Cost-Benefit Data Processing Data Quality Metadata 
datapine
SEPTEMBER 29, 2022
1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.
AWS Big Data
DECEMBER 13, 2023
Offering this service reduced BMS’s operational maintenance and cost, and offered flexibility to business users to perform ETL jobs with ease. For the past 5 years, BMS has used a custom framework called Enterprise Data Lake Services (EDLS) to create ETL jobs for business users.
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erwin
APRIL 16, 2021
Data governance is best defined as the strategic, ongoing and collaborative processes involved in managing data’s access, availability, usability, quality and security in line with established internal policies and relevant data regulations. Enhanced : Data managed equally. erwin Data Intelligence.
Alation
MAY 19, 2022
Establish what data you have. Active metadata gives you crucial context around what data you have and how to use it wisely. Active metadata provides the who, what, where, and when of a given asset, showing you where it flows through your pipeline, how that data is used, and who uses it most often.
Ontotext
DECEMBER 1, 2023
Atanas Kiryakov presenting at KGF 2023 about Where Shall and Enterprise Start their Knowledge Graph Journey Only data integration through semantic metadata can drive business efficiency as “it’s the glue that turns knowledge graphs into hubs of metadata and content”.
AWS Big Data
NOVEMBER 16, 2023
In the following sections, we discuss the most common areas of consideration that are critical for Data Vault implementations at scale: data protection, performance and elasticity, analytical functionality, cost and resource management, availability, and scalability.
Domino Data Lab
OCTOBER 1, 2020
Traditionally, this problem has been solved by either denying access to this data altogether (a not infrequent outcome), or creating and maintaining multiple copies of many datasets for each possible use case by omitting the data that a particular user is not allowed to see (e.g. PII, PHI, etc).
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