Final answer:
Mo can use tools like Excel's data cleaning functions, data cleaning software, and programming languages with data cleaning libraries to clean up his source data. Setting data validation rules can also help prevent irregular formatting in the future.
Step-by-step explanation:
To clean up his source data, Mo can use various tools and techniques. One option is to use Excel's data cleaning functions, such as removing duplicates, converting text to numbers or dates, and correcting formatting errors. Mo can also use data cleaning software, like OpenRefine, which provides advanced features for detecting and correcting irregularities in data.
Another approach that Mo can take is to write scripts or use programming languages like Python or R to automate the data cleaning process. These languages have libraries and modules specifically designed for data cleaning, such as Pandas in Python or dplyr in R.
Mo should also consider creating data validation rules or using data validation tools within his data management software. This can help prevent irregular formatting in the future by setting limits, conditions, or restrictions on the data as it is entered or imported.