Alation

FEBRUARY 7, 2023

The product collected an impressive amount of metadata, from the user interface to the database structure. It then translated all that metadata into an image resembling a spider’s web. Back then, visualizing impact analysis seemed futuristic with great promise. That was my earliest taste of data lineage.

Metadata 52

Metadata Data Warehouse Visualization Data Quality 52

Ontotext

MAY 2, 2024

These models are as important to companies as their frontline products and determine how data is managed, consumed, combined, joined, and analyzed. Data-centric approach In the data-centric approach, metadata serves as a layer of interoperability between the data sources.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

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

Octopai

JUNE 24, 2019

Document classification and lifecycle management will help you deal with oversight of unstructured data. – Data management : As part of maintaining the integrity of your data, it will be necessary to track activities. This maintains a high priority in your data governance strategy.

Data Governance 42

Data Governance Metadata Unstructured Data Software 42

AWS Big Data

MARCH 7, 2023

Sources Data can be loaded from multiple sources, such as systems of record, data generated from applications, operational data stores, enterprise-wide reference data and metadata, data from vendors and partners, machine-generated data, social sources, and web sources.

Analytics 72

Analytics Data Warehouse Data Lake Metadata 72

Ontotext

JUNE 22, 2023

On top of that, data is sometimes unreliable , and inaccurate or missing metadata makes it hard to decide which information to trust. It can also be easily maintained and upgraded, which lowers the costs of adding new data and maintaining data updates.

Metrics 52

Metrics Statistics Visualization Metadata 52

Alation

JUNE 2, 2022

Data management is not yet a solved problem, but modern data management is leagues ahead of prior approaches. These include tracking, documenting, monitoring, versioning, and controlling access to AI/ML models. How Data Lineage Is a Use Case in ML. Data lineage allows companies to troubleshoot errors in data processes.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

Ontotext

MARCH 8, 2023

Solution Outcomes: A semantic recommendation service that is beating accuracy benchmarks and replacing manual processes aggregating content – that is supporting higher-quality, more advanced, and targeted recommendations with clear reasons. million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

AWS Big Data

NOVEMBER 16, 2023

Through Amazon Redshift in-memory result set caching and compilation caching, workloads ranging from dashboarding to visualization to business intelligence (BI) that run repeat queries experience a significant performance boost. Chargeback metadata Amazon Redshift provides different pricing models to cater to different customer needs.

Enterprise 86

Enterprise Data Warehouse Snapshot Cost-Benefit 86

Smart Data Collective

SEPTEMBER 26, 2021

Everyone has to manage raw data at one point or another; yet, not everybody stores it in a way that’s useful for further analysis or comparison to other data sets. As data sets become bigger, it becomes harder to visualize information. Draw a chart highlighting each endpoint in your data. Speaking of which.

Metadata 98

Metadata Visualization Unstructured Data Data mining 98

Alation

JULY 19, 2022

Get the data. Explore the data. Model the data. Communicate and visualize the results. A data catalog can assist directly with every step, but model development. How Data Catalogs Help Data Scientists Ask Better Questions. Modern data catalogs also facilitate data quality checks.

Metadata 59

Metadata Data Quality Statistics Data Science 59

erwin

OCTOBER 29, 2020

Earlier this year, erwin conducted a research project in partnership with Dataversity, the 2020 State of Data Governance and Automation. We asked participants to “talk to us about data value chain bottlenecks.” Data quality and accuracy are recurring themes as well.

Data Governance 105

Data Governance Metadata ROI Digital Transformation 105

erwin

MAY 27, 2020

And it exists across these hybrid architectures in different formats: big and unstructured and traditional structured business data may physically sit in different places. What’s desperately needed is a way to understand the relationships and interconnections between so many entities in data sets in detail. Nine Steps to Data Modeling.

Modeling 98

Modeling Metadata Data Governance Visualization 98

IBM Big Data Hub

OCTOBER 12, 2023

Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. Metadata includes details specific to an AI model such as: The AI model’s creation (when it was created, who created it, etc.)

Cost-Benefit 82

Cost-Benefit Metadata Data Governance Modeling 82

erwin

MARCH 21, 2019

Data governance also relies on business process modeling and analysis to drive improvement , including identifying business practices susceptible to security, compliance or other risks and adding controls to mitigate exposures. Organizations also can use a BPM tool to identify the staff who function as “unofficial data repositories.”

Modeling 75

Modeling Metadata IT Data Governance 75

Alation

SEPTEMBER 23, 2021

What Is a Data Catalog? A data catalog is a centralized storage bank of metadata on information sources from across the enterprise, such as: Datasets. Visualizations. The data catalog also stores metadata (data about data, like a conversation), which gives users context on how to use each asset.

Enterprise 52

Enterprise Metadata Data Governance Visualization 52

erwin

JANUARY 3, 2020

As organizations become data-driven and awash in an overwhelming amount of data from multiple data sources (AI, IoT, ML, etc.), they will find new ways to get a handle on data quality and focus on data management processes and best practices.

Data Governance 139

Data Governance Digital Transformation IoT Metadata 139

DataKitchen

AUGUST 8, 2023

On 20 July 2023, Gartner released the article “ Innovation Insight: Data Observability Enables Proactive Data Quality ” by Melody Chien. It alerts data and analytics leaders to issues with their data before they multiply. It alerts data and analytics leaders to issues with their data before they multiply.

Data Quality 130

Data Quality Testing Snapshot Reporting 130

erwin

JANUARY 3, 2020

As organizations become data-driven and awash in an overwhelming amount of data from multiple data sources (AI, IoT, ML, etc.), they will find new ways to get a handle on data quality and focus on data management processes and best practices.

Data Governance 52

Data Governance Digital Transformation IoT Metadata 52

Rocket-Powered Data Science

MARCH 20, 2023

Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. Source: [link] SAP also announced key partners that further enhance Datasphere as a powerful business data fabric.

Data Warehouse 325

Data Warehouse Metadata Digital Transformation Unstructured Data 325

Alation

FEBRUARY 17, 2023

Reichental describes data governance as the overarching layer that empowers people to manage data well ; as such, it is focused on roles & responsibilities, policies, definitions, metrics, and the lifecycle of the data. In this way, data governance is the business or process side. Seeing is believing!

Data Governance 52

Data Governance Data Quality Metadata Cost-Benefit 52

erwin

AUGUST 1, 2023

Intro erwin ® Data Modeler 12.5 Because of the ability to provide standardization and foundational stability through all these processes, data modeling is a crucial step to success, and erwin ® Data Modeler by Quest ® is celebrating its 30 th birthday by releasing its newest, most collaborative version to date.

Modeling 52

Modeling Metadata Data Quality Data Governance 52

DataKitchen

JANUARY 23, 2023

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements.

Testing 130

Testing Data Governance Data Quality Data-driven 130

Jet Global

FEBRUARY 23, 2024

The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Jet Global

MARCH 14, 2024

Jet’s interface lets you handle data administration easily, without advanced coding skills. You don’t need technical skills to manage complex data workflows in the Fabric environment. Data Lineage and Documentation Jet Analytics simplifies the process of documenting data assets and tracking data lineage in Fabric.

Analytics 52

Analytics Management Reporting Enterprise 52