Data Processing Data Quality Metadata Risk 
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.
Alation
MAY 19, 2022
Gartner Data & Analytics Summit 2022: Keynote Highlights. A Gartner survey found that 57% of Boards of Directors have increased their risk appetites, and data & analytics are fueling more risky (and potentially rewarding) projects. Leaders agree: Data needs to drive business results. Establish what data you have.
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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. erwin Data Intelligence. Click here to read our success story on how E.ON
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.
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.
O'Reilly on Data
MARCH 31, 2020
But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. You might have millions of short videos , with user ratings and limited metadata about the creators or content.
IBM Big Data Hub
JANUARY 9, 2024
Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. Data ingestion must be done properly from the start, as mishandling it can lead to a host of new issues.
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