved data quality aligned with business needs. What Is a Data Product and Who Owns Them?

Metadata 52

Metadata Data-driven Data Quality Data Architecture 52

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. How erwin Can Help.

Data Governance 110

Data Governance Management Metadata Risk Management 110

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. Assess Data Quality.

Management 52

Management Data Quality Metadata Testing 52

IBM Big Data Hub

JULY 6, 2022

Businesses of all sizes, in all industries are facing a data quality problem. 73% of business executives are unhappy with data quality and 61% of organizations are unable to harness data to create a sustained competitive advantage 1. Data observability as part of a data fabric . Instead, Databand.ai

Metadata 57

Metadata Data Quality Snapshot Cost-Benefit 57

TDAN

SEPTEMBER 14, 2021

In this article, I describe a method of modelling data so that it meets business requirements. Central to this method is that modelling not only the required data, but also the subset of the real world that concerns the enterprise.

Modeling 52

Modeling Enterprise Data Quality IT 52

Alation

MARCH 14, 2023

All critical data elements (CDEs) should be collated and inventoried with relevant metadata, then classified into relevant categories and curated as we further define below. Store Where individual departments have their own databases for metadata management, data will be siloed, meaning it can’t be shared and used business-wide.

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

BI-Survey

JUNE 30, 2023

dScribe data is an innovative start-up from Belgium that offers a data catalog focusing on easy implementation, maintenance and usage. dScribe sees lack of user adoption as the main reason for the failure of data catalog projects. The connectors to SAP metadata are particularly important for the German-speaking market.

Key Performance Indicator 52

Key Performance Indicator Metadata Marketing Data Quality 52

IBM Big Data Hub

NOVEMBER 21, 2023

A schema describes the structure of data. Apache Kafka transfers data without validating the information in the messages. It does not have any visibility of what kind of data are being sent and received, or what data types it might contain. Kafka does not examine the metadata of your messages. What’s next?

Data Quality 104

Data Quality Metadata Data-driven Optimization 104

Ontotext

SEPTEMBER 15, 2020

The Data Fabric paradigm combines design principles and methodologies for building efficient, flexible and reliable data management ecosystems. Knowledge Graphs are the Warp and Weft of a Data Fabric. To implement any Data Fabric approach, it is essential to be able to understand the context of data.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

erwin

AUGUST 7, 2019

A data catalog benefits organizations in a myriad of ways. With the right data catalog tool, organizations can automate enterprise metadata management – including data cataloging, data mapping, data quality and code generation for faster time to value and greater accuracy for data movement and/or deployment projects.

Metadata 90

Metadata Data Governance Data Quality Data Warehouse 90

Ontotext

FEBRUARY 12, 2024

Domain teams should continually monitor for data errors with data validation checks and incorporate data lineage to track usage. Establish and enforce data governance by ensuring all data used is accurate, complete, and compliant with regulations.

Data Quality 69

Data Quality Data-driven Data Lake Data Governance 69

TDAN

MAY 20, 2020

Data modeling and database design is an in-demand career path, and database design is a crucial skill set that experienced DBAs must possess. In this article, we’re going to look at some common “beginner” questions, though written with the assumption you’re familiar with some aspects of data modeling and database design.

Modeling 64

Modeling Data Quality Metadata Management 64

erwin

MARCH 8, 2019

Following are two examples that illustrate the data governance stock check, including the Any 2 approach in action, based on real consulting engagements. Then the metadata is classified against a semantic model held in a business glossary. This, among other safeguards, ensures data quality.

Data Governance 65

Data Governance Metadata Data Warehouse Data Quality 65

erwin

JANUARY 30, 2020

The results of our new research show that organizations are still trying to master data governance, including adjusting their strategies to address changing priorities and overcoming challenges related to data discovery, preparation, quality and traceability. Most have only data governance operations.

Data Governance 145

Data Governance Metadata Digital Transformation Cost-Benefit 145

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