09/23 2023

What is Data Cleaning? A Necessary Step Before Conducting Data Analysis!

According to IDC’s research, data analysis is expected to become a crucial skill in the future. Taking action based on the results generated from data can give businesses a competitive edge. However, before diving into data analysis, data preprocessing plays a pivotal role in influencing the subsequent analysis outcomes, with the most crucial step being “data cleansing.” Today, at Nextlink Technology, we aim to provide you with a deeper understanding of the definition and execution of data cleansing. We’ll also show you how to reduce the time spent on data preprocessing using cloud tools, increasing the availability of your data and enabling businesses to make more valuable decisions using data.

What is data cleansing?

The steps of data cleansing are crucial for enterprises that intend to use these materials for future applications such as Machine Learning (ML) or Business Intelligence (BI) tools. However, it’s rare to find a dataset that is entirely flawless. Usually, the following situations arise:

  • Outliers: Extreme values in the dataset can negatively impact the construction of machine learning models.
  • Erroneous data: Data within the same field may contain other characters such as gibberish or special symbols, resulting in inaccuracies in the entire uncleaned dataset.
  • Duplicate data: Repeated values can affect the results of data analysis.
  • Missing data: Missing data may occur in a row and needs to be addressed.
  • Inconsistent data types: If different data types like numbers, booleans, and strings appear in the same field, it can lead to errors in data analysis.

The purpose of data cleansing is to process data that can affect “subsequent data analysis operations,” transforming “missing values” or “erroneous values” from the original file into data that can be used in subsequent machine learning models. Additionally, it involves modifying and removing incorrect and incomplete data columns, ultimately resulting in clean data.

Why do businesses need data cleansing?

In the era of emphasizing data empowerment, businesses must start focusing on data availability. Therefore, in data collection and preprocessing, more effort is required to enable data analysis, allowing businesses to leverage the advantages of data:

  • Avoiding misjudgment due to data flaws: In the world of data, it’s far from being as clean as one might imagine. Various data errors often occur, as mentioned earlier. Therefore, data cleansing plays a crucial role in organizing abnormal data, ensuring high precision in data models, and providing meaningful insights through analysis.
  • Improving the decision-making process: Sound decisions are built on meaningful data quality. The completeness of data cleansing broadens a business’s decision-making horizon and, by using high-quality data analysis results, enables more informed decisions, thereby aiding in business development.
  • Exploring new business opportunities: The primary goal of data cleansing is to help businesses uncover hidden information, market trends, or previously overlooked details. Thorough data cleaning assists businesses in identifying different opportunities in the market, reallocating resources for maximum efficiency, and expanding their operations.

To begin planning data handling and fostering a data-empowered corporate culture, what tools can be used to achieve this goal?

The purpose of data cleansing is to prevent flawed data from leading to misjudgment and to assist businesses in improving their decision-making processes using data.

How to plan and handle data cleansing?

When businesses plan and handle data cleansing-related tasks, they can pay attention to these seven elements:

  • Define the goal of data cleansing: Businesses should first assess the quality of their data and determine the objectives of cleansing. This involves tasks like repairing missing or erroneous data, removing duplicate data, and standardizing data formats.
  • Establish data quality standards: Companies should establish data quality standards based on their business needs and data usage scenarios. This includes ensuring data accuracy, completeness, consistency, uniqueness, and more. Data cleansing should be conducted in accordance with these standards to ensure the quality of analysis.
  • Create a data cleansing process: Businesses need to establish a data cleansing process, including the order of cleansing, methods, and technology choices. Continuous refinement should be a part of this process to demonstrate the value of data cleansing.
  • Select appropriate tools and techniques: Based on the business’s data analysis requirements, choose suitable tools and techniques for data cleansing. Common data cleansing tools include OpenRefine, Trifacta, DataWrangler, while AWS cloud offers solutions like AWS Glue and Amazon EMR for big data processing to assist businesses in their data cleansing operations.
  • Test and validate data cleansing results: After data cleansing, companies should test and validate the results. Compare the cleansed data with the original data and validate the consistency and accuracy to ensure they meet the expected data cleansing standards.
  • Establish monitoring mechanisms: Set up monitoring mechanisms for data cleansing to periodically check data quality. Repair and update data cleansing steps and mechanisms as needed to help businesses maintain the quality of their data cleansing operations.

With these comprehensive steps, businesses can begin planning their data cleansing operations and choose appropriate tools. Data solutions available on the AWS cloud can provide businesses with a one-stop service to handle both data cleansing and preprocessing in one go!

AAWS Services for Data Cleansing: A Comprehensive Approach to Data Preprocessing

AWS offers a variety of big data analysis tools to assist businesses in performing end-to-end data analysis tasks. When it comes to data cleansing, BoHong Cloud architects have compiled two cloud tools to clean data and help businesses make better decisions!

  • AWS Glue: AWS Glue is a serverless and scalable data integration service that can perform “Glue ETL” tasks on data stored in Amazon S3, supporting various data processing frameworks and workloads. This simplifies the tedious process of data cleansing for businesses through configuration.
  • Amazon EMR (Big Data Platform): The big data platform can handle real-time data streaming, extract data from various sources, and perform large-scale processing and data cleansing to ensure data integrity. This accelerates the execution of subsequent big data analysis and machine learning model building, leading to precise decision-making.

By using these two “serverless tools,” businesses can focus on data preprocessing without the need to maintain underlying infrastructure. Utilizing cloud tools for data cleansing not only saves costs compared to other data processing tools but also enables efficient management of all data-related issues associated with data analysis.

談到資料處理與數據分析,博弘雲端擁有AWS「資料分析能力官方認證」,並且配有完整的數據分析團隊,提供企業從資料清洗、資料分析,到洞察報告等一條龍式服務,更在先前成功協助台灣經貿網進行資料處理: When it comes to data processing and data analysis, Nextlink Technology, with its AWS Certified Data Analytics and a complete data analysis team, offers end-to-end services to businesses, including data cleansing, data analysis, and insight reporting. They have successfully assisted Taiwan’s Economic and Trade Network in data processing:

  • Utilizing tools like AWS Glue and Amazon Redshift, they have reduced data processing and analysis time by 30%.
  • Combining various storage tools such as AWS S3 and Amazon RDS to ensure cost-effective data usage.
  • Using Tableau’s data drag-and-drop capabilities to streamline data integration, enhancing decision-making efficiency and accuracy.

Nextlink Technology collaborates with Taiwan’s Economic and Trade Network to create a diversified digital investment promotion model through AWS. With the establishment of a data lake and comprehensive data processing, Taiwan’s Economic and Trade Network has significantly reduced the time required for data analysis, from a potential four months down to as little as two weeks, accelerating their ability to discern market trends!