What Is Data Cleansing (Data Scrubbing)?

Data cleansing, also known as data cleaning or data scrubbing, refers to the process of removing all types of dirty data, including fixing incorrect, incomplete, duplicate, or otherwise erroneous data points. It is a necessary process in any data governance strategy, helping business leaders across all industries improve data quality and maintain data integrity to provide more accurate and consistent information for proactive decision-making.

Why is data cleansing important?

Data is a currency in business analytics and management. In today’s fast-paced and data-driven world, making informed decisions is essential to success. This places a heavy reliance on clean data, requiring business leaders to ensure their data is reliable to maintain their competitive advantage over their peers.

If data is not properly and regularly cleansed, organizations run the risk of faulty information. This, in turn, can lead to flawed business decisions, missed opportunities, misguided strategies, and even massive operational inefficiencies.

An MIT Sloan Management Review estimated that bad data costs a business 15% to 25% of its revenue. Another study by IBM suggested that $3.1 trillion is wasted from the US economy each year due to bad data. While these studies were conducted years ago, IT experts agree that this trend has continued and will continue in the future—now, most especially that AI and data science are some of the top IT trends for 2024 and beyond.

What kind of data does data cleansing correct?

Data cleansing fixes a wide range of errors and issues in data sets. While some of these errors may be caused by human error during the data entry process, other dirty data may have resulted from using different data formats or terminologies in separate systems throughout the organization.

For example, an MSP wants to categorize its data backup and recovery strategies according to each best practice. Instead of creating one best practice labeled “data cleansing”, they made two more categories labeled “data scrubbing” and “data cleaning”. While the terms do have their semantic nuances, these differences are negligible in the overall data governance plan. Thus, the organization inadvertently created redundant categories for data points to be entered into.

Let’s look closer into the different data types that data cleansing can fix.

Invalid data

• What they are: These data points conform to certain requirements for specific types of information.
• Example: Date of birth on a form may only be recognized if it’s inputted in a specific way, such as MM-DD-YYYY.

Inaccurate and incorrect data

• What they are: How close the observed value is to its true value.
• Example: In a survey, you asked, “How often do you take a bath?” and the respondent wrote, “rubber duck.”

Incomplete data

• What they are: Data points that are missing information.
• Example: Respondents that do not answer a survey question, leaving the field blank.

Inconsistent data

• What they are: Data sets that do not logically make sense. Keep in mind that this can sometimes overlap with inaccurate and incorrect data.
• Example: A respondent stated they do not have internet access in an online survey.

Redundant entries

• What they are: You may have accidentally recorded data from the same participant twice.
• Example: A respondent hits “Enter” twice, leading to a double entry.

Variable data

• What they are: Data points not in the same units must be converted to a standard measure.
• Example: When asking about their height, some people may use feet and inches, while others will use centimeters.

How do you clean data?

Every data set requires different techniques to cleanse bad data, but there must still be a practical and strategic framework in place. Ideally, you’ll be able to conserve as much data as you possibly can while ensuring that each data point is clean and accurate. In practice, however, this may be problematic, especially if you’re a larger organization with millions upon millions of data.

That said, most data cleansing workflows look something like this:

1. Implement input sanitization techniques to prevent dirty data.
2. Automate regular data screening to identify errors and inconsistencies.
3. Diagnose and verify data entries to ensure each point meets internal data quality standards.
4. Develop codes for mapping data into their valid values.
5. Remove bad data based on standardized procedures, such as data sanitization.

Note that these steps can change depending on the type of data set you are working with. Ensure you carefully apply data cleansing techniques as necessary, with clear and effective IT documentation of your processes for transparency.

Automating data cleansing: The future of clean data

The emergence of AI tools has dramatically changed the way we cleanse our data. Whereas more traditional (and manual) methods would involve using a spreadsheet where users first need to define rules and then create specific algorithms to enforce them, AI can be leveraged to remove data anomalies much more quickly and efficiently.

Traditional methods are also expensive and impractical for big data. Today, more businesses use Extract, Transform, and Load (ETL) tools to cleanse their data (usually in a data warehouse). Here, a tool extracts data from one source and transforms it into another form. The transformation step removes any errors and inconsistencies in a data point while detecting any missing information. After the transformation process, the clean data is moved into the target data set.

Is manual data cleansing still required?

It’s worth noting that manual data cleansing is still required, though the amount of time needed for this should be minimized. Automation and AI tools are intended to improve operational efficiency, but humans still need to correct typos, standardize formats, and remove outdated data from a data set. Humans also provide an additional quality control check, ensuring a database is as perfect as possible.

Data cleansing and backup software

In every data governance framework, you’ll find strategies for data cleansing and the importance of backup software. Remember that every data validation strategy should always be accompanied by robust backup software, such as the one offered by NinjaOne.

NinjaOne’s backup software has a built-in data recovery tool, ensuring comprehensive backup and swift data recovery. The all-in-one tool safeguards your digital assets from all cyber threats, including any vulnerability exposed by bad data.

If you’re ready, request a free quote, sign up for a 14-day free trial, or watch a demo.