Data Governance 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.
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. Data Governance Is Business Transformation. Predictability.
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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. Data Governance.
Octopai
JANUARY 31, 2021
Within each episode, there are actionable insights that data teams can apply in their everyday tasks or projects. The host is Tobias Macey, an engineer with many years of experience. Agile Data. Agile Data. Another podcast we think is worth a listen is Agile Data. TDWI – Philip Russom. Malcolm Chisholm.
Alation
NOVEMBER 28, 2022
It is also hard to know whether one can trust the data within a spreadsheet. And they rarely, if ever, host the most current data available. Sathish Raju, cofounder & CTO, Kloudio and senior director of engineering, Alation: This presents challenges for both business users and data teams.
AWS Big Data
MAY 7, 2024
Data governance is a key enabler for teams adopting a data-driven culture and operational model to drive innovation with data. Amazon DataZone allows you to simply and securely govern end-to-end data assets stored in your Amazon Redshift data warehouses or data lakes cataloged with the AWS Glue data catalog.
Domino Data Lab
OCTOBER 1, 2020
Combining the power of Domino Data Labs with Okera, your data scientists only get access to the columns, rows, and cells allowed, easily removing or redacting sensitive data such as PII and PHI not relevant to training models. For the compliance team, the combination of Okera and Domino Data Lab is extremely powerful.
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