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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 Blog

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. The data quality analysis metrics of complete and accurate data are imperative to this step.

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Handling Outliers Using Smarten Assisted Predictive Modelling!

Elegant BI

Outliers, also referred to as anomaly, exception, irregularity, deviation, oddity, arise in data analysis when the data records differ dramatically from the other observations. In layman’s terms, an outlier can be interpreted as any value that is numerically far-flung from most of the data points in a sample of data.

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Top 10 Analytics And Business Intelligence Trends For 2020

Datapine Blog

Accordingly, the rise of master data management is becoming a key priority in the business intelligence strategy of a company. Today, most companies understand the impact of data quality on analysis and further decision-making processes and hence choose to implement a data quality management (DQM) policy, department, or techniques.