Data cleaning is a term often used similarly to 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. 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 inaccuracies and inconsistencies to improve data quality, data matching refers to the process of resolving duplicate registrations and integrating multiple data records.
When integrating company-wide databases for data utilization, if the same company or customer exists as a duplicate in various departmental databases, you may end up repeating the exact same approach to the same entity. This can lead to dissatisfaction or even 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 identical entities—is essential. However, since variations in registered data 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 essential to fully understand the benefits and importance of performing data cleansing.
Improve the Accuracy of Data Analysis
If unorganized data is used, the accuracy of analysis will inevitably decline. In customer databases, in particular, issues such as outdated information, missing entries, or duplicates are problematic. Since accurate results cannot be derived from analyzing noisy data, it is impossible to grasp the true situation correctly. By resolving data deficiencies through data cleansing, data quality is improved, and analysis accuracy is enhanced. Because marketing initiatives can be implemented based on highly accurate analysis results, the likelihood of achieving expected outcomes increases.
Streamline Business Operations
If registered data contains duplicates or inconsistent formatting, the searchability of the database decreases. Furthermore, if flawed data is used for analysis, there is a risk that the analysis will need to be redone 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 business operations. When data within a database is consistently organized and integrated, necessary information can be retrieved immediately, and the need to redo analysis is avoided, which is expected to improve productivity. Furthermore, since employees previously responsible for data correction will have reduced workloads, they can focus on their core responsibilities, which also leads to a reduction in labor costs.
Reduce Data Management Costs
Operating a database incurs certain costs. When flawed data accumulates, it consumes server capacity unnecessarily, resulting in extra costs. By organizing data through data cleansing and integrating it through data matching to remove unnecessary entries, the load on the server is reduced, enabling savings in operational costs.
Prevent the Deterioration of Data Quality
There are various reasons for the deterioration of data quality, one of which is the lack of unified data entry rules within a company. If information imported from various media is entered by each department using its own methods, data with inconsistent formats will be scattered throughout the database. Just as regular maintenance is required for operating in-house servers, maintenance is indispensable for maintaining data quality. By setting a predetermined frequency for data cleansing, the deterioration of data quality can be prevented, ensuring that reliable data is always available for use.
Two Methods for Data Cleansing
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There are two primary methods for performing data cleansing. Choose the approach that best suits your company's current situation.
Using Internal Resources
If you are handling a small volume of data, you can utilize your company's internal resources. While having employees with extensive data knowledge may allow for more efficient processing, manual data correction generally does not require specialized skills. 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. A significant disadvantage is the increased operational burden on employees who must manage data in addition to their primary responsibilities. This can lead to more errors and oversights, which not only degrades data quality but also reduces 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 tasks that were previously performed manually, you can reduce human error and ensure 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 relying on internal resources.
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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 data updates is also a critical point.
Corporate information, such as company names and addresses, changes frequently due to office relocations, mergers, and acquisitions. Continuous data maintenance and updates are essential for accurate data utilization. If data is updated appropriately, high data quality can be maintained. While update frequency varies by tool, some update information 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 key is whether the tool can detect these changes and perform updates accordingly. When introducing a tool, consider how frequently updates are needed based on the nature of your data.
Implementation Costs
Before full-scale implementation, be sure to calculate the costs thoroughly. If your company handles a small amount of data, a free tool might suffice. However, paid tools generally offer more features and additional options. If you handle large volumes of data and require advanced functionality and robust 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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