5 Steps of Data Cleaning Process
Cleaning data is hard. It is even harder to maintain the consistency and accuracy of the database once the dataset has been cleaned. However, if you abide by proper data cleaning best practices you will see the desired results for your business.
As we already know, maintaining clean data is one of the most challenging aspects of data cleansing. One typing error can lead you to face a myriad of problems and hours of manual cleansing of data which would have been avoided easily.
Error in the data is inevitable which is why following the data cleaning process is highly recommended for your business. Data cleansing can help you determine the areas where there is a case of weak data and in a need of attention.
The data cleaning process will help you dive in for finding duplicates and fixing other problems within your database from the first planning stage up until the last stage of monitoring the data. You should also keep in mind that this process is a continuous cycle. You should regularly clean the data using these steps for eliminating errors and other issues.
The steps that help to consolidate your database for maintaining clean data are as follows:
1. Data Auditing
The first step of the data cleaning process starts by auditing the data for detecting the contradictions and anomalies with the help of a database and statistical methods. This eventually gives a signal of the characteristics of different anomalies and their locations. You have the option of specifying various kinds of constraints in different commercial software packages and then generate codes that check the validation of these constraints within the database.
Data auditing will help you verify the quality of your database. It provides information that can be used to determine the needs for adopting data management systems and strengthening them by taking appropriate actions.
Auditing the data for quality is important especially if you do not know where to start cleaning your data from. It helps you to recognize the data that is missing, that can be thrown out and that have gaps in between.
2. Analyzing for Cleansing
After you know the priority of the data that your business needs, you will have to identify a set of resources for handling and cleaning the exceptions to your rules manually. The amount of work done manually is directly in correlation to the number of levels of acceptable data quality that you have. Once you put out a list of standards or rules for data cleansing, it will become easier to begin cleaning data.
3. Implementing Automation
Once you begin cleaning your data you should also start standardizing and cleansing the flow of the new data that keeps entering the system by creating proper scripts or workflows in place. In this data cleansing step you can run these scripts or workflows in real-time or in a batch, (weekly, monthly) depending on the amount of data that you are working on. You can apply these routines to the new data or the database that has been keyed-in previously.
4. Appending Data
Appending the missing data is especially important for data records which cannot be corrected automatically. The examples of such records are the email address, phone number, industry type, company size, etc.
This makes it very essential for you to identify the right way of acquiring the missing information from different sources such as third-party append sites, getting in touch directly with the customers or simply by using Google.
You need to be very vigilant about setting up periodic reviews so that you can monitor the issues before they become a major complication. You should not only monitor your database as a whole but also keep a tab on the individual units, accounts, contacts, etc. This will help you track the bounce rates, bounced back emails and the response rates. It is also important to stay updated with the latest developments in the company so that if a customer hasn’t replied to your campaigns in the past 6 months, you can find out if he still holds the same position and is at the same company.
Repeat, the sixth step that you should always follow if you don’t want your database to be useless after some time. Since people’s lives have become dynamic it is more likely that different information associated with them can change frequently. Therefore, you should not think of the data cleaning process as a one-time affair, you should work on it regularly and make it a part of your workflow. Regular sifting of inaccurate data and then updating the customer database is one of the best data cleansing techniques and the only way of maintaining a healthy customer database.
Data cleaning process helps you to kick-start your data scrubbing procedure especially when you don’t really know where to begin. The data cleansing steps are simple and systematic. It is a process cycle that goes:
Audit – Analyse – Cleanse – Repeat
That’s all you need to remember while planning the data cleaning process.
The process not only saves your time and money, which is otherwise wasted on contacting people who are irrelevant for your business but also saves your business reputation. Although a difficult process, data cleansing steps are important and needs dedicated time and resources. With the steps mentioned above, you can certainly create your ideal clean database which will offer different benefits across your business functions and serve as an essential factor in your business growth.