课程: How to Build and Execute a Successful Data Strategy
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Evaluating data infrastructure options
课程: How to Build and Execute a Successful Data Strategy
Evaluating data infrastructure options
- The most common data quality issues include duplicate data, hidden data, inconsistent data, inaccurate data, missing values, inconsistent values, duplicate values, outlier values, and incomplete values. These issues can significantly impact decision-making processes and the overall reliability of data-driven insights. I have found that it is important to understand and proactively manage these common data quality issues. To do this, you'll want to adopt the appropriate tools and practices. Let's go through some of these common data issues in more detail, starting with duplicate data. Duplicate data occurs when the same information is stored in multiple locations within a database or between multiple databases. This can result in inconsistencies and inaccuracies when updates are made to one instance of the data, but not to others. Incomplete data. Incomplete data refers to missing or insufficient information within a dataset. It can occur when users fail to enter all required fields…
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Data architectures: Data lakes, data warehouses, etc.1 分钟 29 秒
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Evaluating data infrastructure options5 分钟 34 秒
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Defining data storage, processing, and integration requirements5 分钟 10 秒
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Establish data governance policies and procedures for data architecture and infrastructure5 分钟 8 秒
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