Clear data is crucial for marketing and analytics. Clean data helps ensure your communication reach the right people. And as GDPR comes into effect, a business that does not maintain a clean dataset will soon face heavy fines. Lastly, clean data allows for better decision-making and better customer understanding.
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Data can be cleaned by correcting errors and corruptions or manually processing the data to avoid them from happening again. While most aspects of data cleansing can be performed using software, some must still be completed manually.
SQL is an essential skill for data analysts, but it is not always used in the context of data pipelines. SQL is still a useful pre-processing tool that can be used to accomplish many tasks such as data cleansing and wrangling
Experian estimates that human error is responsible for more than 60% of all dirty data. Poor interdepartmental communication accounts for about 35% inaccurate records.