In the era dominated by data, businesses have gradually realized the value and impact of data. Data is no longer just a string of numbers; it is a valuable resource that can provide deeper insights at the business level. This has transformed data analysis from being an option into an inevitable trend for enterprises. However, given the immense volume of data, where does one begin?
According to surveys, in 2022, the world generated nearly 100ZB of data, which is equivalent to needing more than 97.2 billion 1TB-sized hard drives to store all this data. Yet, these data do not initially provide complete analysis; they require a form of “alchemy” to extract valuable insights. Hence, the skill of “data preprocessing” becomes the cornerstone for businesses venturing into data analysis.
In Nextlink Technology’s special series, “Knocking on the Door with Data Analysis,” we aim to introduce you to the concept of data preprocessing, helping you lay a solid foundation in the early stages of data analysis and empowering your enterprise with data!
Table of Contents
Table of Contents
What is Data Preprocessing?
Want to master the secrets of data analysis? First, grasp the core concept of data preprocessing! Data preprocessing, refers to the process of cleaning, transforming, and organizing raw data before conducting data analysis, model building, or machine learning. The purpose of data preprocessing is to ensure data quality and consistency, reduce uncertainty and noise, and make the data more suitable for subsequent analysis and modeling.
Raw data typically comes from multiple sources, including a company’s ERP system, marketing platform data, or business data indicators, all of which require preprocessing. Typically, the most raw data is riddled with errors, incorrect formats, extreme values, and duplicate data, which are common reasons for poor data quality. Therefore, the importance of data preprocessing lies in identifying and filtering out this unfit data, making vast data sets valuable for insights.
What are the Steps in Data Preprocessing?
However, if a business wants to embark on data preprocessing, what are the steps that can help the enterprise move towards a successful path in data analysis? Nextlink Technology has organized four major steps to help you understand the key aspects of data preprocessing:
Data Cleaning
Data cleaning involves the removal of errors, missing values, and inconsistencies in the data. During this step, businesses need to fill in missing values, remove duplicate or invalid records, and correct erroneous values or labels.
Data Transformation
Data transformation involves altering the data to make it more suitable for analysis. The main components of data transformation include feature scaling (such as standardization or normalization), feature extraction (extracting more meaningful features from raw data), categorical encoding (converting categorical data into numerical values), and dimensionality reduction (such as principal component analysis). For example, a company aiming to analyze customer spending behavior may have multiple features, including data like spending frequency and purchased items. These data must be transformed into meaningful “values” during preprocessing to facilitate subsequent analysis.
Data Integration
Data integration is the process of consolidating data from various sources, in different formats and structures, into a unified data repository. It’s often seen as the aggregation of data sources across departments and systems to ensure data consistency and availability. However, challenges can arise when dealing with inconsistent data formats in the process. Therefore, it is recommended that businesses implement data cleaning steps to facilitate the smooth execution of data integration.
Data Reduction
Finally, there is data reduction, which involves reducing the scale of data by decreasing its dimensions, size, or complexity while retaining essential features and information. Data reduction helps to lower computational costs, mitigate the impact of noise, and enhance the efficiency and interpretability of models.
How to leverage Amazon Web Services to enhance data preprocessing efficiency?
Compared to traditional data analysis using on-premises Excel tools, which can consume a significant amount of manpower and time, the advantage of using the cloud lies in efficiency improvement. Additionally, it allows for the integration of systems running on the cloud platform. By incorporating the steps of data preprocessing with AWS cloud-based data solutions, businesses can position themselves as leaders, employing key techniques to streamline intricate data operations and successfully extract valuable business insights.
Amazon S3
During the data source aggregation process, there is a need for a centralized location to store the data, and Amazon S3 is an object storage solution that offers businesses the flexibility to store raw data efficiently while achieving cost-effectiveness advantages.
Amazon Glue
Amazon Glue is a fully managed ETL (Extract, Transform, Load) service that automates processes such as data extraction, transformation, and loading. It helps businesses integrate data from different sources, perform format conversions and cleaning, and finally load it into a target database (such as another Amazon S3 storage or a data lake).
Amazon Redshift
Amazon Redshift is a high-performance data warehousing service used for large-scale data analytics. Businesses can import data from various sources into Redshift, perform transformations and cleaning, and then run complex analytical queries. Redshift also accelerates data preprocessing.
AWS Data Pipeline
After performing ETL and data import tasks, AWS Data Pipeline assists businesses in automating the data transformation process. It allows enterprises to extract what they need from various data integrations and adjust the data processing workflow. This way, businesses no longer need to expend significant effort on data retrieval and transformation, as AWS Data Pipeline takes care of the data preprocessing steps.
Amazon SageMaker Data Wrangler
If you prefer to integrate data preprocessing directly into machine learning, AWS offers the “Data Wrangler” service within its machine learning solution, Amazon SageMaker. This service enables businesses to clean, transform, and engineer features from data before deploying machine learning models, facilitating the subsequent model training process.
How can businesses achieve success in data preprocessing?
Regardless of the industry, adopting data analysis in daily business operations inevitably goes through a period of transformation. However, setting up the foundation in the early stages to automate the cumbersome tasks of data preprocessing is crucial for businesses moving towards a data-driven culture.
Taiwan Trade Network is an organization that helps Taiwanese businesses connect with global commercial opportunities, primarily focusing on international business matchmaking and promotion. Dealing with complex business information and vendor data, coupled with rapidly changing global business conditions, demanded a more flexible system to meet industry demands. Therefore, Taiwan Trade Network initiated the optimization of their data preprocessing workflows. They utilized AWS Lambda, AWS Glue, and Amazon Redshift, among other tools, to reduce the time spent on raw data preprocessing. This resulted in saving nearly 30% of the analysis time, enabling faster market assessments for vendors.
As a freight logistics provider in the Asia-Pacific region, Hong Kong Yamato Group relies on its Freight Management System (FMS) for daily distribution and shipping tasks, handling thousands of shipments daily. However, during the pandemic, the logistics and transportation industry faced challenges integrating “systems” and “operations,” not to mention handling extensive data without a clear purpose. Facing these challenges, Yamato decided to adopt the AWS Data Pipeline solution to organize and automatically transform their data. Implementing key strategies for data preprocessing, they updated their management systems, enabling them to meet market demands and accelerate their industry transformation.
From the two industry examples above, it becomes evident that data preprocessing is an indispensable tool for businesses on their journey towards data analysis. Only by cleaning and correctly formatting data in the early stages can companies make the most of their data analysis efforts in the later stages. This approach leads to efficient trend identification and precise analysis insights. Nextlink Technology possesses AWS-certified data and analytics services competency and has assisted leading brands in various industries with data preprocessing and data analysis projects, becoming an enterprise that empowers others with data. Are you still hesitant to take the first step toward data analysis due to the complexity of data preprocessing? Contact our professional cloud data analytics consultant, to help you gain critical insights in data preprocessing and data analysis projects, guiding your industry towards the future!
