Data Quality Data Transformation Enterprise 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
In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.
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CIO Business Intelligence
MARCH 4, 2024
Despite these benefits, the core problems that data centralization so often fails to address are the pragmatic realities of many enterprise data ecosystems. Additionally, data silos and fragmentation often occur inorganically as in the case of merger or acquisition scenarios.
erwin
APRIL 29, 2020
Many large organizations, in their desire to modernize with technology, have acquired several different systems with various data entry points and transformation rules for data as it moves into and across the organization. Who are the data owners? What are the transformation rules? Data Governance.
erwin
JANUARY 11, 2019
Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. So questions linger about whether transformed data can be trusted.
erwin
SEPTEMBER 17, 2020
It’s paramount that organizations understand the benefits of automating end-to-end data lineage. Critically, it makes it easier to get a clear view of how information is created and flows into, across and outside an enterprise. The importance of end-to-end data lineage is widely understood and ignoring it is risky business.
Alation
MARCH 22, 2022
As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT. Simply put, IDF standardizes data engineering processes. DataOps has emerged as an exciting solution. Transparency is key.
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