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What is data cleaning and benefits of data cleaning?


Data cleaning is the first and most important step, as it ensures the quality of the data is met to prepare data for visualization. This process involves preparing and validating data, usually takes place before your analysis. Data cleaning is not just removing unwanted data.


Data cleaning is the process of ensuring that your data is correct and useable by identifying any errors in the data, or missing data by correcting or deleting them. If you don't clean the data it will impact the results of your analysis.


The main tasks you’ll have to carry out when cleaning data include:


Getting rid of unwanted observations: Removing observations that aren’t relevant to the problem you’re trying to solve.

Unifying the data structure: You’ll need to ensure data from different sources is consistent by mapping it to a unified underlying structure.

Standardizing your data: This involves things like ensuring the numerical observations in your dataset use the same unit of measurement.

Removing unwanted outliers: Outliers can be useful, but if they’re erroneous they’ll skew the results of your analysis. You’ll need to make a judgment call about which outliers to keep and which to remove.

Fixing cross-set data errors: Data rarely comes from a single source; ensuring that different data sources don’t contradict each other is vital.

Resolving type conversion and syntax errors: This involves things like removing whitespace, checking for spelling mistakes, or simply ensuring data is categorized correctly. For example, are number fields properly labeled as numerical data?

Handle missing data: If there are gaps in your data, what effect will this have? You might choose to remove associated entries, guess missing values, or simply flag them so you can measure their impact later on.

Validating your data: This is the final step of the process. It usually involves executing scripts that check if you’ve carried out all the other steps of the process correctly. You’ll often have to go back and repeat some of the earlier steps.


Benefits of Data Cleaning: There are many benefits of data cleaning. Some of the benefits are :

  1. Effective decision making

  2. Clean data support accurate and better analytics

  3. Save precious time and money

  4. Reduce waste

  5. Increase productivity

  6. Minimal risks

  7. Better marketing and sales efforts

  8. Growth in revenue

Few Data cleaning tools are:

1.OpenRefine

2.Trifacta Wrangler

3.WinPure Clean & Match

4.TIBCO Clarity

5.Melissa Clean Suite

6.IBM Infosphere Quality Stage

7.Data Ladder


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