It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time analyzing it. The time spent cleaning is vital since analyzing dirty data can lead you to draw inaccurate conclusions and misleading results.
As Without making sure that data is properly cleaned in the exploration and processing phase, we will surely compromise the insights and reports subsequently generated, and the results of any data analysis or machine learning model could be inaccurate.
As the old says "garbage in garbage out".
For more details about problems and challenges that could be found in the tabular form, read this blog. https://medium.com/@Mustafa77/data-cleaning-63bf9da94b00