What is Data Cleaning?

Correcting or deleting erroneous, corrupted, improperly formatted, duplicate, or missing data from a dataset is known as a data cleaning process.

There are many possibilities for data duplication or mislabeling by integrating different data sources. And if the data is accurate, the results and algorithms are inconsistent. Since data cleaning methods differ from dataset to dataset, there is no one-size-fits-all approach to prescribing the exact steps throughout the method. However, creating a blueprint for your data cleaning procedure will ensure that you do it correctly every time.

The importance of data cleaning in analytics

Using clean data would maximize the overall efficiency and enable you to make decisions based on the best quality evidence available. Some of the advantages of data cleansing in data science are as follows:

  • Errors are eliminated where many data points are involved.
  • Clients will be happier and managers will be less irritated if there are fewer mistakes.
  • Ability to figure out the various tasks and what the data is supposed to do.
  • Monitoring mistakes and better documentation to determine the source of errors makes it possible to correct inaccurate or corrupt data for potential applications.
  • Data cleaning software can allow for more effective business processes and faster decision-making.

How to do data cleansing

  • Remove all unnecessary observations, such as duplicates or invalid observations, from your dataset. Duplicate findings are more likely to occur during the data collection process. Duplicate data can be created when you merge data sets from different sources, scrape data, or collect data from clients or multiple agencies. One of the most important aspects to remember in this phase is de-duplication. When you notice observations that aren’t important to the dilemma you’re trying to solve, you’ve made irrelevant observations.
  • When you calculate or move data and find odd naming patterns, typos, or inaccurate capitalization, you have structural errors. Mislabeled divisions or groups may result from these inconsistencies. For example, the terms “N/A” and “Not Applicable” can occur in the same category, but they should be treated as such.
  • There would frequently be one-off findings that do not seem to match within the data you are studying at first glance. If you have a good excuse to delete an outlier, such as incorrect data entry, doing so will make the data you’re dealing with function better. The existence of an outlier, on the other hand, will also prove a hypothesis you’re working on.
  • Many algorithms would not consider missing values, so you can’t dismiss them. There are some options for dealing with lost records. Neither choice is ideal, but they can all be considered.

You can drop findings of missing values as a first choice, but this can cause you to lose information, so be aware of this before you do so.

As a second choice, you can fill in missed values based on other observations; however, you risk losing data credibility when you’re working on hypotheses rather than true observations.

As a third alternative, you might change the way the data is used to handle null values more efficiently.