In the realm of data science, the quality of data is paramount. Generally speaking, garbage in, garbage out. Data cleaning is a crucial step in the data preprocessing pipeline. Data cleaning, or data cleansing or scrubbing, involves identifying and correcting errors, inconsistencies, and inaccuracies in datasets to improve their quality and reliability. This process is essential for ensuring that the data used for analysis and modeling is accurate, reliable, and consistent.
This article will delve into the data-cleaning process and outline its manifold benefits.
What is Data Cleaning in Data Science?
The process of data cleaning involves identifying and fixing incorrect data. Possible problems are formatting errors, duplicates, corruption, inaccurate, incomplete, and irrelevant data. Data values representing incorrectness in the data can be fixed in various ways. A data pipeline is used to implement any data science project’s cleaning and validation steps. Throughout a data pipeline, inputs and outputs are consumed and produced. Data pipelines have the advantage that each step is smaller, self-contained, and easier to check. Additionally, some data pipeline systems allow you to resume the pipeline from the middle, which saves time.
Why is Data Cleaning So Important?
Data cleaning is of paramount importance when it comes to data analysis and decision-making. It provides the basis for accurate and reliable insights. In addition to errors, inconsistencies, missing values, and outliers, raw data often contains errors, inconsistencies, and missing values, which can corrupt analysis results. We ensure consistency, completeness, and error-free information by cleaning the data.
Analysis and machine learning models maintain credibility and integrity when data is cleaned properly. By keeping data clean, you can enhance the quality of your results and save time and resources that would otherwise be spent troubleshooting and correcting errors. It is essential to clean data before data analysis, as it enables organizations to make informed decisions based on reliable data.
7 Steps to Clean Your Data
Data leaders should prioritize creating clean, reliable datasets that can be leveraged across the organization for effective data analytics. There are seven basic steps to clean data effectively:
Step 1: Identify data discrepancies using data observability tools
Data analysts should initially use data observability tools such as Monte Carlo or Anomalo to detect data quality issues, such as duplicated data, missing data points, and entries with incorrect values.
Step 2: Remove data discrepancies
Data analysts can remove discrepancies from the existing dataset after identifying and evaluating them appropriately. Data entry deletions, merging of data sets, and accuracy assurance may be needed.
Step 3: Standardize data formats
Data discrepancies must be removed before standardizing data formats can ensure consistency throughout the dataset. Dates may be formatted differently in one data set than in another. It is essential for data analysts to ensure that all data is stored in the same format across all datasets, such as YYYY/MM/DD.
Step 4: Consolidate data sets
After that, different data sets can be consolidated unless privacy laws prevent it. Data silos are often broken down, and datasets are brought together. Using or considering data lakes, data warehouses, or data lakehouses, many organizations use emerging data architectures. Data analysis becomes more efficient when data sets are consolidated by reducing data redundancy and streamlining data processing.
Step 5: Check data integrity
Data analysts and visualizers should verify that all data is valid, accurate, and current before analyzing or visualizing it. The data is validated or scrutinized for accuracy during a data integrity test.
Step 6: Store data securely
Data professionals must then protect data against unauthorized access and loss by storing it securely. Additionally, it is essential to encrypt data at rest, use secure file transfer protocols while transmitting data, and regularly back up data.
Step 7: Expose Data to Business Experts
Lastly, business users are exposed to the data. These domain experts can quickly identify inaccurate or outdated data thanks to their deep knowledge. Data and business teams need the right self-service business intelligence solution so that users can explore data to find data cleanliness issues.
Data analysts can reduce data redundancy and ensure data reliability by following these seven data cleanup steps. By analyzing data, data scientists can make more accurate decisions.
Top Data Cleaning Benefits
Among the most important aspects of data management is data cleansing. Performing data cleaning can profoundly impact data accuracy, usability, and analysis. Data cleansing techniques such as validation, verification, scrubbing, and normalization ensure the accuracy and integrity of business data. Among the many benefits of data cleaning for organizations are:
Improved Data Accuracy
Data records can be improved by regularly cleaning them, especially if they are part of an automated data pipeline. Data integrity is crucial for data analysis and enables organizations to make data-driven decisions confidently.
Increased Data Usability
Data professionals such as analytics engineers may trust clean data for a wider variety of use cases, making data more accessible and valuable to other users across different business areas. Data cleaning ensures that data is in a standardized format and can be used for various data-driven applications.
Easier Data Analysis
Analyzing data with clean data makes it easier to gain insights. To produce reliable data analysis results, it is essential to ensure that data records are accurate and current.
Ensure Data Governance
A data strategy must ensure that data is secure and is only accessible to those who need it. Data governance initiatives that protect privacy can be strictly followed with proper data cleaning.
More Efficient Data Storage
By eliminating unnecessary data and reducing data duplication, data cleaning can help reduce data storage costs, whether using a cloud data warehouse or an on-premises solution. Consolidating data records allows organizations to reduce data storage requirements and maximize data utilization.
Real-life Examples Of Data Cleaning
Data cleaning ensures that data is reliable and accurate for further analysis during any data analysis process. As examples, here are three real-life data-cleansing examples:
Empty or Missing Values
Data sets can sometimes have missing or empty data points. The gaps will be filled in with appropriate estimates for the data set by data scientists using data-cleaning techniques. For example, the average location data from the data set can be substituted for a missing “location” data point. Organizations can find data points from other sources to fill this gap.
Some data points in a data set may differ greatly in value or behavior from those in others. By incorporating this factor, data analysis can be skewed, resulting in incorrect results. It’s essential to understand outliers and anomalies to ensure accuracy. Identifying and removing outliers from data sets can be accomplished with the help of data-cleaning techniques.
When data is formatted, it is converted into a particular data type, its structure is changed, or it is made into a suitable model. Data-cleaning techniques will ensure that data sets are formatted correctly, and inconsistencies can lead to errors during data analysis. In addition, data scientists can combine multiple data sources into one data set or convert categorical data to numeric data.