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Data Cleaning

Nowadays data quality is very important for department and company performance. Without correct and real-time data, management is left with poor planning which leads to bad decision-making. Data cleaning or cleansing matters as the most important step to keep your dataset accurate, while also allowing your team to complete their analyses faster and have full confidence in the data. Understanding the best practices for data cleaning and scrubbing also allows you to learn how to manage and transform your data to create an efficient workplace and make effective business decisions.

Why is Data Cleaning Important?

The best synonym you can always remember when you think about data cleaning is quality data. The important questions you need to ask include: How valid is the information in the data collected? Does it fit within your business rules and constraints? How close to the true or correct value is the data? Can you easily confirm that all your data sets are consistent? 

What are the Best Practices for Data Cleaning?

Do not confuse cleaning with transformation – one is data removal versus data conversion. Data cleaning involves deleting incorrect, corrupted, duplicate, or even incomplete data within your database. Although depending on the dataset you have, the cleaning process will look different so it is important to create a standard workflow that helps you ensure you do it right every time.

1. Create a Standardized Process

One of the first steps in developing the right standard to follow is to systematize the data entry processes. Especially when you have more than one data source, it is important to start from zero by removing all the formatting. You also need to make sure that the dataset text is consistent – keep an eye out for mixed capitalization. It is definitely best to keep all data in lowercase.

2. Create a Backup (Duplicate Database)

Before you start any cleaning process, it is common practice to have a backup or copy so that you never lose your original dataset. You can use it as a restore tool, as well as a protection layer in case any critical data might get lost or corrupted during the cleaning phase. We usually recommend full backups but it is as useful to deploy incremental backups, focusing only on the data that has changed since the last backup. The main difference is that with the latter option you should expect smaller, and faster backups.

3. Spell Checking & Fixing

Not fixing misspelling errors can actually affect the analysis and deliver skewed results. Typos will lead you to miss out on key findings from your data, so a simple spell check goes a long way. Extra punctuation around your email address data can lead you to miss out on communicating with your select clients or send unwanted emails.

4. Track Errors

To actually improve the quality of your data, tracking errors is very important. The main reason for this process is to record common mistakes and keep a detailed report to avoid them in the future. Some of these errors can also be conflicting or invalid data. Recording them in an excel sheet can be the initial step which will make it a lot easier to identify incorrect or corrupt data. If you are integrating to other solutions, this will help to make sure errors don’t clog up the implementation.

5. Remove Duplicate Data

Duplicates can cause misrepresentation of data such as inventory or billing and invoice details. Even after you have tracked your errors and still duplicates sneak past this exercise, you have to remove them. To help remove duplicate entries, it is important to also consider standardizing, merging and filtering data practices to sift through your data in detail.

6. Deal with Missing Data

It is important to take proper time to scan your data to locate missing or blank cells, any spaces in the text, or incomplete data. Make sure to determine if any other data is connected to this missing data and then if it should be completely discarded, individual cells entered manually, or left as is.

7. Validate the Data

After your data has been standardized and scrubbed for duplicates, it is time to validate it. This might be a good step in the process to utilize data tools to be able to validate the accuracy and clean your data in real-time. The importance of validating your data is to avoid “dirty” data which can in turn end up in poor business decision-making.

8. Monitor Regularly

Now that you’ve fixed and cleaned up your data, it’s important to keep monitoring it regularly. It is essential to keep the team that uses your data the most to be in the loop regarding the adoption of the new data protocols. Incorporate proper review sessions at least quarterly to catch inconsistencies.

Keeping on top of accurate data inputs is essential to data management. The 8 main steps outlined in this article should help simplify the process to create daily or weekly protocols. You can now confidently move forward using your now accurate and reliable data for true insights into your business and customer, as well as make successful business decisions for the future.

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