What Is Data Cleaning?

From customer information to employee records, businesses amass a wealth of data, but not all prove valuable. Redundancy, errors, and obsolescence taint data, causing it to appear “dirty.” 

Data cleaning is the first step toward readying data for business intelligence (BI). The process is similar to straightening a messy room. Decluttering makes it easier to find what you need, just as cleaning data uncovers valuable insights and ensures accurate analysis.

Let’s explore the ins and outs of data cleaning in more detail to help you have a better grasp of its function and role.

What does data cleaning entail?

Data cleaning, also called data scrubbing or cleansing, is the practice of weeding out data within a data set that is inaccurate, repetitive, or invalid. Data cleaning is typically done manually by a data engineer or technician or automated with software.

Why is data cleaning important?

According to Gartner, a management consulting company, organizations incur an annual average cost of $12.9 million due to subpar data quality [1]. 

Clean, high-quality data simplifies the interpretation and utilization of data files in various business applications, such as sales, marketing, and financial reporting. High-quality data is also pertinent in training machine learning (ML) models, as poor-quality training data sets can lead to inaccurate results or predictions. 

6 steps to clean data

Data cleaning can be complex. However, breaking it down into smaller targets simplifies the process. The following offers steps you can follow to achieve a cleaner data set. 

1. Assess data quality.

Start with a review of your data to ascertain its quality and flag issues for analysts. This includes identifying data element relationships and collecting statistics on data sets to uncover inconsistencies.

2. Delete duplicate or inappropriate entries.

Eliminate redundant data from your data set(s) through data deduplication techniques, such as inline and post-processing deduplication. Additionally, scan for irrelevant data points that may negatively impact your data set(s). For instance, if your study revolves around fast-food restaurant preferences, removing inapplicable, off-the-subject data, such as fine-dining restaurant choices, can result in a more concise and relevant data set. 

3. Correct structural errors.

Make sure your database columns are uniform in terms of data type. This may involve maintaining a consistent date format, numeric format, or unit of measurement throughout your data set. Furthermore, verify and standardize the use of abbreviations. For example, if you have "United States" and "US" referring to the same entity, standardizing them to one consistent format can help reduce ambiguity.

4. Discard outliers. 

Outliers are unusual values in your data. While outliers can enhance sample set understanding in specific scenarios, they are not always beneficial. This is because outliers can introduce extreme data variance, leading to skewed, inaccurate conclusions. For example, occasional traffic spikes in a data set of monthly website traffic are valuable outliers for identifying popular content. Yet, these outliers should be excluded for accuracy when conducting general analyses like calculating average traffic.

5. Check for missing data. 

Machine learning algorithms learn from data. They identify patterns, relationships, and trends within the data to use for making data-driven decisions and predictions. When left unchecked, missing data can inadvertently affect machine learning algorithms’ performance.  

For example, consider a salary survey data set in which certain participants omitted their income information, resulting in missing data. To handle this situation, you can either exclude all the participants' responses, approximate their income based on factors such as their educational background and occupation, or indicate the absence of income data. These strategies are not without their flaws but are essential for mitigating the impact on overall data analysis. 

6. Validate cleansed data. 

The last step involves validating your cleansed data set for accuracy and reliability. To do so efficiently, utilize scripts to compare the data set with a reference or ideal database.

Did you know?

Data gathered for the National School Lunch Program (NSLP) and School Breakfast Program (SBP) eligibility verification undergoes multiple stages, and thereby multiple reforms, before it reaches analysts at the Food and Nutrition Service (FNS). In 2019, FNS renewed its data validation approach using an application programming interface (API)-based system, boosting data quality without altering schools' standard data submission protocols [2]. 

Advantages of data cleaning 

The following are some notable perks of cleansing data: 

• Refining data in CRM and sales systems enhances marketing and sales effectiveness.

• Clean data mitigates the risk of inventory shortages, incorrect deliveries, and other business challenges, resulting in cost savings.

• Mitigating recurring errors in data sets through data scrubbing enables IT teams to concentrate on strategic tasks instead of repetitive maintenance.

Challenges of data cleaning  

The common hurdles in cleansing data include: 

• Segregated data repositories within an organization can impede data cleaning processes.

• Scrubbing data in intricate systems with diverse data types, such as structured, semi-structured, and unstructured, is labor-intensive and costly.

• The addition of missing data values may not always be possible.

Data cleaning vs. data transformation: What’s the difference? 

Data cleaning involves removing irrelevant data from your data set, while data transformation helps convert data into a different format or structure. Data transformation is also called data wrangling. 

Both serve different purposes. Data cleaning boosts accuracy while wrangling facilitates data modeling. Typically, cleaning precedes wrangling. 

Is automated data cleaning always advantageous? 

Automated data cleansing tools can speed up your analyses. However, manual processes may persist in your workflows despite effective and cost-efficient software solutions. This is because automation is seldom a fix-all solution. 

Consider a data set with missing birthdates. Despite extensive training, artificial intelligence (AI) or ML models in automation may struggle to accurately predict or fill these gaps. At the same time, a human can infer missing birthdates based on available data or external information.

Get started with Coursera. 

Deepen your understanding of data cleansing with Google’s Process Data from Dirty to Clean course on Coursera. Designed for beginners, this course offers instructions on applying basic SQL functions for scrubbing string variables in a database.

You may choose to complement this course with a Guided Project, Data Cleaning in SQL: Prepare Data for Analysis, also available on Coursera. The program provides hands-on training in detecting and removing duplicates, handling CASE data transformations, and eliminating null values in SQL.