Data Cleansing Best Practices

Posted By :Badal Singh |27th October 2021

Data is what drives the world today. Having the right data at the right time is a factor for companies investing these days. Every decision made by an entity can be based on its failure and success. However, it does not mean that the data is what makes or breaks a business.

Reports indicate that about 94% of B2B companies suspect that they have incorrect data on their website. This begs the question, how confident are you about the quality of the data on your website? Have you ever considered cleaning data?

Suppose for a moment: if the data in your CRM is outdated or irrelevant, what is the ROI, marketing and sales you are missing out on?

Why Imported Data Cleaning?

Cleaning data is important for companies as it improves data quality. Doing so increases the overall efficiency of the company. If you choose to clean up your data you make sure your team does not have outdated or incorrect information, leaving you with the best quality information available in your CRM. Your team ensures that your team does not have to work through a bunch of outdated documents, allowing your employees to make the most of their time.

If you have the right information, it reduces some of the unnecessary and unexpected costs. For example, you could end up printing incorrect information in a company letterhead. Having persistent problems can damage your company's reputation.

So without further ado, let's explore the 8 best ways to clean up data.

1. Know your goals
It is important for businesses to set expectations for their data. One can begin by analyzing and visualizing how your data should look at the end before starting any data processing activities. It is therefore advisable that businesses should establish the objectives of their analysis pipeline and document the information requirement.

It is equally important to know what raw data will look like in the future. It’s really frustrating when unexpected surprises arise during pre-processing when you realize you need to do something different or analytical work to deal with a fluke in the database. To counteract this it is recommended that companies make a small analysis of the information and list the possible confusing and data types - and then strategize accordingly.

2. Establish quality standards
Once all objectives have been set for data processing, you will now need to make sure you are moving in the right direction with data cleaning. It can be done by creating performance indicators for data quality keys (KPIs).

This will help you to develop additional steps in your strategy. The focus should be on how to meet these strategies, track data health and maintain healthy data on a regular basis.

It is important to identify the wrong data and take the necessary steps, such as understanding the source of the data problems in your organization. This helps your data team create a plan to ensure the health of your data.

3. Improve workflow
Data cleaning is a complex process and requires a considerable and well-designed workflow. One of the best ways to create a workflow is to divide your work flow into independent blocks, each with its own set of individual tasks.

here is an example:

Step 1: Find the raw data in question and go to the data repository.

Step 2: Make basic changes to data such as cable cleaning, segment recording, and other simple cleaning tasks.

Step 3: Use the first level integration function to integrate the data and make additional changes to that level.

Step 4: Start the high-level integration where you take the data from the first level integration and integrate it into the advanced level, make some changes and restore the data.

4. Evaluating data
Another important process for data purification is configuration. Setting is basically the process by which you create a protocol to follow the guidelines for what each field / column / parameter should look like (or what is expected). This is done so that you can comply with the data processing pipe.

5. Data verification
If you are cleaning up an existing database, it is also recommended that you set up a real-time verification program This is where data experts start working. Installed with the right data cleaning tool they can clean and validate multiple data points.

To take full advantage of it, you need to create data purification guides. Here are some of the issues that businesses use when cleaning up their existing information:

Required restrictions - required fields that can be left blank.
Data type restrictions - values in the column must be a specific type (numbers, date, text, etc.)
Scope limits - minimum and maximum data set issues.
Different limits - data that can be repeated requires different values (for example, social security numbers).
Membership restrictions - data to be selected from an existing list of options.
Common speech patterns - apply to data with a specific pattern as shown (for example, phone numbers).
Cross-country verification - where the amount of data segments should equal the total.

6. Issuing duplicate records
Duplicate records are another threat to businesses. Businesses end up spending money on routine care and can cause reporting accuracy. Avoiding duplication is important in the data purification process. To ensure that, businesses must need to verify the data and then rub it in order to obtain any duplicate records and delete them.


7. Data aggregation
Once the data is suspended, verified, and filtered for duplication, you are ready to merge. Here's where you can hire data experts. Data professionals can capture data directly from the original company's sites, which are then refined and compiled to provide information that you can apply to your business intelligence and statistics.

8. Process review
It is also important to keep track of the cleaning tasks you perform so that the process can be easily changed and repeated or removed some unnecessary tasks. Various tools are used to monitor actions and help us track them easily to maximize your team's performance.

About Author

Badal Singh

He is a java developer with sound knowledge of frameworks like Spring Boot and Hibernate. He has also tried his hand in Micro services

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