Data cleaning is a term often used interchangeably with data cleansing. What are the differences between the two?
In conclusion, data cleansing is also referred to as data cleaning, and there is no difference in meaning between the two. Furthermore, data scrubbing is synonymous with data cleansing.
Data consolidation is sometimes considered part of data cleansing, and the two are often confused; however, each process serves a distinct purpose. While data cleansing is the process of improving data quality by eliminating inconsistencies and errors, data consolidation 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 various departmental databases, you may inadvertently repeat the exact same approach to that entity. This can lead to negative feedback or a loss of corporate credibility. To prevent such situations, the process of data consolidation—which assigns IDs to attribute data such as company or customer names and addresses to identify and integrate identical entities—is essential. However, since variations in notation within registered data can reduce the accuracy of consolidation, it is crucial to complete data cleansing 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.
Improvement in Data Analysis Accuracy
Using unorganized data inevitably reduces the accuracy of analysis. In customer databases, in particular, issues such as outdated, missing, or duplicate data are problematic. Analyzing noisy data prevents you from deriving accurate results, making it impossible to grasp the actual situation correctly. By resolving data deficiencies through data cleansing, you improve data quality and, consequently, analysis accuracy. Since marketing initiatives can be implemented based on highly accurate analysis results, the likelihood of achieving your expected outcomes increases.
Enhancing Operational Efficiency
If registered data contains duplicates or variations in notation, the searchability of the database decreases. Furthermore, if flawed data is used for analysis, you may be forced to redo the analysis later. Extracting and correcting problematic data on an ad-hoc basis is inefficient and causes operational interruptions, leading to a loss of time.
Data cleansing is essential to eliminate such wasteful tasks and improve operational efficiency. When data within a database is consistently organized and integrated, you can retrieve necessary information immediately and avoid the need to redo analyses, which is expected to boost productivity. Furthermore, as employees previously responsible for data correction see their task time reduced, they can focus on their core responsibilities, which also leads to a reduction in labor costs.
Reduce Data Management Costs
Operating a database incurs consistent costs. When incomplete or inaccurate data accumulates, it unnecessarily consumes server capacity, leading to excess expenses. By organizing data through data cleansing and integrating it via deduplication to remove unnecessary records, you can reduce the load on your servers and achieve significant savings in operational costs.
Prevent Data Quality Degradation
There are various reasons for data quality degradation, one of which is the lack of standardized data entry rules within an organization. When different departments input information from various sources using their own methods, the database becomes cluttered with inconsistent formats. Just as your internal servers require regular maintenance, data also requires ongoing care to maintain its quality. By establishing a regular schedule for data cleansing, you can prevent quality degradation and ensure that reliable data is always available for use.
Two Methods for Implementing Data Cleansing
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There are two primary methods for implementing data cleansing. Choose the approach that best suits your company's specific situation.
Using Internal Resources
If you handle a small volume of data, you may choose to utilize your own internal resources. If you have employees with extensive data knowledge, they may be able to perform the work efficiently. However, data correction generally does not require specialized skills and can be done manually. Handling this in-house has the benefit of saving on external outsourcing costs.
On the other hand, as data volume increases, the work becomes more complex. A significant disadvantage is the increased operational burden on employees who must manage data in addition to their primary responsibilities. This often leads to more errors and oversights, which can degrade not only data quality but also overall operational efficiency. Furthermore, if different departments operate separate databases, the sheer volume of data makes it unrealistic to rely solely on internal resources.
Using Data Cleansing Tools
If your internal resources are insufficient or if you are handling massive amounts of data, you should consider using a data cleansing tool. These tools allow you to cleanse large volumes of data efficiently. By automating manual tasks, you reduce human error and ensure that data is organized more accurately.
While there are costs associated with implementing and using these tools, they offer significant reductions in human labor costs and time compared to performing the work in-house.
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[Reference Article] What Is Data Cleansing? Methods, Procedures, and Key Considerations
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Frequency of Data Updates
The frequency of corporate information updates is another critical point.
Corporate details such as company names and addresses change frequently due to office relocations, mergers, and acquisitions. Continuous data maintenance and updates are necessary for accurate data utilization. If data is updated appropriately, high quality can be maintained. While update frequencies vary by tool—ranging from monthly or weekly to daily—a higher frequency is not always inherently better. The necessity of updates depends on the nature of your data. If your data changes rapidly and requires precision, a higher update frequency is preferable. The most important factor is whether the tool can detect these changes and update accordingly. When selecting a tool, consider the nature of your data and the frequency of updates required.
Implementation Costs
Before full-scale implementation, be sure to calculate the costs thoroughly. If your company handles a small amount 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 robust functionality and security measures, we recommend using a paid tool.
Some tools do not publish pricing on their websites, so be sure to inquire and request a quote.
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