The correct answer is False. Raw data in the real world is often messy and unstructured. It may be incomplete, inaccurate, or duplicated. It may also be stored in a variety of formats, making it difficult to analyze.
There are a number of reasons why raw data is often messy. One reason is that it is often collected from a variety of sources, each with its own unique format. Another reason is that data may be collected over time, and changes in the way data is collected can make it difficult to compare data from different time periods. Finally, data may be collected by different people, each with their own unique way of recording data.
The messiness of raw data can make it difficult to analyze. Data that is incomplete or inaccurate can lead to incorrect conclusions. Data that is duplicated can make it difficult to identify trends. And data that is stored in a variety of formats can make it difficult to combine data from different sources.
There are a number of things that can be done to clean and organize raw data. One common approach is to use a data ETL (extract, transform, load) tool. Data ETL tools can be used to extract data from a variety of sources, transform it into a consistent format, and load it into a data warehouse or data lake.
Another common approach is to use a data quality tool. Data quality tools can be used to identify and correct errors in data. They can also be used to identify duplicate data and to standardize data formats.
Once data has been cleaned and organized, it can be used for a variety of purposes, such as data analysis, data mining, and machine learning.