Importance of Good Data Quality | English Premier League Example | Quantra Course
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Welcome to this video lesson on data quality. After completing this video, you will be able to explain the importance of good quality data and list the factors that cause data quality issues.
After collecting the data, most people start the analysis on it. Often, they forget to do a sanity check on the data. If the data is of bad quality, it can give misleading information.
For example, if you start the analysis without ensuring data quality then you might get unexpected results such as the Crystal Palace club will win the next EPL. However, your domain knowledge on EPL says that the result looks inaccurate as Crystal Palace has never even finished in the top 4.
A surprising fact is that a professional data scientist spends approximately 60% of his time ensuring that data is of high quality. But why does the data quality issue occur?
It can occur during data collection or data integration. Consider that you are recording the average time that employees spend in a cafeteria weekly across companies. You recorded 100 hours instead of 10 hours, or the unit of measurement recorded incorrectly. Also, if you are interviewing and someone may choose not to respond to certain questions which leads to missing values.
Another cause is when data is collected from different sources and merged. For example, you require the weight of all players of EPL in a single file. You extract the player's weight data from source X. But the weight data of some new players is not available, so you get it from source Y.
The unit of weight measurement in source X is in pounds, and source y is in kilograms. If data collected from both sources are combined as it is, then there will be inconsistency in the data, and it will result in inaccurate data.
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