Data profiling: what it is and how it helps improve data quality

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Data profiling takes time and effort, but the results will be quality. See how.
In an increasingly interconnected and digital world, the relevance of data is unquestionable, but for it to also be functional to our business, we must pay special attention to its quality.

And if we are going to work on projects that promote data quality, it is important to give relevance to data profiling, one of the most critical steps in the process. Data profiling is a process that consists of reviewing the source of the data, understanding its structure, content and relationships and thus identifying the potential it has for different business projects.

What are the best practices that can be applied to ensure data profiling is more successful?


Blank or zero-value percentages
It is important to analyze each column. We will need to do this in homeowner database there is any type of data loss (blanks) or unknown information (zero values) that may cause later interpretation problems. By detecting these, architects can configure more accurate predefined values, allowing for exceptions in specific cases that help make daily maintenance more satisfactory.

Analyze unique values
The next step is to carry out a specific analysis of the different values ​​that we can find in each of the columns. By doing this on the original data, we will be identifying the key factors of the database and saving time and effort later on.

In the best cases, these unique values ​​are highlighted in the file itself by the column names or supporting information you've provided. In other cases, you have to put in some effort and identify the key factors.



Data quality as an essential part of MDM


Numeric and date range analysis
Working with numeric and date ranges with maximum and minimum values ​​will help us balance performance, thanks to the fact that we will know the different types of existing data, limiting the margin of error. Having this information at hand will prevent unwanted situations and possible problems that may appear overnight. In the past, the problem of converting dates from Oracle to SQL Server was very common. Until a definitive solution was given, the initial limit dates were set to January 1, 1753, which led to failures in Oracle systems. Managing numeric and date ranges well will ensure that these possible problems do not occur.