Data cleaning is a term often used similarly to data cleansing. What are the differences between the two?
In conclusion, data cleansing is sometimes referred to as data cleaning, and there is no difference in meaning between the two. Data scrubbing is also synonymous with data cleansing.
Data matching is sometimes considered a part of data cleansing, and the two are often confused; however, each process has a different purpose. While data cleansing is the process of eliminating data defects and inconsistencies to improve data quality, data matching refers to the process of resolving duplicate registrations and integrating multiple data sets.
When integrating company-wide databases for data utilization, if the same company or customer exists as a duplicate in each department's database, you may end up repeating the exact same approach to the same entity, which could lead to resentment or a loss of corporate credibility. To prevent such situations, the process of data matching—which assigns IDs to attribute data such as company or customer names and addresses to identify and integrate the same entities—is essential. However, since variations in registered data notations can reduce the accuracy of data matching, it is crucial to complete the data cleansing process beforehand.
Why Is Data Cleansing Necessary?
Why is data cleansing considered necessary for data utilization? It is important to fully understand the benefits and significance of performing data cleansing.
Improve Analytical Accuracy for Data Utilization
Using unorganized data inevitably reduces analytical accuracy. In customer databases, in particular, issues such as outdated information, missing values, or duplicates are problematic. Analyzing noisy data prevents the derivation of accurate results, making it impossible to grasp the true state of affairs. By resolving data deficiencies through data cleansing, you improve data quality and, consequently, analytical accuracy. Since marketing initiatives can be implemented based on highly accurate analytical results, the likelihood of achieving your expected outcomes will also increase.
Enhance Operational Efficiency
If registered data contains duplicates or inconsistent formatting, database searchability decreases. Furthermore, utilizing flawed data for analysis may lead to the need for rework later. Extracting and correcting problematic data on an ad-hoc basis is inefficient and leads to time loss, as operations are interrupted during the process.
Data cleansing is essential to eliminate such wasteful tasks and streamline operations. When data within a database is consistently organized and integrated, you can retrieve necessary information immediately and avoid the need for analytical rework, thereby boosting productivity. Furthermore, employees previously responsible for manual data correction can reduce their workload and focus on their core responsibilities, which also leads to a reduction in labor costs.
Reduce Data Management Costs
Operating a database incurs fixed costs. When flawed data accumulates, it unnecessarily consumes server capacity, leading to excess costs. By organizing data through data cleansing and integrating it through data matching to remove unnecessary entries, you reduce the load on your servers and save on operational costs.
Prevent Data Quality Degradation
There are various reasons for data quality degradation, one of which is the lack of unified data entry rules within a company. If each department inputs information from various media using its own unique methods, the database will become scattered with data in inconsistent formats. Just as internal servers require regular maintenance, data also requires ongoing maintenance to preserve its quality. By setting a frequency for data cleansing in advance, you can prevent data quality degradation and ensure that reliable data is available for use at all times.
Two Methods for Implementing Data Cleansing
There are two methods for implementing data cleansing. Choose the approach that best suits your company's situation.
Using Internal Resources
If the volume of data is small, you may choose to utilize your company's internal resources. While having employees with extensive data knowledge can help streamline the process, correcting data generally does not require specialized knowledge or skills and can be performed manually. Handling this in-house has the benefit of saving costs associated with outsourcing.
On the other hand, as the volume of data increases, the work becomes more complex, which increases the operational burden on employees who must manage data in addition to their core duties. This can lead to more errors and oversights, reducing not only data quality but also overall operational efficiency. Additionally, if departments operate separate databases, the sheer volume of data makes it unrealistic to rely solely on internal resources.
Using Data Cleansing Tools
If your company lacks sufficient internal resources or handles a massive volume of data, you should consider using a data cleansing tool. These tools allow you to cleanse large volumes of data efficiently. By automating tasks that were previously performed manually, you can reduce human error and organize data more accurately.
While there are costs associated with implementing and using these tools, you can expect significant reductions in human labor costs and time compared to performing the work with internal resources.
[Reference Article] How to Perform Data Cleansing: Methods, Procedures, and Key Considerations
Frequency of Corporate Information Updates
The frequency of corporate information updates is also a critical point.
Corporate information, such as company names and addresses, changes for various reasons, including office relocations and corporate mergers or acquisitions. Continuous data maintenance and updates are necessary for accurate data utilization. If data is updated appropriately, high data quality can be maintained. While update frequency varies by tool—with some updating monthly, weekly, or even daily—a higher frequency is not always better, nor is a lower frequency always worse. The necessity of updates depends on the nature of the data. If the data changes rapidly and requires caution during use, a higher update frequency is preferable. The important factor is whether the tool can detect these changes and perform updates accordingly. When implementing a tool, consider the nature of your data and determine the ideal update frequency.
Implementation Costs
Before full-scale implementation, be sure to calculate the costs thoroughly. If your company handles a small volume of data, free tools might suffice. However, paid tools generally offer more features and additional options. If you handle large volumes of data and require comprehensive functionality and security measures, we recommend using a paid tool.
Since some tools do not disclose pricing on their websites, you should inquire directly to request a quote.
