Data Cleaning 101: A Step by Step Guide for Data Analysts
Data cleaning is a vital but often overlooked step in the data analysis process. Without it, businesses risk making decisions based on inaccurate or incomplete information that can cost them millions of dollars and significantly reduce their productivity. Data analysts and engineers should be aware of the importance of data cleaning and familiarize themselves with effective techniques to ensure they are working with accurate information. In this article, we will discuss why data cleaning is essential and provide an overview of six useful methods for effectively scrubbing your datasets.
Overview of Data Cleaning and Why it is Important
Data cleaning is the process of preparing data for analysis. This involves identifying and correcting errors, inconsistencies, and duplicates in the dataset so that it can be used effectively. Data cleaning is essential because it ensures that any insights derived from the data are reliable and accurate. Data can become dirty due to a variety of reasons such as human error, formatting issues, missing values or incorrect assumptions. Without proper cleaning these errors can lead to misinterpretations and inaccurate conclusions which can be costly for businesses.
Data Cleaning Techniques That You Can Put Into Practice Right Away
Data cleaning techniques vary depending on the dataset and the goals of the analysis. However, here are some of the most common methods that Data Analysts use to clean their data:
1. Remove Duplicates
Removing duplicates is an essential part of data cleaning as it ensures that any insights derived from the data are reliable and accurate. Data can become dirty due to a variety of reasons such as human error, formatting issues, missing values or incorrect assumptions. Duplicate records in a dataset can lead to bias in analysis results and misinterpretations which could be costly for businesses. Data engineers should pay close attention when identifying and removing duplicate records so that they have clean datasets to work with.
2. Remove irrelevant data
Removing irrelevant data is a key part of the data cleaning process. Data engineers should review the entire dataset and identify any information that doesn’t contribute to their analysis or objectives. Data that is not relevant should then be filtered out so as not to skew any results. Examples of irrelevant data include non-relevant fields, outdated records, corrupted files, and duplicate records.
3. Standardise capitalisation
Standardising capitalization is an important component of data cleaning. By standardizing the capitalization of text, Data Analysts can ensure that all variables are treated equally when being processed. This helps to prevent any bias from arising due to different cases being used for the same variable. For a computer, small case and large case letters are not same hence they are treated differently. For instance, Bill, bill, BILL all are different words for a computer. Capitalization also plays an important role in readability and maintainability when dealing with large datasets. Data Analysts can use automated tools such as Regular Expressions (RegEx) or Data Cleaning Libraries to help standardize their datasets quickly and efficiently.
4. Convert Data Types
Converting data types involves changing a variable’s type from one format to another in order for it to be used within a specific context or application. This helps make sure that any insights derived from the dataset are reliable and accurate, as different applications may interpret the same value differently depending on its type. Data engineers should also take into account any potential errors that could arise when converting between different formats so as not to introduce any discrepancies into their datasets.
5. Clear Formatting
Data analysts must pay close attention to the formatting of their dataset, as any inconsistencies in formatting can lead to inaccurate results and misinterpretations. Data analysts should review any formatting errors in the dataset, such as inconsistent spacing, use of different delimiters or special characters, and incorrect field alignment.
Data Analysts should also take into account any potential errors that could arise when converting between different formats so as not to introduce any discrepancies into their datasets. By ensuring that the data follows a consistent format, Data Analysts can prevent bias from arising due to different cases being used for the same variable. Furthermore, proper clean formatting can help improve readability and maintainability when dealing with large datasets by making it easier for Data Analysts to identify patterns and trends within their data quickly and efficiently.
6. Handle Missing Values
This is one of the key step in data cleaning lifecycle. Data analysts should review the dataset for any missing values or records and take steps to either fill in the gaps with valid information or remove them altogether. There are several approaches that Data Analysts can use when dealing with missing values such as imputing, interpolation, deletion, or using a suitable substitute value. Each approach has its own merits and drawbacks depending on the context of the dataset so Data Analysts need to weigh up all options before making a decision.
The Wrap-Up
While the data cleaning may be a tedious process but it will allow you to take care to ensure that their datasets are free from any inconsistencies or errors. We all know of the phrase “Garbage in Garbage out”, Feeding bad data into machine learning algorithms will lead to poor results and insights. There are a range of techniques and strategies that are used for data cleaning but
Ultimately, Data Analysts must use their discretion when deciding which approach best suits each context as no one-size fits all solution exists when it comes to handling missing values.
