Alation

MAY 16, 2023

Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible. Data Quality When using a data pipeline, data consistency, quality, and reliability are often greatly improved.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

BizAcuity

MARCH 29, 2019

Load data into staging, perform data quality checks, clean and enrich it, steward it, and run reports on it completing the full management cycle. Numbers are only good if the data quality is good. To get an in-depth knowledge of the practices mentioned above please refer to the blog on Oracle’s webpage.

Data Lake 102

Data Lake Unstructured Data Cost-Benefit Data Quality 102

Ontotext

JULY 29, 2021

The two distinct threads interlacing in the current Semantic Web fabrics are the semantically annotated web pages with schema.org (structured data on top of the existing Web) and the Web of Data existing as Linked Open Data. Below, we outline the two directions in which we at Ontotext see and build the Semantic Web.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

AWS Big Data

JULY 12, 2023

A comprehensive testing framework ensures that your models consistently deliver accurate and reliable data, while modularity enables faster development via component reusability. Combined, these features can improve your data team’s velocity, ensure higher data quality, and empower team members to assume ownership.

Data Warehouse 52

Data Warehouse Modeling Dashboards Data Lake 52

Alation

MAY 16, 2023

Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible. Data Quality When using a data pipeline, data consistency, quality, and reliability are often greatly improved.

Data Lake 40

Data Lake Data Governance Data Warehouse Data Processing 40

Jet Global

MAY 9, 2024

ETL is a specific type of data pipeline that focuses on the process of extracting data from sources, transforming it, and loading it into a destination, such as a data warehouse or data lake. ETL is primarily used for data warehousing and business intelligence applications. How is ELT different from ETL?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

O'Reilly on Data

DECEMBER 19, 2018

Anomaly detection is well-known in the financial industry, where it’s frequently used to detect fraudulent transactions, but it can also be used to catch and fix data quality issues automatically. The history of data analysis has been plagued with a cavalier attitude toward data sources.

Machine Learning 258

Machine Learning Software Metadata Testing 258

Ontotext

JANUARY 10, 2024

This will allow enterprises to derive insights from their disparate databases, integrate external knowledge for better interpretation of their data, and incorporate information extracted from unstructured content. In order to integrate structured data, enterprises need to implement the data fabric pattern.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

Ontotext

JANUARY 6, 2021

A knowledge graph can be used as a database because it structures data that can be queried such as through a query language like SPARQL. Reuse of knowledge from third party data providers and establishing data quality principles to populate it. The connections made through these descriptions create context.

Metadata 59

Metadata Unstructured Data Cost-Benefit Enterprise 59

Alation

OCTOBER 28, 2021

Protect data at the source. Put data into action to optimize the patient experience and adapt to changing business models. What is Data Governance in Healthcare? Data governance in healthcare refers to how data is collected and used by hospitals, pharmaceutical companies, and other healthcare organizations and service providers.

Data Governance 52

Data Governance Measurement Metrics Modeling 52

Ontotext

JULY 6, 2023

This shift of both a technical and an outcome mindset allows them to establish a centralized metadata hub for their data assets and effortlessly access information from diverse systems that previously had limited interaction. There are four groups of data that are naturally siloed: Structured data (e.g.,

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

Peter James Thomas

AUGUST 29, 2018

I have since run and driven transformation in Reference Data, Master Data , KYC [3] , Customer Data, Data Warehousing and more recently Data Lakes and Analytics , constantly building experience and capability in the Data Governance , Quality and data services domains, both inside banks, as a consultant and as a vendor.

Data-driven 61

Data-driven Cost-Benefit Metadata Technology 61