Big Data & Analytics


Carlos Barge

Regardless of the type of analysis or data visualizations you need, data cleaning is a vital step to ensure that the answers you generate are accurate. When collecting data from several streams and with manual input from users, information can carry mistakes, be incorrectly inputted, or have gaps.

Data cleaning helps ensure that information always matches the correct fields while making it easier for business intelligence tools to interact with data sets to find information more efficiently. One of the most common data cleaning examples is its application in data warehouses.

Cleaning data from multiple sources to transform it into a format that data analysts or data scientists can work with is a cumbersome process because – as the number of data sources increases, the time take to clean the data increases exponentially due to the number of sources and the volume of data generated in these sources. It might take up to 80% of the time for just cleaning data making it a critical part of analysis task.

A successful data warehouse stores a variety of data from disparate sources and optimizes it for analysis before any modeling is done. To do so, warehouse applications must parse through millions of incoming data points to make sure they’re accurate before they can be slotted into the right database, table, or other structure.

Organizations that collect data directly from consumers filling in surveys, questionnaires, and forms also use data cleaning extensively. In their cases, this includes checking that data was entered in the correct field, that it doesn’t feature invalid characters, and that there are no gaps in the information provided.

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