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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

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.

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A summary of Gartner’s recent DataOps-driven data engineering best practices article

DataKitchen

On 24 January 2023, Gartner released the article “ 5 Ways to Enhance Your Data Engineering Practices.” Data team morale is consistent with DataKitchen’s own research. We surveyed 600 data engineers , including 100 managers, to understand how they are faring and feeling about the work that they are doing.

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Set up advanced rules to validate quality of multiple datasets with AWS Glue Data Quality

AWS Big Data

Data is the lifeblood of modern businesses. In today’s data-driven world, companies rely on data to make informed decisions, gain a competitive edge, and provide exceptional customer experiences. However, not all data is created equal. AWS Glue Data Quality measures and monitors the quality of your dataset.

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How to choose the best DSPM solution for your organization: comparison of features, benefits, and pricing models of different DSPM vendors

Laminar Security

Cloud data security is one of the most critical, yet most challenging aspects of doing business in the age of cloud. As more organizations migrate their data to the cloud, they face an increasing range of risks and threats, including data breaches, data leakage, data loss, data misuse, data compliance violations, shadow data and more.

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Optimizing Risk and Exposure Management – Roundtable Highlights

Cloudera

We recently hosted a roundtable focused on o ptimizing risk and exposure management with data insights. Across the industry, the pandemic caused a huge breakdown in model performance due to the change in macroeconomic conditions and government stimulus packages. Mental health of employees is a critical area to monitor .

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Data Governance Maturity and Tracking Progress

erwin

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. Maturity Levels.

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The Art and Science of FP&A Storytelling

Timo Elliott

I recently participated in a web seminar on the Art and Science of FP&A Storytelling, hosted by the founder and CEO of FP&A Research Larysa Melnychuk along with other guests Pasquale della Puca , part of the global finance team at Beckman Coulter and Angelica Ancira , Global Digital Planning Lead at PepsiCo. What typically goes wrong?