Data Cleaning: What It Is, Procedure, Best Practices

The scalability and reliability of your business heavily rely on the quality of the data you utilize. Inaccurate or missing data might result in flawed inferences, incorrect procedures, and lost opportunities. On the other hand, organized and structured datasets can assist you in performing robust data analysis and predictions. This is where the role of data cleaning takes center stage. It assists you in unlocking the potential of your data and making more informed decisions by comprehending the fundamentals of data cleansing.

This article discusses the significance of data cleaning, the steps required to improve data quality, and the best practices for maintaining data integrity.

What is Data Cleansing?

Data cleansing is the process of correcting inaccurate, missing, duplicate, or otherwise erroneous data in a data set. It is also known as data cleaning or data scrubbing. This process involves identifying and fixing data mistakes by adding, deleting, or altering them. It is a key step in the preparation of data since it maintains the reliability and accuracy of the data used for analysis or machine learning algorithms.

What are the Benefits of Data Cleaning?

Data cleaning is becoming more crucial than ever in ensuring that data is reliable and clean as it gets increasingly integrated into your business operations. Apart from organizing raw data in a structured format, data cleaning has many benefits, such as:

Make Data Analytics-Ready

Data must be free of duplicate entries, errors, and irrelevant values before being converted from one format to another. To facilitate this process, data cleaning ensures you are validating your data to make it analytics-ready. This, in turn, promotes efficient data warehousing and storage processes.

Enhances Operational Efficiency

Having an organized and structured database boosts business productivity. If your sales and customer support teams can access accurate and comprehensive customer data, they can communicate effectively, offer more tailored services, and quickly address problems. This results in improved data management, streamlined workflow, and increased customer satisfaction.

Promotes Better Business Decision-Making

Data cleansing allows you to make data-driven decision-making by providing precise and reliable information. This empowers you to gain actionable insights into consumer behavior, market trends, and business performance, thus facilitating the scalability of your enterprise. In addition, implementing a data cleaning process will help discover opportunities, mitigate risks, and gain a competitive edge in the market.

Cost-Effective Solution

Maintaining a clear and concise database can help your company avoid unnecessary expenses. It can also help you optimize your budget by eliminating repetitive data, removing unnecessary values, concentrating on promising leads, and limiting resources spent on unproductive initiatives.

What Are The Steps Involved in the Data Cleaning Process?

There are several steps in the data cleaning process, each of which addresses a different kind of discrepancy in the dataset. To achieve high-quality data, you can perform the following data-cleaning steps:

1. Define Data Cleaning Objectives

Before beginning the data cleaning process, it is crucial to assess the raw data and identify your requirements or desired output from the dataset. This helps you focus on the specific parts of the data, thus saving your time and resources. 

2. Eliminate Duplicate or Irrelevant Values

You can generally observe repetitive data while extracting it from multiple sources into a centralized repository. Such values take up unnecessary space in your dataset and often result in flawed analysis. Using data cleaning tools or techniques, you can easily locate duplicate or irrelevant values and remove them to achieve a more optimized dataset.

3. Correct Structural Flaws

Structural errors include misspellings, incorrect word usage, improper naming conventions, capitalization mistakes, and many others. They mainly occur while migrating or transferring data from one place to another. So, applying a quick data check in such a scenario ensures the credibility of your dataset.

4. Remove Data Outliers

Outliers are unusual values in a dataset that differ greatly from the existing values in the dataset. Although the presence of such values can be fruitful for research purposes, they can also impact your data analysis process. Therefore, it is always advisable to employ data-cleaning methods to remove any inconsistent values, thus maintaining data accuracy.

5. Restore Any Missing Data

Data values can be lost or removed during the extraction process, leading to inefficient data analytics. Therefore, before using your data for business operations, you must scan the dataset thoroughly and look for any missing values, blank spaces, or empty cells in it. 

6. Ensure Data Validity 

Once the above steps are completed, you must perform a final quality check on your dataset to ensure its authenticity, consistency, and structure. To facilitate this process quickly, you can also leverage AI or machine learning capabilities and verify data. This helps your organization work with reliable data and use it for seamless analysis and visualization.

