Data Cleaning and Analysis using Excel

I know some hate Mathematics😋  but anyone can do data cleaning if they pay attention to a few instructions. It is not rocket science. 

You have probably come across misspelled words, stubborn trailing spaces, unwanted prefixes, improper cases, and nonprinting characters.It is really disturbing if you intend to carry out a research study, analysis or draft a report. 

Data cleaning is important because it improves your data quality and in doing so, increases overall productivity. When you clean your data, all outdated or incorrect information is gone – leaving you with the highest quality information. This ensures your team do not have to wade through countless outdated documents and allows employees to make the most of their work hours. 

Ensuring you have correct information also helps reduce some unexpected costs. For example, you may print incorrect information onto company letterheads – and realise it must all go to waste once that error is found! Having consistent errors in your work can also harm your organisations credibilty. 

When cleaning data, the first thing to remember is you don’t always have control over the format and type of data that you import from an external data source, such as a database, text file, or a web page. Before you can analyse the data, you often need to clean it up. Fortunately, Excel has many features to help you get data in the precise format that you want. 

Sometimes, the task is straightforward and there is a specific feature that does the job for you. For example, you can easily use Spell Checker to clean up misspelled words in columns that contain comments or descriptions. Or, if you want to remove duplicate rows, you can quickly do this by using the Remove Duplicates dialog box. 

At other times, you may need to manipulate one or more columns by using a formula to convert the imported values into new values. For example, if you want to remove trailing spaces, you can create a new column to clean the data by using a formula, filling down the new column, converting that new column’s formulas to values, and then removing the original column. 

It’s a practical process and practice is necessary to build up the skill. However, with a little patience, the process can be mastered. Learn more about the cleaning steps via this link. 

Among the other benefits of cleaning data are improved decision making, reducing costs and waste. 

With an up-to-date data list, you can ensure you are contacting people that have a genuine interest in your message. This greatly reduces the likelihood of your mailing being thrown away before it is read. 

Cleansing also helps by removing incorrect details that may affect mailing accuracy. This includes details on people that have changed their work/home address or died. 

So, get your data in order by cleaning it regularly to achieve better results. 

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