09/26 2023

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

According to IDC’s research, data analysis will become a highly important skill in the future. Taking action based on the results generated from data allows businesses to seize more opportunities. Prior to conducting data analysis, data preprocessing, specifically “data cleaning,” plays a crucial role in influencing the subsequent analytical outcomes. Today, Nextlink Technology aims to take you deep into understanding the definition and execution of data cleaning while also enabling you to reduce data preprocessing time using cloud tools. This enhances data availability, empowering businesses to make more valuable decisions through data.

What is data cleaning?

The steps of data cleaning are crucial for businesses that intend to utilize this data for future applications such as “Machine Learning (ML)” or “Business Intelligence (BI)” tools. However, a dataset is rarely flawless; typically, the following situations may arise:

  • Outliers: In data sets, extreme values may occur, which can subsequently impact the development of machine learning models.
  • Erroneous Data: Data within the same column may contain other characters, such as gibberish or special symbols, resulting in errors in the entire uncleaned dataset.
  • Duplicate Data: Repeated values can affect the results of data analysis.
  • Missing Data: Missing data may appear within a row of data and requires processing.
  • Inconsistent Data Types: If different data types, such as numeric, boolean, and string, appear in the same column, it can lead to errors in data analysis.

Therefore, the function of data cleaning is to process the data elements mentioned above that can affect “subsequent data analysis tasks.” It involves transforming “missing values” or “erroneous values” from the original file into data that can be used in subsequent machine learning models. Simultaneously, it entails modifying and removing incorrect and incomplete data columns, ensuring that the data is thoroughly cleaned.

Why do businesses need to perform data cleaning?

In an era that emphasizes the empowerment of data, businesses must start giving importance to the availability of data. Therefore, in data collection and preprocessing, it is inevitable that more effort needs to be invested to facilitate data analysis. Only then can businesses harness the advantages that data brings:

  • Avoiding misjudgment due to data flaws: In the world of data, it’s not as clean as one might imagine. Various data errors as described above often occur. Therefore, data cleaning plays a significant role in organizing abnormal data, ensuring that data models have high precision, and providing meaningful analysis.
  • Improving the decision-making process: Sound decisions are built on meaningful data quality. Thus, the completeness of data cleaning can provide businesses with a broader perspective for decision-making. By utilizing high-quality data analysis results, organizations can make more suitable decisions, thereby aiding in business development.
  • Exploring new business opportunities: The ultimate goal of data cleaning is to help businesses uncover hidden insights, market trends, or previously unnoticed details. Comprehensive data cleansing assists companies in identifying different business opportunities in the market or reallocating resources to maximize efficiency, ultimately expanding their operations.

What tools can be used to begin planning data processing and fostering a data-empowered corporate culture?

The purpose of data cleaning is to prevent flawed data from leading to misjudgments and to assist businesses in enhancing their decision-making processes through data.

How to plan and handle data cleaning?

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

  • Determine the objective of data cleaning: Businesses should first assess the quality of their data and determine the goal of cleaning. Preliminary tasks include repairing missing or erroneous data, removing duplicate data, and standardizing data formats.
  • Establish data quality standards: Companies should define data quality standards based on their business needs and data usage context. This includes ensuring data accuracy, completeness, consistency, uniqueness, and other criteria. Data cleaning should then align with these standards to ensure the quality of analysis.
  • Develop a data cleaning process: Businesses need to create a data cleaning process, including the sequence of cleaning steps, methods, and technology choices. Continual refinement is essential to demonstrate the value of data cleaning.
  • Select appropriate tools and techniques: Choose tools and techniques that suit the business’s data analysis requirements. Common data cleaning tools include OpenRefine, Trifacta, DataWrangler, while AWS cloud services like AWS Glue and Amazon EMR can be used for data cleaning tasks, assisting companies in achieving their data cleaning goals.
  • Test and validate data cleaning results: After data cleaning, businesses should test and validate the results. Compare the cleaned data with the original data, verify the consistency and accuracy, and ensure it meets the expected data cleaning standards.
  • Establish monitoring mechanisms: Implement a monitoring mechanism for data cleaning to regularly inspect data quality. Repair and update data cleaning steps and mechanisms as needed to help companies maintain the quality of their data cleaning operations on a routine basis.

With well-defined steps, businesses can begin planning their data cleaning operations and choose the right tools. The data solutions available on AWS cloud can offer a one-stop service, allowing enterprises to handle data cleaning and preprocessing tasks in one go!

AWS services streamline data cleaning, tackling data preprocessing issues comprehensively

AWS offers a variety of big data analytics tools to assist businesses in conducting end-to-end data analysis tasks. As for data cleaning, Nextlink’s Cloud architects have compiled two cloud-based tools to help readers clean data and make better decisions!

  • AWS Glue: AWS Glue is a serverless and scalable data integration service that can perform “Glue ETL” tasks by configuring it to work with data stored in Amazon S3. It supports various data processing frameworks and workloads, simplifying the cumbersome process of data cleaning for businesses.
  • Amazon EMR (Big Data Platform): The Big Data Platform can handle real-time data streams, extract data from various sources, and conduct large-scale data processing and cleaning. This ensures that there are no anomalies in the data, accelerating business operations for subsequent big data analysis and machine learning model building, ultimately achieving precise decision-making results.

By utilizing these two “serverless tools,” businesses no longer need to worry about maintaining infrastructure while performing data preprocessing. Using cloud-based tools for data cleaning not only saves costs that would otherwise be spent on other data processing tools but also enables efficient management of all data-related issues, streamlining the process of data analysis.

When it comes to data processing and data analysis, Nextlink Technology holds the official AWS “Data Analytics Competency” certification and boasts a complete data analysis team. We provide businesses with end-to-end services, including data cleaning, data analysis, and insight reporting. We have previously successfully assisted Taiwan’s Economic and Trade Network in data processing:

  • Using tools like AWS Glue and Amazon Redshift, we reduced data processing and analysis time by 30%.
  • By combining various storage tools such as AWS S3 and Amazon RDS, we ensured cost-effective data usage.
  • Leveraging Tableau data drag-and-drop capabilities, we streamlined data integration, enhancing decision-making efficiency and precision.

Nextlink Technology collaborates with Taiwan’s Economic and Trade Network to create a diversified digital investment promotion model using AWS. After setting up a data lake and completing comprehensive data processing, Taiwan’s Economic and Trade Network can now perform data analysis in as little as two weeks, significantly accelerating the pace of market trend analysis, compared to the previous maximum of four months!