What is Data Scrubbing

Nearly 2.5 quintillion bytes of data are produced every day as businesses depend more on datasets for their growth, success and day-to-day operations. The usage of data, which continues to grow, is now posing various challenges for organizations when it is maintained in humongous chunks. As a result, innovative methodologies like “Data Scrubbing” have emerged which help in ensuring the quality and authenticity of data.

With an enormous data flow through pipelines, organizations need to ensure that the data they process is of good quality, clean, error-free, and also readily available for reporting and analysis. Error free data can be really helpful when it comes to managing costs, while a dataset full of errors can lead to lower efficiency, poor productivity, and poor decision making.

Companies are now looking for efficient employees skilled in Data Scrubbing who can help them in making their data flawless. Candidates who have pursued courses like PG Diploma in Data Science and similar certificates are eligible for such roles. Data Scrubbers are expected to keep the data free of inaccuracies and also save costs. Let’s understand more about data scrubbing in detail, including its process and importance.

Data Scrubbing

Data Scrubbing, also referred to as data cleaning or data cleansing, is a process of identifying and removing common errors from the dataset, to ensure that the data is correct, consistent and usable.

  • It includes simple steps like repairing, deleting, or normalizing data to use it for sales initiatives, customer support, marketing campaigns, etc.
  • The companies use multiple data scrubbing tools to manage their data. These help in saving a lot of effort and time compared to when performing manual operations.
  • Industries that deal with extensive amounts of data like Banking, Insurance, Telecommunication, Transportation, Retail, etc. use data scrubbing tools to examine data for flaws.

Why is Data Scrubbing Important?

Data Scrubbing plays a significant role in data management and data analytics history, even though it is challenging and still in the development phase. Some of the key reasons and factors which make it essential are listed below.

  • Data is collected through various resources like mobile devices, sensors application servers, GPS systems, etc. These datasets recovered from multiple sources need to be redefined separately and have proper filters.
  • The data is too noisy and useless if not cleaned. Companies need to make sure that it is available to them in a format that is usable.
  • Until and unless these data sets are not converted into a unified form, data scientists cannot use them to create insights.
  • Data scrubbing helps in filtering out irrelevant information that includes poorly formatted data sets, duplicate records, and missing or incorrect information. It also eliminates the data records that are not necessary.
  • The data formats for logs and metrics are different and therefore it becomes difficult for analysis. So, data scrubbing not only removes errors but also transforms log and metrics data into a common format.

Thus data scrubbing helps to share views & insights and also helps to speed up the work frequency and accuracy.

What All Key Errors are Solved Through Data Scrubbing?

Some of the general data issues that are solved through the process of data scrubbing are mentioned below.

  • Duplicate Data: Copy-pasting similar data for more than one time creates several duplicates. This duplication of data is removed through Data Scrubbing to have a single record of a particular thing.
  • Inconsistent Data: Scrubbing ensures that all the fields follow a consistent format. The information looks very redundant when not in a common proper format.
  • Redundant Data: It helps to remove the repeated data, which is unnecessary and creates no use.
  • General Human Errors: There are several mistakes when a human enters the data manually. There can be typing mistakes, grammatical errors, etc. Data scrubbing usually improves these general errors too.

What are the Steps to Perform Data Scrubbing?

Data scrubbing involves a set of processes which have further sub-steps. We’ll look at the standard process of data scrubbing below.

  • Audit and Inspect: Data scrubbing tools start with conducting audits to check the overall data and identify issues that need to be fixed.
  • Data Cleaning: This step finds mistakes and makes corrections by fixing common errors, removing duplicates, repairing formatting errors, and other smaller issues.
  • Verification: This step ensures that all the standards and regulations are followed. The results are examined again to verify the cleanliness of the data.
  • Report: The cleaned and verified data is now converted into reports to highlight trends and progress.
  • Create Automated Processes: To avoid similar data issues in the future, the modifications are done and are automated to determine the problem and fix it automatically.

Which are the Popular Data Scrubbing Tools?

Data scrubbing tools have turned out to be quite effective as they help to skip the tedious job of cleaning the data manually. In spite of checking and cleaning the entries individually, they can help eliminate errors through automated processes. These tools systematically inspect the data using different rules and algorithms and identify flaws and correct them. 

Data scrubbing tools are worthy investments for businesses. Some of the best data scrubbing tools are mentioned in the table below.

OpenRefineDrakeTIBCO Clarity
WinpureData LadderData Cleaner
Cloudingo Reifier IBM Infosphere Quality Stage

What Importance Does Data Scrubbing Hold for Businesses?

Data scrubbing is beneficial for companies as it has shown the following benefits.

  • It helps businesses save employees by assigning them other tasks while a tool is performing data cleaning for them.
  • It can enable the smooth functioning of daily operations by removing errors through multiple sources of data. These removed errors help businesses in making accurate decisions.
  • It saves time and additional labor costs by reducing inconsistencies in the data sets.
  • It helps to fix corrupt data for future applications by monitoring and reporting the errors.

Data scrubbing is a must-have skill and an important process for Data Science and analytics, and also for building models for Machine Learning and Artificial Intelligence. There are various online courses available that individuals can join to learn and showcase in their resumes.

A Data Scrubber in India can earn an average salary package of ₹20 lakh per annum. So if you’re also interested in this field, do not wait and join a course now.

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