What is the Data Cleansing Process? Ultimate Guide

Your outputs are only as good as your inputs. This is a true fact for most anything in life, but certainly for data and insights in business. The data cleansing process makes it possible to trust your insights and gain timely information about your business to make the best decisions.

We’re going to review what data cleaning entails, as well as see how data cleansing tools can help to expedite and optimize the process.

Coming Up

What is Data Cleansing?

What is the Difference between Data Cleansing and Data Transformation?

What are the Steps for the Data Cleansing Process?

What is Data Cleansing vs. Data Cleaning vs. Data Scrubbing?

Why is it Important to Cleanse Data?

What Errors Does Data Cleansing Process Fix?

What are the Characteristics of Clean Data?

What are the Benefits of Data Cleansing?

What are the Challenges of Data Cleansing?

What are the Best Data Cleansing Tools?

Final Words

What is Data Cleansing?

Imagine an office without a filing cabinet or structure to be able to access important documentation. You can see how nothing would get done, and if it did, it would take way too long to do it, and there’d be a risk of using old data.

Data cleansing is the digital way of avoiding such a precarious situation. Data cleansing is the process of editing and fixing data so that when it’s used, you can rest assured by knowing that it’s accurate.

The data cleaning process involves removing duplicates and irrelevant data, as well as transforming data into a similar format so it can be digested by your systems.  

What is the Difference between Data Cleansing and Data Transformation?

There are several steps to get data in the right place to be able to trust the insights it provides. While data cleansing and transformation may be easy to confuse, the two processes are performed for different reasons.

Data cleansing is done to remove any old, irrelevant, or duplicate data. On the other hand, data transformation converts data from one format into another so it can be utilized. Data transformation may also be called data wrangling or data munging.

What are the Steps for the Data Cleansing Process?

Want to use your data to help not hurt your business? Everyone does! That’s why it’s important to follow these data cleansing process steps so you can make sure that your data is in good order.

Take a look:

1. Remove Outdated and Irrelevant Data

The best place to begin is to isolate the questions you’re trying to answer and what data you need to do so. This way, you can take the time to review the data you need to have available. By reviewing your dataset, you can remove any outdated or irrelevant records.

2. Remove Duplicate Data

Another cause for concern when looking at data is duplicates. You don’t want the same information twice as it will affect your outcomes.

The reason for duplicate data, more often than not, is because data is pulled from multiple sources or departments, which can overlap.

That’s why it’s helpful to use an automation solution that can pull your data into a centralized location and cleanse it automatically for you improving accuracy by 90%.

3. Review Structure

Make sure there are no structural errors within your data. For example, these errors could be misspellings, improper capitalization, or inconsistent naming.

4. Look for Missing Data

Are there any missing cells or line items in your data? Human error or incomplete information may cause such an occurrence. If this is the case, it’s important to fill in the missing information so as to not throw off your analysis.

5. Remove Outliers

Outliers are data points that fall way outside the range of your existing data. It can cause your analysis to result in very skewed outcomes. If you find outliers that are glaring, remove them before conducting your analysis.

It also surely depends on the kind of analysis you’re running to determine whether or not you can omit it from the dataset.

6. Validate Data

Last but not least, be sure to authenticate that your data is accurate and high quality. Make sure that you have enough data to use, it’s formatted in a way that your analysis tool can make use of it, and that it’s clean and ready to go!

When you have any volume of data, your goal is to make it useful. Data automation software can help you source, centralize, store, and transform your data into usable insights.

Along with analytical capabilities, robust automation platforms are able to automate your processes and transform your workflows for the better. This can free up your teams time by running processes 100x faster.

What is Data Cleansing vs. Data Cleaning vs. Data Scrubbing?

Data cleansing, data cleaning, and data scrubbing are often used interchangeably. However, it’s more accurate to use data cleaning and data cleansing as synonyms. Both are the same way of saying that you make sure your data is relevant, clean, and ready to use without missing information.

