3.4. Verification data quality¶

Data quality is essential to ensure project success.

After analyzing the data to verify data quality, it is important to answer some questions such as:

Is there missing attributes and blank fields?
Does it cover all the cases required?
Are the collected values possible to occur or are they outliers?
How often do errors and missing values in the data occur?
Are there attributes with different values that have similar meanings?
Does the acquired data satisfy the relevant requirements?
Does it necessary to a collect different sets of data?

It is important to provide a data quality report containing:

list the results of the data quality verification
consider both data and business knowledge to solve data quality issues
list possible solutions when occurs quality problem

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3.3. Exploration the data

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4. Data Preparation