What is Data Scrubbing?

Data scrubbing, also known as data cleansing or cleaning, is the set of steps involved with preparing data to be analyzed. It pertains to modifying or deleting any data that is incomplete, irrelevant, duplicated, improperly formatted, or incorrect so that such data will not lead to inaccurate results down the line. This process is typically more complicated than erasing existing information and replacing it with new data. It also can involve discovering ways to maximize the accuracy of the data in a set so that it doesn’t have to be eliminated. Actions such as standardizing data sets, correcting missing codes and empty fields, addressing syntax and spelling errors, and spotting points where data has been duplicated all fall under the umbrella of data scrubbing.

A variety of methods and tools can be used to scrub data. Selecting the most appropriate one can depend on the type of answers being searched for, as well as how the data is stored. The act of data cleaning is one of the core components of data science and data analytics as it helps to ensure that the answers discovered in the analytical process are as reliable and helpful as possible. 

What Does Data Scrubbing Involve?

The act of data scrubbing differs based on the organization that is performing it. However, there are some common steps involved in this process:

  • Eliminate irrelevant or duplicate information. The first step in the data scrubbing process involves getting rid of any unnecessary information within a dataset. This is especially important when working with data that was combined from various sources, as duplicate data can be created. De-duplication of data is one of the most important aspects of this step. In addition, irrelevant data, such as that which doesn’t inform the problem being analyzed, must also be removed in order to ensure that analysis is as efficient as possible and any information that distracts from the main point is minimized.
  • Repair any structural errors. Structural errors occur when data is transferred and inconsistencies appear, such as typos, unnecessary capitalizations, and other unintended naming conventions. It’s important to address these inconsistencies since they can lead to mislabeled classes or categories.
  • Filter irrelevant outliers. Although not all outliers are problematic, some spring from improper data entry and can be eliminated from datasets. Eliminating unwanted outliers can improve a dataset’s performance. However, the presence of an outlier doesn’t necessarily indicate any problem with the data. Only those outliers that are deemed to be a mistake or not pertinent to the analysis at hand should be removed.
  • Account for missing data. Many algorithms will not accept missing data values, which means that Data Analysts must find an effective way to handle missing data. One way to do so is to eliminate any observations that include missing values. However, keep in mind that this may lead to lost or dropped information that is of value to the issue at hand. Another way to handle missing data is to input values that are based on other observations or assumptions. Data Analysts may also opt to change the actual way data is being used in the analysis process in order to work with null values.
  • Monitor and report on errors. This ensures that the source of the errors can be identified, and enables Data Analysts to repair corrupt or incorrect data before it can be used in future endeavors.
  • Validate the resultant data. Once the data scrubbing process has been completed, Data Analysts should be able to answer some basic questions to make sure the data is valid:
    • Does the data being used make sense?
    • Does it adhere to the rules in its specific field?
    • What insights does this data provide? Does it confirm or disprove the working theory?
    • What trends are noticeable in the data? Can any of them be used to inform subsequent theories?

If the answer to any of these questions is “no,” the data quality may be in question and dirty data may be present, which can lead to false conclusions. Further data scrubbing may be needed before moving forward.

Why is Data Scrubbing so Important?

Data scrubbing plays an integral role in the data analytics process. With the help of various data analytic tools and platforms, users can ensure that the data they are working with is as error-free and accurate as possible, which will lead to a better end result for their company or business. Regardless of which data scrubbing tool or tools or methods you ultimately select, the good news is there are many helpful options available to meet your organization’s needs.

The following are some of the many benefits data scrubbing provides, such as:

  • Increased efficiency: Not only does working with clean data provide benefits for a company’s external needs, but it also can improve in-house productivity. The act of cleaning data can uncover insights into a company’s needs that may otherwise go overlooked.
  • Better decision making: The better quality of data a company has to work with, the more likely it will be to implement effective strategies and make important decisions.
  • Competitive advantage: Companies who are able to meet and exceed customer needs are in a position to outperform the competition. Working with clean, reliable data is a valuable tool that allows a business to stay abreast of new trends as well as customer needs. Acting on this information provides quicker responses and ultimately a better customer experience.
  • More effective customer targeting: Working with unscrubbed or poorly scrubbed data can cause a company to target the wrong market. Because customer purchasing habits can change very quickly, it is common for data to be outmoded. By incorporating effective data scrubbing, new, updated data pertaining to a specific target market is available for analysis.
  • Faster decision making: Because data scrubbing improves the overall efficiency of the data analytics process, important decisions based on the data can be made faster when working with clean data.
  • Overall cost reduction: Organizations that streamline their work environment can reduce overall operational costs. By incorporating data analytic and scrubbing tools, organizations are in a position to spot new opportunities, such as a demand for a new product that was being cloaked by outdated statistics.

Clean data is a vital component of any successful business that works with data. Access to clean data cuts down on costs, improves efficiency, and lends itself to more effective decision-making for your company. 

Hands-On Data Analytics & Data Science Classes

If you are interested in learning more about the various tools that are currently available for managing and visualizing big data, Noble Desktop’s data science classes provide a great option. Courses are available in-person in New York City, as well as in the live online format in topics like Python and machine learning. Noble also has data analytics courses available for those with no prior programming experience. These hands-on classes are taught by top Data Analysts and focus on topics like Excel, SQL, Python, and data analytics.

Those who are committed to learning in an intensive educational environment can enroll in a data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 40 bootcamp options are available for beginners, intermediate, and advanced students looking to learn more about data mining, data science, SQL, or FinTech. 

For those searching for a data science class nearby, Noble’s Data Science Classes Near Me tool makes it easy to locate and learn more about the nearly 100 courses currently offered in the in-person and live online formats. Class lengths vary from 18 hours to 72 weeks and cost $800-$60,229. This tool allows users to find and compare classes to decide which one is the best fit for their learning needs.