The importance of data for an efficient decision-making process cannot be overstated. Organisations utilise data for this purpose by gathering data from past events, understanding it, forming some conclusions, and using that knowledge to take actions to improve the future. I.e. learning from the past to improve the future. However, the bottleneck comes when the data gathered are not in the correct format. So when is data dirty and when is it bad?
What is dirty data?
Any data is dirty when it suffers quality issues such as duplicate data, poor alignment and it is bad when it suffers integrity issues for example Missing, Incomplete and Inaccurate data
As a data analyst, you must know how to clean your data before performing your analysis. You must ensure not to do this manually or change the data to something else.
Data cleaning entails changing inaccurate data, merging split data, replacing missing values with either the mean or mode value.
Cleaning dirty data with the power query tool
You can automate the data cleaning process by using Excel or Python.
To effectively clean your data using Excel, you must install a tool called power query. Power Query is a tool built by Microsoft to make working with data easier. It helps to connect external data sources, querying, transforming, cleaning, and parsing data. It is available as an add-in for Excel 2010 professional plus or 2013 and comes already built-in for Excel 2016. Unfortunately, It is not available for earlier versions of excel and Mac users
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