• Develop a data cleansing strategy
  • Decide on a standard method of entry for new data
  • Validate data accuracy and remove duplication
  • Fill any gaps of missing data
  • Create an automated process going forward

Data cleansing is a fundamental aspect of knowing what data you retain and using it most effectively. The process of cleansing reduces the potential for error and enhances data reliability. Here's how to get started.

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Data Cleansing - Five Best Practices

Here are five steps you can take to cleanse your data. Think through these steps and how they apply to your organization. The objective is to get the best use out of your data, without errors or inaccurate information.

(1) Develop a data cleansing strategy

Start with strategy. This is a discovery and planning step. Understand the data you have and where it is coming from. Then, determine how you ultimately want to use that data.

Once you know this in broad strokes, select a small segment of data and clean it. This will give you a picture of what you need to create a standard process for all of your data. Once you have that process, make it uniform and document it, so others in your organization can follow the same procedure.

(2) Decide on a standard method of entry for new data

Know where your data is coming from. Eliminate any haphazard or inconsistent ways of bringing data into your pipeline. For example, if you keep customer information in more than one location, create a single route for that customer information to move into your database.

Let others know about your database entry procedures. This prevents data errors that may be created through miscommunication or task overlap.

(3) Validate data accuracy and remove duplication

Understand how your data is structured and know its source. Your data may come from legacy systems, new business activities, or a host of other sources. Know where your information comes from. Then you can more closely analyze the data to remove items that are old, out-of-date, no longer relevant, or erroneous.

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Look closely for duplication. Remove duplicate sources of data. Depending on how you use your data, duplication may or may not affect customer relationships. Duplicate records, however, will be a barrier to reliable analytics. At its simplest, duplicate records throw off the raw numbers you have to rely upon -- from the number of customers to sales volume.

(4) Fill any gaps of missing data

Data cleansing sometimes means adding data. That means filling in what you don't know about the subjects in your database but should. For example, you may need to know the location of your e-commerce customers to ensure you comply with local privacy laws.

You can identify what gaps you need to fill when you sketch out your data cleansing strategy. Look at everything to determine where the gaps are -- laws you need to follow, business activities you need to complete, and other responsibilities you hold when it comes to your data.

(5) Create an automated process going forward

If data will be streaming in on a regular basis, you want to ensure your data cleansing processes can keep up. An automated, repeatable process can assist with this. You may need to modify your data cleansing for particular projects or as your business activities scale and evolve.

Tools for Data Cleansing

After developing a strategy for data cleansing, you can get started on choosing tools to do the job.

If the data ends in a database, use some database-layer cleansing tools. If the data starts in a database, use the CAST or CONVERT functions within the SELECT-clause to convert data types. 

If the data will start and end in an Excel file, making a Macro to cleanse your data may be the best solution since the whole process of cleansing and analyzing data stays within the same tool.

If the data is used, or transformed, in Python or R, it is sometimes advisable to do data cleansing in that same language. This avoids context-switching as much as possible. In addition, all end users will use the same software to process the data, preventing usability issues.

Other times, it will be easier to use an out-of-the-box solution for data cleansing.

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Data cleansing is fundamental, but it is just one element of your organization's data pipeline. The ETL and ELT solution Xplenty makes it easy for you to make the most of your data, turning your collection of information into meaningful business analytics. Schedule a demo today with Xplenty to learn how this solution elevates your data's usability and functionality.