Definition

data scrubbing (data cleansing)

What is data scrubbing?

Data scrubbing, also referred to as data cleansing, is the process of amending or removing data in a database that is incorrect, incomplete, improperly formatted or duplicated. Organizations indata-intensive industries like banking, insurance, retail, telecommunications and transportation use data scrubbing tools to systematically examine data for flaws.

Typically, a database scrubbing tool includes programs that are capable of correctingspecific types of mistakes, such as adding missing zip codes or finding duplicate records. Using a data scrubbing tool can save a database administrator a significant amount of time and can be less costly than fixing errors manually.

While both data scrubbing and data cleansing are used in the context of customer data, they represent different data hygiene processes. Data scrubbing involves specific processes including merging, filtering, decoding and translating data. However, data scrubbing, data cleaning and data cleansing are frequently used interchangeably to refer to the same process.

What are common data errors that data scrubbing fixes?

In addition to removing inaccurate or corrupt data, data scrubbing also fixes the following:

  • Duplicate data. Data scrubbing is an automated way to identify duplicate records and remove them from the data set. If an organization needs to merge data from two systems, data scrubbing can be used to identify the duplicates between the two systems. The duplicate records can then be reconciled and merged to create one record representing the true data.
  • Inconsistent data. A data scrubber is a tool that analyzes data and cleans it up so that it is consistent with the rules set for that data. For example, a data scrubbing tool can help ensure that all data in a system follows a certain required format.
  • Redundant data. Data scrubbing can help remove redundant data from your data stores, and as a result, it can reduce the amount of disk space required to store all that data.
  • General errors and typos in data. Data scrubbing can help fix general errors such as typos and missing information. For example, while data scrubbing may not be able to fix errors in the file format, scrubbing can correct some of the errors that occur from manual entry.

The benefits of data scrubbing

Data scrubbing offers the following productivity and data quality-related benefits:

Advantages of improving data quality by data scrubbing
Removing inaccuracies and errors provides several benefits
  • Improves decision-making. Data scrubbing is an important part of the data quality process, as it identifiesand removes errors, biases and inconsistencies in data. This makes the data more accurate, so it has a larger scope andcan produce better results. These benefits can be applied to any decision-making process, in any environment.
  • Boosts efficiency. Data scrubbing performs data deduplication to find and remove duplicates from a database. This process matches the values of each data row in a database to that of another row and marks them as duplicate or unique. Organizations typically perform data scrubbing regularly to ensure applications are running smoothly. In the long term, this saves time and money, as employees are not performing mundane data quality projects.
  • Reduces inconsistencies. Data scrubbing detects and corrects inconsistencies in data. Data inconsistencies, often called data integrity issues, can cause problems in many business processes. Poor or missing data can cost money and time and result in erroneous conclusions. Data scrubbing identifies information that does not conform to business rules or policies and automatically corrects it. This data governance enhances the integrity and quality of the data.

Challenges of data scrubbing tools

The data cleansing process is an important part of any data management strategy, but it is not without its disadvantages. One of the biggest issues is that it is time-consuming, since it can be difficult to identify why data is inaccurate. Often, the best way to understand what is wrong with the data is to consult with the original source of the data, which is often difficult or even impossible. In addition, data cleansing does not always yield the desired results. If too much data is removed, the data set may become incomplete and ineffective.

Data scrubbing steps

There are numerous data cleansing products on the market that are suitable for organizations of all sizes. Although the data cleansing process may vary from data set to data set, data scrubbing tools typically perform the following actions:

  1. Audit and inspect. A data scrubbing tool audits and inspects data to find inconsistencies. An audit reveals the overall health of a system's data and helps identify issues that must be fixed.
  2. Data cleaning. This involves the process of finding discrepancies and making corrections. This might include removing duplicate data, repairing inconsistencies in formatting and fixing general errors such as typos.
  3. Verification of data cleanliness. After the cleaning process, the team must examine the results to verify the cleanliness of data and to ensure they are following all standards and regulations.
  4. Report. The results are converted into a report to highlight trends and progress.
  5. Use results to prevent future data issues. The team can examine the report to determine what data is problematic, so they can make modifications based on this information to avoid data issues in the future.

Learn how strong data governance policies can help organizations prevent data silos and ensure better quality data.

This was last updated in May 2021

Continue Reading About data scrubbing (data cleansing)

Dig Deeper on Data quality techniques and best practices

SearchBusinessAnalytics
SearchAWS
SearchContentManagement
SearchOracle
SearchSAP
SearchSQLServer
Close