Why Is Data Cleansing Important? A Comprehensive Guide to Benefits, Implementation, and Tool Selection

What Is Data Cleansing?

Data cleansing refers to the process of organizing, converting, and processing data into a format suitable for analysis by addressing errors, noise, duplicates, missing values, and outliers. Data analysis generally follows a process starting with "Data Collection," followed by "Storage," "Extraction," "Transformation," "Visualization," and "Analysis." To visualize and analyze data collected and stored in a database, a process is required to extract the necessary information and convert it into an analysis-ready format.

Raw data stored in databases often lacks uniform formatting or granularity and typically contains "dirty data," such as corrupted, inaccurate, or duplicate entries. To improve the accuracy and speed of data analysis, the process of removing errors and noise, as well as converting and processing the data—known as "Preprocessing"—is essential. Data cleansing is a specific step within preprocessing and is also referred to as "Data Cleaning" due to its nature of handling missing values and outliers.

The Importance of Data Cleansing for Accurate Data Utilization

In today's era of information explosion, a critical management challenge for companies is how to effectively utilize the ever-increasing volume of data within their business domains. However, data managed across various departmental business systems is often not ready for immediate use in data analysis. For example, to visualize and analyze data using BI tools, it is necessary to store structured data—prepared through preprocessing—in a data warehouse and then transmit it to the BI tools.

Storing unstructured data in a data warehouse is not straightforward. Data with missing values or significant outliers can lead to reduced accuracy and slower analysis speeds. Preprocessing is essential to maintain data consistency, and data cleansing—specifically the handling of missing values and the removal of outliers—is a mandatory process. By increasing the accuracy of data analysis, companies can achieve product development that captures latent customer demand and high-precision demand forecasting, making data cleansing a vital strategy for enhancing corporate value.

Causes of Dirty Data

There are various causes for the creation of dirty data that necessitates data cleansing. Common causes include registration errors by users, duplicate registrations, and minor inconsistencies in notation depending on the person entering the data. Additionally, there may be a lack of fields required to determine unique data entries. It is important to accurately identify the causes within your own company and implement appropriate countermeasures.

Benefits of Data Cleansing

What benefits does data cleansing bring to an organization when it enables accurate data utilization? Here, we introduce three representative benefits gained from implementing data cleansing.

Operational Efficiency

Data analysis typically follows a process of collecting and storing raw data in ERPs or data lakes, extracting and transforming it using ETL tools to send it to a data warehouse, and then visualizing and analyzing it using BI tools or machine learning. If the collected and stored data contains inconsistencies in notation, duplicates, or missing values, it takes more time to extract and visualize, and the accuracy and reliability of the data analysis are compromised.

By forming consistent, structured data through data cleansing, you can streamline and accelerate the transmission to data warehouses and visualization via BI tools. Furthermore, removing data errors and noise not only contributes to improved analysis accuracy but also significantly reduces the operational burden on departments specializing in data analysis. This allows resources to be reallocated to core tasks that directly improve business performance, leading to a comprehensive strengthening of the management foundation.

Improved Accuracy in Data Analysis and Decision-Making

In the modern era, digital technology is advancing at an accelerating pace, and markets are maturing alongside technological development. Consequently, the demands of customers and general consumers are becoming more sophisticated and diverse. To secure a competitive advantage, companies must formulate business plans that capture latent market demand. Logical decision-making based on quantitative data analysis is essential to uncover these latent customer needs and nurture them through appropriate approaches.

For example, analytical methods such as "3C Analysis," "4P Analysis," and "PEST Analysis" are used when formulating business plans and marketing strategies. To ensure the reliability and efficiency of these methods, it is necessary to have accurate datasets with minimal information gaps or biases, rather than simply collecting and storing data. Data cleansing helps improve the accuracy of data analysis and decision-making by organizing data to remove duplicates, noise, discrepancies in granularity, and inconsistencies in notation.

