erwin
APRIL 29, 2020
Business terms and data policies should be implemented through standardized and documented business rules. Compliance with these business rules can be tracked through data lineage, incorporating auditability and validation controls across data transformations and pipelines to generate alerts when there are non-compliant data instances.
erwin
SEPTEMBER 17, 2020
Here are six benefits of automating end-to-end data lineage: Reduced Errors and Operational Costs. Data quality is crucial to every organization. Automated data capture can significantly reduce errors when compared to manual entry. Request your own demo of erwin DI to see metadata-driven, automated data lineage in action.
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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.
Ontotext
NOVEMBER 18, 2021
They’ve read some of the many available resources on the topic and seen Ontotext’s excellent product demos. OntoRefine is a data transformation tool that lets you unite plenty of data formats and get them into your triplestore. Ensuring data quality with SHACL. But would the surveyors write SPARQL?
Alation
DECEMBER 7, 2021
Alation and Bigeye have partnered to bring data observability and data quality monitoring into the data catalog. Read to learn how our newly combined capabilities put more trustworthy, quality data into the hands of those who are best equipped to leverage it. trillion each year due to poor data quality.
erwin
MARCH 4, 2021
In addition to drivers like digital transformation and compliance, it’s really important to look at the effect of poor data on enterprise efficiency/productivity. You can request a demo of erwin Data Intelligence here. Webinar: The Value of Data Governance & How to Quantify It. Join us March 15 at 10 a.m.
Alation
AUGUST 11, 2022
But there are only so many data engineers available in the market today; there’s a big skills shortage. So to get away from that lack of data engineers, what data mesh says is, ‘Take those business logic data transformation capabilities and move that to the domains.’ Let’s take data privacy as an example.
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