Data Cleaning Best Practices

In this section, you will explore some of the data cleansing best practices:

Understanding Data Inconsistencies

The process of cleansing data is extensive and requires a strong, well-organized workflow. However, before performing this process, it is crucial to understand the type of data inaccuracies and errors. These fallacies can be in the form of misspellings, blank spaces, or repetitive values, resulting in data inconsistency. Therefore, developing a clear set of objectives for your cleaning process is one of the best practices for addressing data flaws. 

Perform Detailed Data Profiling

Data profiling is an essential step in determining the state of your data. It entails examining the dataset's completeness, quality, and structure. Comprehensive data profiling allows you to discover trends, anomalies, and problems with data quality that need to be fixed. 

💡Suggested Read: Data Profiling Tools

Leverage Automated Data Cleaning Tools

Automation is one of the simplest methods of introducing regular data cleansing into your workflow. You can save time using automated data cleaning solutions, particularly if your team has just started implementing best cleaning practices. Based on the size of your database and the use case, you can employ tools such as OpenRefine, TIBCO Clarity, and Tableau Prep.

Continuously Monitor and Improve Processes

Data cleansing is a continuous process. Therefore, your data team should regularly review and improve how your data is cleaned as per the pre-defined standards. Your database will evolve and change with the expansion of your business, its products, and your client base. Therefore, your data cleaning practices must adapt to ensure that you maintain precise, consistent, and high-quality data at every level. 

Ensure Continued Data Governance

Data cleaning is not a one-time event; therefore, a strong data governance architecture must be established to ensure data quality over time. You should formulate policies and procedures for entering, updating, or retrieving data. To ensure continuous data cleanliness, define responsibilities for data stewardship, perform frequent data audits, and regularly check the quality of the data.

Incorporating Airbyte into Data Cleaning Workflows

Before cleaning your datasets, it is important to gather your data from different sources in one place. This step not only saves your time but also ensures consistency and compatibility across the integrated datasets. When it comes to performing streamlined integration, Airbyte is a robust option.

Airbyte is a go-to platform for data integration, ingestion, and replication. It empowers you to effortlessly extract data from sources such as SaaS applications, databases, or flat files and load it into a data lake or warehouse. Airbyte has an extensive library of 350+ pre-built connectors to automate data pipeline creation within minutes. If you can’t find a connector of your choice, you can create a custom one using its CDK or contact the Airbyte sales team to request a new one.

Apart from integration capabilities, Airbyte also supports data replication. It provides CDC functionality to capture any changes in the source data and replicate them in the target system. This allows you to track your data and maintain data consistency. 

Some of the key features offered by Airbyte are:

• Developer-Friendly UI: With Airbyte, you can work with its recently launched open-source Python library, known as PyAirbyte. You can utilize this library to programmatically extract data from Airbyte-supported connectors and enhance your data pipeline development capabilities.‍
• Handle Different Data Types: Airbyte supports data sources that manage unstructured, semi-structured, and structured datasets, thus enabling you to adapt to modern data integration requirements.‍
• Data Security: To protect your data from external threats, Airbyte offers various security measures. These include audit logging, credential management, access controls, encryption, authentication mechanisms, and regular security assessments. Vibrant Community: Being an open-source platform, Airbyte hosts a large community of 15000+ members. You can engage with other members to resolve queries during data ingestion and help improve Airbyte features.

Conclusion

As data continues to play a significant role in business decision-making, a commitment to data cleaning has become imperative. By carefully identifying data flaws and inaccuracies, you can enhance its data quality and gain meaningful insights. This article has delved deep into the concept of data cleaning, its benefits, data cleansing procedures, and its best practices to help you build an optimized workflow. 

Data cleaning can be effectively performed on a dataset only when it is present in a unified repository. So, we recommend using Airbyte, a reliable data integration platform. It’s rich connectors library will help you streamline data collection and centralize your data into targeted repository. Sign in on the platform today and explore its dynamic features.

💡Suggested Read: Aggregation with SQL