Strengthening Customer Relationships and Competitiveness

In today's market, which is saturated with products and services due to market maturation, consumption trends are shifting from "product consumption" to "experience consumption." It is becoming difficult to differentiate from competitors by appealing solely to functional value. To develop sustainably in such an era, companies must strengthen relationships with prospective and existing customers and provide unique added value that competitors cannot offer. No matter how much technology advances, the foundation of business remains human relationships, and business activities are built upon relationships with customers.

Creating products and services that customers desire requires a process of analyzing prospective customer attributes, purchasing behavior, and latent demand from multiple perspectives. Data cleansing contributes to the realization of optimized approaches for each individual customer by accurately identifying latent demand through enhanced customer analysis accuracy. For example, regularly cleaning customer data stored in a CRM minimizes information gaps and duplicates, which leads to building better customer relationships and helps provide unique added value that competitors cannot match.

How to Proceed with Data Cleansing

The data cleansing process is generally carried out based on the following three steps.

1. Data Collection
2. Discovery and Formatting of Notation Inconsistencies and Discrepancies
3. Organization and Classification for Data Utilization

1. Data Collection

The first step in data cleansing is collecting the data to be analyzed. Relevant information is gathered from raw data within business systems managed by various departments, such as ERP, CRM, core systems, DBMS, file servers, and data lakes. Since data managed in departmental systems is often siloed and varies in format and granularity, it is common to use a data integration platform or ETL tool to manage it on a single platform. This process also helps in understanding the current state of the data held by the organization.

2. Discovery and Formatting of Notation Inconsistencies and Discrepancies

The next step is to prepare the imported data by addressing errors, noise, missing values, and outliers so that it is ready for analysis. For example, this involves identifying and processing issues such as inconsistent use of half-width and full-width characters, inconsistent currency symbols (e.g., "Yen" vs. "¥"), duplicate customer registrations, missing input fields, and incorrect notations (e.g., different ways of writing corporate suffixes). It is necessary to establish clear rules—such as filling in missing values with averages or medians, or using existing datasets for predictive models—and perform data cleansing based on those standards.

3. Organization and Classification for Data Utilization

The final step is the process of organizing and classifying the formatted data with strategic utilization in mind. For example, if you are performing a 3C analysis to create a new business, you must analyze the growth potential of the market you are considering entering, the market share of competitors, and the strengths and weaknesses of your own products from a broad perspective. Furthermore, to utilize customer information for marketing analysis, "data matching" (merging) is required to integrate customer information scattered across multiple databases. In this way, you organize and classify the necessary data according to objectives and departments, arranging it into a format that is easy to utilize within the business domain.

Challenges in Data Cleansing and the Use of Tools

A challenge often cited regarding data cleansing is the time and effort required for the process. As mentioned earlier, we are in an era of information explosion, and the strategic utilization of big data has become a critical management issue for companies. However, it is said that preprocessing, including data cleansing, accounts for 70% to 80% of the total time spent by data analysis teams, and it requires deep knowledge of statistical analysis and machine learning. Therefore, since the challenge lies in how to rationalize and streamline the data cleansing process, it is necessary to consider the introduction of solutions such as data integration platforms, ETL tools, and RPA.

Selection Points for Data Cleansing Tools

Executing data cleansing itself is not the goal; it is a means to promote the accuracy and speed of data analysis. Therefore, when introducing tools for the purpose of automating or reducing labor in data cleansing, you must consider your company's management situation and business model, and select tools from the perspective of how the data will be utilized after cleansing. Specifically, you need to select solutions based on the volume and quality of the corporate information you hold and the frequency with which that information is updated. Another important point is what kind of items the tool can supplement in addition to company names and phone numbers. It is important to consider the cost-effectiveness and plans of the solutions and select one that is suitable for your organization's structure.

Data cleansing is an information processing process that handles missing values and outliers in raw data to convert it into a format suitable for analysis. By removing data noise and errors, you can improve the accuracy and speed of data analysis, enabling decision-making that does not rely on ambiguous factors such as intuition or experience. To build a data-driven management structure, please consider working on the efficiency of your data cleansing processes.