The benefits and necessity of data cleansing, essential for data-driven businesses

Data accumulated in production management systems, sales management systems, etc. is an important indicator for making management decisions and formulating sales strategies. However, in order to utilize this data as usable data, data cleansing is required.

Once cleansed, the data can be visualized and used as live data.

BI (Business Intelligence) is effective in visualizing data and utilizing it for management decisions. Please read this article for an explanation of BI.

What is Data Cleansing?

Data cleansing refers to the process of extracting items from the information stored in a database that contain duplications, typos, or inconsistencies in expression, and then correcting or deleting them to optimize the information.

Companies typically hold a variety of data, such as customer lists, membership lists, and product lists. However, it is rare for this data to be organized in an orderly manner according to set rules, and there are many cases where the same data is duplicated, there are incorrect notations, or the same items are notated differently. This is particularly common in databases for large companies where information has been added over many years, and if left as is, it can interfere with data management and risk causing unnecessary trouble.

In addition, these databases are sometimes used as a basis for business decisions and as a reference when formulating sales strategies. However, incomplete data that has not been cleansed can lead to incorrect decisions and misunderstandings, which can ultimately cause significant damage to the company. Data cleansing is an important task in order to avoid such problems, and it is necessary to check whether the data has been cleansed before using it.

The methods and actual process of data cleansing are explained in detail in the following article. Please also take a look.

What is "name matching" in data cleansing?

You may have heard of the term "name aggregation" as a step in data cleansing. This is the process of combining duplicate items in the data into one, and is one of the most important steps in data cleansing.

As mentioned briefly in the previous section, it is not uncommon for data to be duplicated in a database. Taking the fictitious company "XYZ Engineering Co., Ltd." as an example, its customer list might contain the following:

XYZ Engineering Co., Ltd.
XYZ Engineering Co., Ltd.
XYZ Engineering Co., Ltd.
XYZ Engineering Co., Ltd.
XYZ Engineering

These include differences in pronunciation, katakana spelling, English spelling, whether to use a dot, or simply entering the abbreviation as it is normally called. There are also cases where a company that was formerly called "XYZ Engineering Co., Ltd." has merged and changed its name, and if the old and new company names are on the same list, this will result in duplicate data.

To the human eye, these are the same company names, and it is clear that they are just spelled differently, but when operated mechanically as a database, they are recognized as different companies.For this reason, when operating a database, it is necessary to establish certain spelling rules and carry out name matching work to correct and delete duplicate data in accordance with those rules.

This name matching can be said to be the main task of data cleansing, and due to its importance, many people think that "name matching = data cleansing." However, while name matching is the process of eliminating duplicate items, data cleansing is generally used as a broader term that includes tasks such as eliminating data errors and spelling variations, and in some cases filling in missing data.

This name matching process can be done manually in some cases if the number of items is around 100. However, when the number of items reaches thousands, it is impossible to do this manually, so it is common to use dedicated tools to make the corrections mechanically.

How does "coarse" data come about?

Why does "rough" data that requires data cleansing come into existence? Let's look at some examples of cases that can occur in daily work.

Lack of uniform rules for data entry

The most likely cause of duplicate data is when there are no rules for how data is entered in the first place. For example, customer management data shared by the sales department is likely to be entered by each sales department employee individually for their own customers. In this case, if Person A enters "Inc." as "Inc.", while Person B, wanting to save time, enters "(Inc.)," this alone will result in data with inconsistent notation.

In addition to company names, there are countless possible reasons for inconsistency in notation, such as whether to use a full-width or half-width space between a person's first and last name, whether to use hyphens in telephone numbers, whether to use the "〒" symbol in postal codes, whether to enter street addresses in full-width or half-width numbers, etc. To prevent such situations, it is necessary to establish notation rules when operating a database and regularly cleanse the data based on those rules.

Integrate data managed by multiple people and departments

Integrating multiple data sets is another situation where inconsistencies in data notation are likely to occur. For example, when it comes to notation of postal codes, one department may enter the seven digits as "0000000" because they expect the data they enter to be linked to another system and used, while another department may enter it as "000-0000" because they expect the Excel data to be printed directly onto address labels for mailing.

The purpose of creating a database and how it will be used will differ depending on the department. Depending on the purpose, the way items are written and the type of information to be entered may change, so simply integrating all of the customer data within the company can lead to inconsistencies in notation.

The risks of using "coarse" data

Why is data cleansing necessary? Let's consider the risks involved in using "rough" data as is, such as data with inconsistent notation and duplication.

Decrease in work efficiency

The first risk that comes to mind when using uncleansed data is that it can reduce business efficiency. Databases maintained by companies are likely to be used in a variety of business situations. These can be used for a variety of purposes, such as sending direct mail to registered users, creating a list of key products from a product list, or as a master when building a web database.

However, if the data is not cleansed, these tasks require time and effort, such as formatting the data and eliminating duplicates.If this kind of time and effort is incurred after introducing digital technology to improve business efficiency, it can be said that this is counterproductive.

Decreased analytical accuracy

Corporate databases are also used to analyze market and customer trends when making management decisions and formulating sales strategies. Data such as what products are selling to which demographics, and how many potential customers are likely to purchase products in the future, are all important pieces of information that can determine the success of a business.

However, if the data used for this analysis is inaccurate or there is a lot of duplication, it will be impossible to make accurate decisions. For example, if you estimate there are 100 potential customers, but there are many duplicates and in reality there are only 30... If this happens, you may even need to reconsider your sales strategy from scratch.

Damaging customer confidence

Inaccurate data can also affect a company's credibility. When documents such as contracts and invoices are sent to customers by mail or courier, they are usually sent based on a customer list maintained within the company. However, duplicate data or incorrect spelling can lead to problems such as sending documents with the wrong name or even important documents not being delivered to the recipient. In today's world where stronger security and compliance are emphasized, such situations can result in significant losses for a company.

Data cleansing is essential to utilize data for business purposes

The data that companies accumulate in the course of their daily operations can be used for more detailed marketing activities, customer follow-up, and in some cases, can even lead to new business ideas, making it extremely important for companies. In order to effectively and efficiently utilize this data, it is essential not only to accumulate it but also to maintain it through data cleansing. Companies that are particularly interested in linking data to sales should consider undertaking data cleansing and name matching.