Decision-making within a company must be made based on accurate data, to obtain equal results. For this reason, performing a data cleansing is something of the utmost importance for any large organization. It is not simply about having well-ordered information, but rather that it must be clear, precise, and consistent.
In this article, you can see what this cleaning refers to and what is its importance for any organization. In addition, you will know the procedure to carry it out, as well as how to obtain quality data. Next, you can observe all these aspects:
1. What is data cleansing?
2. Why is data cleansing important for large organizations?
3. What are its benefits?
4. How is data cleansing done?
5. What is the difference between a clean database and a “dirty” one?
6. How can we help you with data cleansing?
1. What is data cleansing?
Data cleansing is the process of purging the database of an organization. Through this process, you can repair or remove corrupted, incorrectly formatted, duplicated, or incomplete data. This is because when you combine data from different sources, errors like the ones mentioned above occur most of the time.
Therefore, this procedure saves your organization a lot of time, as well as having more reliable data. Also, you will be sure that any process you carry out with that data will give you accurate results. However, there is not a specific way to do the cleanup process as it varies between databases.
2. Why is data cleansing important for large organizations?
This is a vitally important procedure within any organization, large or small. As mentioned above, it allows you to make decisions based on data you can trust. This is due to the amount of information that a company has, in addition to the fact that it comes from different sources.
Every business must ensure that the information in its database is secure and organized. All this information refers to employees as well as customers. Therefore, all of this data must be accurate and reliable for decision-making to be equal.
Likewise, the importance of having a clean database is that it can help you increase productivity in your business. You can achieve this because outdated and incorrect data will be out of your database. In this way, your employees will not have to deal with unnecessary data and will make better use of their working hours.
Another aspect that makes cleaning data important is that it reduces expenses. You will achieve this because errors will disappear, which saves unnecessary expenses.
3. What are its benefits?
The benefit of performing data cleansing in your business is that it will help you improve efficiency and organization. In addition, you will also get high-quality information when making decisions. Among the benefits of this cleaning are:
- Through this procedure, you can make your employees more efficient, since they will be able to acquire the data more quickly when they are needed.
- You will get more satisfied customers, as well as less frustrated workers thanks to the elimination of errors.
- Another benefit is that there is a better understanding of the objectives you want to achieve. Since you will know what to do with the data and where it comes from.
- With better data quality, the decision-making process is easier.
- When you do the process of cleaning dataproperly, it shows quality information. In this way, the needs are clear, leading to better efficiency and productivity.
- Thanks to the correction of errors and the knowledge of the source of it, the next process will be simpler.
4. How is data cleansing done?
There is no set process for data cleansing, as this varies by data type, quality, and more. However, you can guide with the procedure below:
4.1 Remove irrelevant or duplicate data
Duplicate data collection occurs for two specific reasons: inconsistency in data entry and multiple data sources. Thus, when you combine data from various sources, there is a high chance of causing duplicate data. The deduplication process is one of the largest areas of cleaning.
Irrelevant data, on the other hand, is the one you think does not fit the problem you are analyzing. This does not mean that they do not serve as data, but rather that they do not provide solutions in particular cases. Therefore, cleaning your database of duplicates and irrelevant data will help you maximize efficiency by minimizing distractions.
In addition, this cleansing allows you to create a more manageable database, and also to interpret it in a better way. Of course, you can do this cleaning manually, but there is specialized software for it. The latter will save you time, as well as effort and expense.
4.2 Implement a verification system
To avoid the mistakes mentioned above, implementing a comprehensive system in your organization is ideal. In this way, you can be sure that the data will be entered correctly from that moment on. For example, this system helps you to verify that the information fields entered to have the same format.
Likewise, requiring complementary data to be mandatory will prevent there from being missing fields and, from containing the same information. Thanks to this, data entry errors are reduced to facilitate the next cleaning process of your data.
4.3 Filter data out of series
In this case, it is data that does not fit into the classification you are currently reviewing. Of course, you should not delete or modify data lightly, but rather determine if it is not relevant. These can be misguided in its category, errors in the structure, among other aspects that make it differ in a class.
However, on some occasions, these data may reveal significant aspects of a theory that you want to test. Therefore, this is not to say that they are wrong all the time, but rather that you have to establish their validity. Here is the importance of this step, filter them to see if there is any relevance or if it is an error.
