Data Governance Data Processing Definition Metrics 
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. 10) Data Quality Solutions: Key Attributes.
Alation
JANUARY 20, 2022
In Ryan’s “9-Step Process for Better Data Quality” he discussed the processes for generating data that business leaders consider trustworthy. To be clear, data quality is one of several types of data governance as defined by Gartner and the Data Governance Institute. Step 2: Data Definitions.
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Alation
FEBRUARY 17, 2023
This past week, I had the pleasure of hosting Data Governance for Dummies author Jonathan Reichental for a fireside chat , along with Denise Swanson , Data Governance lead at Alation. Can you have proper data management without establishing a formal data governance program?
Alation
NOVEMBER 23, 2021
The same could be said about data governance : ask ten experts to define the term, and you’ll get eleven definitions and perhaps twelve frameworks. However it’s defined, data governance is among the hottest topics in data management. This is the final post in a four-part series discussing data culture.
Alation
MAY 31, 2023
The Data Governance & Information Quality Conference (DGIQ) is happening soon — and we’ll be onsite in San Diego from June 5-9. If you’re not familiar with DGIQ, it’s the world’s most comprehensive event dedicated to, you guessed it, data governance and information quality. The best part?
Alation
JULY 6, 2021
Common Data Governance Challenges. Every enterprise runs into data governance challenges eventually. Issues like data visibility, quality, and security are common and complex. Data governance is often introduced as a potential solution. And one enterprise alone can generate a world of data.
DataKitchen
APRIL 13, 2021
Because it is such a new category, both overly narrow and overly broad definitions of DataOps abound. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. .
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