课程: How to Build and Execute a Successful Data Strategy

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Defining data storage, processing, and integration requirements

Defining data storage, processing, and integration requirements

课程: How to Build and Execute a Successful Data Strategy

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Defining data storage, processing, and integration requirements

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- Many organizations do not pay enough attention to data quality. Ultimately, this is the main downfall of the majority of strategies, but don't worry, we'll avoid that fate for you. You need to make sure that your data is accurate, complete, and reliable, and that starts with measurement and assessment. Here's my go-to strategy to ensure you are successful. Let's start by looking at data quality measurement, DQM. DQM involves evaluating the quality of data against a set of predefined criteria or metrics. It helps organizations understand the level of data quality they currently have and identify areas for improvement. I want to highlight some key steps you should involve in data quality measurement. Define data quality metrics. Before measuring data quality, you will need to define specific metrics that align with your business requirements. These metrics can vary depending on the type of data, industry, and organizational goals. Some common data quality metrics include accuracy…

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