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
The data analysis process—consisting of "collection," "storage," "extraction," "transformation," "visualization," and "analysis"—typically follows a path of collecting raw data into an ERP or data lake, 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 variations 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.
If consistent, structured data can be formed through data cleansing, it leads to more efficient and rapid transmission to data warehouses and visualization using 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 concentrated on core tasks that directly lead to improved performance, thereby strengthening the overall management foundation.
Improved Accuracy in Data Analysis and Decision-Making
Digital technology is advancing at an accelerating pace, and markets tend to mature 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. To uncover these latent customer needs and nurture them through appropriate approaches, logical decision-making based on quantitative data analysis is essential.
For example, analytical methods used when formulating business plans or marketing strategies include "3C Analysis," "4P Analysis," and "PEST Analysis." To ensure the reliability of these analytical methods while increasing efficiency, it is necessary to have accurate datasets with minimal information gaps or biases, rather than just collecting and storing data. Data cleansing helps improve the accuracy of data analysis and decision-making by organizing data to remove duplicates, noise, differences in granularity, and variations 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," making it difficult to differentiate from competitors by appealing solely to functional value. For a company to develop sustainably in such an era, it 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.
To create products and services that customers desire, a process of multifaceted analysis of prospect attributes, purchasing behavior, and latent demand is indispensable. Data cleansing contributes to the realization of approaches optimized for each individual customer by accurately identifying latent demand through enhanced customer analysis accuracy. For example, if customer data stored in a CRM can be cleaned regularly, it minimizes information gaps and duplicates, leading to the construction of good customer relationships and helping to provide added value that competitors cannot match.
How to Proceed with Data Cleansing
The data cleansing process is basically carried out based on the following three steps.
1. Data Collection
2. Discovery and Formatting of Notation Variations and Inconsistencies
3. Organization and Classification for Future Data Utilization
1. Data Collection
The first step in data cleansing is the collection of data to be analyzed. Relevant information is collected from raw data within business systems stored in each department of the organization, such as ERP, CRM, core systems, DBMS, file servers, and data lakes. Since data managed in each department's business system is often siloed and varies in format and granularity, it is common to manage it on a single platform using a data integration foundation or ETL tools. This process also leads to a better understanding of the current state of the data held.
2. Discovery and Formatting of Notation Variations and Inconsistencies
The next step is to prepare errors, noise, missing values, and outliers in the imported data for analysis. For example, processes such as grouping, unifying, converting, and replacing are applied to address issues like inconsistent use of "half-width" and "full-width" characters, inconsistent currency symbols (e.g., "Yen" vs. "¥"), duplicate customer information, missing data due to input omissions, and incorrect notations (e.g., different ways of writing "Incorporated"). It is necessary to establish certain rules, such as supplementing missing values with averages or medians, or using existing datasets for predictive models, and to perform data cleansing based on those standards.
3. Organization and Classification for Future Data Utilization
The final step is the process of organizing and classifying the formatted data with strategic data utilization in mind. For example, if performing a 3C analysis for the purpose of creating a new business, one must analyze the growth potential of the market being considered for entry, the market share of competitors, and the strengths and weaknesses of one's own products from a bird's-eye view. Furthermore, to utilize customer information for marketing analysis, "data consolidation" (Meishi Sonar) is required to integrate customer information scattered across multiple databases. In this way, necessary data is organized and classified according to purpose and department, and formatted into a structure that is easy to utilize in business areas.
