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Data Errors in Financial Services: Addressing the Real Cost of Poor Data Quality

TDAN

Data quality issues continue to plague financial services organizations, resulting in costly fines, operational inefficiencies, and damage to reputations. Key Examples of Data Quality Failures — […]

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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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DataKitchen’s Data Quality TestGen found 18 potential data quality issues in a few minutes (including install time)!

DataKitchen

DataKitchen’s Data Quality TestGen found 18 potential data quality issues in a few minutes (including install time) on data.boston.gov building permit data! Imagine a free tool that you can point at any dataset and find actionable data quality issues immediately! first appeared on DataKitchen.

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Unit Test framework and Test Driven Development (TDD) in Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Overview Running data projects takes a lot of time. Poor data results in poor judgments. Running unit tests in data science and data engineering projects assures data quality. You know your code does what you want it to do.

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What I Learned from Executing Data Quality Projects

TDAN

Getting to great data quality need not be a blood sport! This article aims to provide some practical insights gained from enterprise master data quality projects undertaken within the past […].

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Navigating the Storm: How Data Engineering Teams Can Overcome a Data Quality Crisis

DataKitchen

Navigating the Storm: How Data Engineering Teams Can Overcome a Data Quality Crisis Ah, the data quality crisis. It’s that moment when your carefully crafted data pipelines start spewing out numbers that make as much sense as a cat trying to bark. You’ve got yourself a recipe for data disaster.

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Knowledge Enhanced Machine Learning: Techniques & Types

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In machine learning, the data is an essential part of the training of machine learning algorithms. The amount of data and the data quality highly affect the results from the machine learning algorithms.