Data scrubbing is often used as another synonym, but it’s a more intense version of data cleansing and data cleaning. It’s just like when you clean a countertop versus scrub a countertop - scrubbing is more rigorous.  

Why is it Important to Cleanse Data?

If you understand the importance of using data in the first place, then the importance of data cleansing is just as clear. With the data cleaning techniques, you get to have organized, complete, clear, and accurate data available to use.

Since data comes into organizations from so many sources and with such great speed, it’s increasingly valuable to perform the data cleansing process. By consolidating data in a centralized location and removing irrelevant and outdated data, you can make the most out of having it in the first place.

What Errors Does Data Cleansing Process Fix?

Data cleansing and data scrubbing get your data in tip-top shape. They help to resolve common issues, such as:

1. Typos and Missing Data

Data cleaning can be done to correct inconsistent spelling in data and other typos, like syntax mistakes or missing data.

2. Repetitive Data

Duplicate records can throw everything off. With data cleansing, you have the chance to review your data and remove them.

3. Unnecessary Data

Irrelevant data such as out-of-date entries and/or outliers can skew your analytical results.

4. Inconsistent Data

When you pull data from different sources, each source typically has its own format for storage. In one system, you may have a column that doesn’t exist in the fellow system, so it can be inconsistent for your needs.

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That’s a lot to remember when handling your influx of data. Luckily, finance automation solutions do the heavy lifting for you! Finance automation software will centralize, cleanse, and format data to ensue it’s always up-to-date.

With added capabilities like process automation for account reconciliations, rebate management, expense analytics, reporting, and more, there are many advantages to be had with its deployment.

What are the Characteristics of Clean Data?

After completing the data cleansing process, your data is ready to use with certainty. Here’s what you can expect from clean data:

• Consistency
• Accuracy
• Validity
• Uniformity
• Integrity
• Completeness

When you deploy automation software to help manage your data centralization, storage, and cleansing needs, your organization can always trust its data analytics and reports.

This makes it easier to maintain internal control, keep stakeholders aware of the company’s standing, and reduce compliance and regulatory risks.

What are the Benefits of Data Cleansing?

There are plenty of benefits to be had when the data cleansing process is performed consistently and properly.

Let’s take a look at the most outstanding advantages that your business stands to gain by doing so:

1. Better Decision Making

Effective business strategies and choices depend on the analytics that you see. The analytics that you see depend on the inputs you provide, and in this sense, we’re talking about raw data! That’s why clean data is needed for clean (and optimal) answers.

2. Increased Efficiency

We’ve been focused on data that affects customers and external outcomes, but when you think about reviewing data, there is also the benefit of using it for process improvement.

When you have the ability to use data to reflect on how your organization is running, you can make timely adjustments to get better.

3. Improved Competitiveness

With greater ability to service customers and improve employee satisfaction comes a greater competitive edge.

What are the Challenges of Data Cleansing?

In just a day, there’s an enormous amount of data entering your organization, so imagine the amount that you have a year. If you’re trying to manually cleanse data, there wouldn’t be enough time in the world. The aid of automation and artificial intelligence has made it doable.

However, there are challenges that exist, such as:

1. Distributed Data

Since data comes from so many separate sources, it needs to be centralized to be able to cleanse it properly.

2. Variety of Data

Along with the different data sources, there is also a variety of data formats, like spreadsheets, images, videos, social media content, and the like.

What are the Best Data Cleansing Tools?

The best data cleansing tools remove the manual stress of the process. Data automation tools can be used to remove 98% of human errors and save time.

The best data cleansing tools combine automation to manage your business processes, like expense management, rebate management, account reconciliation and more, along with consolidating your data, analytics, and dashboards.

This means that you get to combine all your data sources into a centralized repository for a full picture view of your business and its analytics at any time. In turn, you can make informed business decisions on the fly.

Final Words

The best part about the data cleansing process is that it can be made easy with the aid of data automation technology. This is great news because all companies should be executing data cleansing so that they can trust their analytics and understand their business with clarity and confidence.