4.4 Update data constantly
It has been shown that most data becomes stale when done manually. This, to a large extent, is due to changes within the organization itself, among other reasons. These movements lead to updating emails, charges, telephone numbers, so there will be obsolete data.
For this reason, the implementation of data update methods is one of the important points of data cleansing. One of these tools can analyze entered emails and update this type of information when necessary. Also, if there are changes in a position in the company, the implemented system can perform the update automatically.
Spam can cause the existence of unwanted elements within your database. Therefore, a system should be used that eliminates unused or discarded emails. In this case, email marketing tools are ideal for solving this, so you do not have outdated or irrelevant data.
4.5 Handle lost data
One of the serious problems in the manual creation or updating of an information base is lost data. This occurs due to unawareness, poor verification systems, or human errors. Thus, at the time of data cleansing, you will surely find missing information, something that cannot be simply ignored.
The automation of the data management process requires that these are complete. This is because an algorithm will not work correctly if there are missing fields, as the data output will not be reliable. Also, the program may demand the inclusion of these missing data, which represents a big problem.
There are a few ways to solve the missing data problem, although they are not necessarily optimal, but should be considered. One of them is that when you find some missing data, you can discard it. However, this will cause you to lose information, so you must be thorough in this regard.
Another way to deal with this is through data entry based on previous observations. However, you may find assumed and unverified information, compromising the integrity of the data. This will not necessarily be the case, but you must be very careful as well.
A solution that can be effective is to use the data differently, to avoid the data that is not there affecting the result. All these aspects must be carefully analyzed to obtain the most reliable results.
4.6 Validate the results
The data validation process will tell you if your database is clean, or if you need to correct certain points. An exhaustive examination must be carried out to be able to make sure of the above.
One of the important points is to verify that the data left makes sense. This means that there are no inconsistencies, in addition to the fact that they serve the objective pursued. This must be done from the beginning of the cleaning, following the aspects mentioned in the previous sections.
Likewise, you must be sure that the data follows the established rules of your field, so that there are no errors. Another aspect is whether they provide clarity about the working theory you want to test or the problem you want to solve. Also, if the data helps you think about the next decisions to be made within your organization.
If the database does not match most of these points, it means that there is still “dirty” data in your database. If so, you may get inconsistent results, or even reach the wrong conclusions. This causes the quality of decisions to be inadequate, affecting the organization and the employees.
5. What is the difference between a clean database and a “dirty” one?
Knowing how to identify when the database of your organization needs cleaning is important. There are a series of characteristics that show the need to debug it. Among them are:
- It is wrong. This occurs because when the data is entered into the system, it is done incorrectly or with errors. It is worth saying that these errors happen for both human and technological reasons. In both cases, they happen accidentally, so establish a data validation.
- Inconsistency. People tend to express ideas in different ways, however, in a database, this is not acceptable. Although the data is true and falls within the classification, it must have the same format.
- It is messy. Data cleansing is also required when data is good but scattered across multiple sources. Therefore, the ideal is that they all come together in a central database.
In addition to analyzing each of the points mentioned, to have a clean information base, you must be sure that the data is of quality. You can achieve this by attending to the following features:
- Consistency: all records of the same class must match in type and structure.
- Accuracy: the data must be precise, to avoid inconsistencies.
- Uniformity: adjusted to the same type of measurement.
- Validity: they must serve the purpose for which they will be used.
- Integrity: the data must be complete.
6. How can we help you with data cleansing?
Data cleansing for large organizations must be constant, to ensure accurate results. The benefits that you will obtain are multiple; among them is the saving of time, effort, and costs. Also, make sure you anticipate errors, inconsistencies, and other inconveniences by training your staff for this purpose.
At PEO Middle East, we provide you with the necessary advice so that you can carry out this cleaning in your database. In the same way, we offer you recruitment services in the UAE, to simplify this process. Also, you can check our insights to learn about other topics that will help you improve your business.
Do you want to learn more about why data cleansing is important to big organizations? You can contact us through the email [email protected]. In the same way, through the telephone number +97143316688, we can answer any of your questions.
Likewise, if you want to work with us, you can send us an email at [email protected]. Similarly, by entering the thetalentpoint.com, you can send us your resume and we will contact you.
