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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. Integrate DQM and BI : Integration is one of the buzzwords when we talk about dataanalysis in a business context.
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. According to Gartner, poor data quality is estimated to cost organizations an average of $15 million per year in losses.
Outliers, also referred to as anomaly, exception, irregularity, deviation, oddity, arise in dataanalysis 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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