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A Beginner’s Guide to Data Cleansing: Step by Step

What is data cleaning or data cleansing? The simplest definition is that it is all about making information easier to understand. It is the process of ensuring the data we hold is correct, relevant, ...

What is data cleaning or data cleansing? The simplest definition is that it is all about making information easier to understand.

It is the process of ensuring the data we hold is correct, relevant, and complete. This means removing unnecessary duplicates, updating records, and refining the systems we use to collect data.
As you may imagine, data cleaning can be a monumental task! This is likely the case if you run an established firm and have yet to clean your data silos.
However, there’s no need to worry. You can clean data manually, or even easier, you can use data cleansing software like WinPure. We aim to make the cleaning process swift, accurate, and comprehensive.
Let’s take a look at the key steps you need to know when cleaning your corporate data for the first time.

1. Remove All Duplicates

Duplicate data is a crucial cleanliness concern. The bigger we build our data silos, the harder it can be to spot duplicate information.

To start managing this side of your data, you’re going to need to choose an import tool. There is a handful out there, but the aim is to bring all your data pools into one whole.

Once your data imports, you need to cross-reference files that cross over. For example, you may have two patient records for the same person or address. If you sort and filter by name or patient record number, you may spot duplicates easier.

However, this can be time-consuming. What’s more, you need to ensure that all relevant details merge into one record. Again, some suites can help with this.

2. Check for Inconsistency

Consistency, too, is an essential measure in data cleanliness. This means that you will need to ensure all your data capture parameters are working from the same guide. For example, you may have some data captured in upper case, while others will be in lower case. If the same phrases or units miss each other due to case conflicts, you need to establish the default.

This is entirely possible to achieve through simple coding. However, as with any suitable data plan, you should set a clear template beforehand.

Establish your data capture parameters first, and then start sifting through the raw information to fit the bill.

3. Fill in the Blanks

Missing data can seem like a nightmare scenario if you have a well of information to handle. However, starting to diagnose this issue may be as simple as arranging a clear map of the data parameters you need.

Once you have lined up your full dataset and can see which information is widely missing, it’s time to investigate.

Perhaps frustratingly, there can be many reasons why data is missing from records. It may not be relevant, for example. Or, it maybe it was not entered in at the point of capture.

This will require deeper analysis in the long run. However, you may not always need all of the categories in your dataset. Are there any parameters you can safely remove out of irrelevance? What about setting them to 0 or NULL?

This is another area where a detailed data remap will help you. Again, the right software can help you tackle wide-ranging datasets with ease.

4. Normalize Your Data

Normalizing or scaling your data means bringing all your parameters to the same level. At least, this means you should open up your data distribution to see the bigger picture.

Your existing data distribution may prioritize one or two parameters over another. Your datasets may even treat one parameter with the same priority as something completely irrelevant. With that in mind, you need to ideally ‘undo’ these refinements if you need a deep clean.

Through data cleaning and remapping, you may decide to switch priorities when it comes to parameters. Therefore, it makes sense to level the field! Normalized data is generally easier to work with.

Ultimately, this stage in proceedings is rather like untangling your data. It’s essential to lay out what you need to clean so that it is flat and visible before fine-tuning.

Why Use Data Cleansing Software?

The above points in data cleansing seem straightforward enough on the surface. However, without specialist tools and software, you are approaching a lot of manual labor.

The most efficient way to re-organize and clean your data is to use leading software such as WinPure. Our platform enables you to untangle, re-prioritize, and weed out data, ready to transfer to a singleArticle Search, unified system.

Want to know more? Take up WinPure Clean & Match for a free demo now or get in touch with our team.

Source: Free Articles from


Darren Wall has been producing business and tech pieces for B2C and B2C publications for twenty years; he’s among the top-rated in his field. He’s also a content consultant and a creative entrepreneur who loves giving start-up, planning, and process advice.

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