It should come as no surprise that data isn’t perfect. Just like everything else in life, digital data is subject to human error, inconsistencies, redundancies, spelling mistakes, and incomplete information. Since so much of our life and work now resides in databases, it’s more important than ever to make sure that data is as close to perfect as can be.
It’s time to get educated on the practice of data scrubbing, including the best tools for the job, and how data scrubbing it differs from data cleaning.
What is Data Scrubbing?
If in the course of doing household chores, someone told you to clean the floor, you most likely grabbed a broom, swept the floor, then maybe ran a damp mop over it. But if that same person tells you to scrub the floor, then you will be down on your hands and knees with a scrub brush and bucket of hot soapy water and putting a major effort in cleaning. The word “scrub” implies a more intense level of cleaning, and it fits perfectly in the world of data maintenance.
Techopedia defines data scrubbing as “…the procedure of modifying or removing incomplete, incorrect, inaccurately formatted, or repeated data in a database.” The procedure improves the data’s consistency, accuracy, and reliability.
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What is Data Cleaning, and is it the Same Thing?
Although many sources use the phrases “data scrubbing” and “data cleaning” interchangeably, that’s not accurate.
Data cleaning, also called data cleansing, is a less involved process of tidying up your data, mostly involving correcting or deleting obsolete, redundant, corrupt, poorly formatted, or inconsistent data. Data professionals do the actual cleaning, checking the database and making corrections and edits as needed, and practicing good data entry habits.
Consider data scrubbing as a subset of data cleaning. Data scrubbing employs actual tools to do a much “deeper clean” than just having a user pore over database spreadsheets and making corrections. Here’s a glance at how you should clean your data, and how scrubbing fits into the timeline.
Monitor and Record Database ErrorsIdentify and catalog areas that generate the most mistakes
Come up with a Set of StandardsBefore you clean any data, make sure that there is a consistent set of rules and protocols in place that you can compare the data against. It’s pointless looking for inconsistencies in your information if the standards aren’t current and in place
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Validate Your DataVerify the accuracy by acquiring data tools that let you clean your data in real-time. This validation signals the start of data scrubbing
Scrub Duplicates from Your DatabaseUse data scrubbing tools to search and remove redundant information, a condition that usually occurs when users must merge two different databases
Have the Data AnalyzedOnce your data has been cleaned and scrubbed, make sure it is following all regulations and standards. If possible, use a third-party for data tool for verification
Inform Your TeamWhen the data is cleaned and conforms to the new standards, notify your team and anyone else in the organization that should know. By informing people about the new methodology, you minimize the need to perform extensive data cleaning in the future. Additionally, appoint someone in your organization to be the data quality evangelist, who has the responsibility of spreading awareness and facilitating communication about all aspects of data quality
Who Should Employ Data Scrubbing, and Why?
Everyone should have clean data; that’s a no-brainer. However, there are specific sectors and industries that, due to the essential roles they play in society, must make data scrubbing a very high priority.
Unsurprisingly, data scrubbing is a high priority in data-intensive industries such as banking/finance, insurance, retail, and telecommunications.
Here’s a breakdown of the chief sources of database errors:
- A human error made during data entry
- Merging databases
- A lack of either industry-wide or company-specific data standards
- Older systems that hold on to obsolete data
This article provides some sobering statistics about data quality. Among the points it touches upon:
- Businesses lose up to 20% of their revenue because of bad data quality
- Employees waste up to half of their production time dealing with routine data quality tasks
- In any given hour of the day, almost five dozen companies will change their addresses, nearly a dozen will change their name, and over 40 new businesses will open
Today’s businesses and organizations need to make data quality a higher priority, incorporating better data quality practices, and acquiring useful data cleansing tools.
The Best Data Cleansing Tools
As the old saying goes, “use the right tool for the right job.” In the spirit of these words of wisdom, here are six of the best data scrubbing tools available today, presented in no specific order.
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- Data analysts can clean and prepare disorganized and eclectic data faster and with more accuracy. Trifacta uses machine learning algorithms to prepare data for scrubbing by suggesting common transformations and aggregations.
There are many more data cleaning utilities out there, with some that emphasize certain aspects of data cleansing over others. Every business has unique demands, so make sure to shop around for the best fit.
Do You Want to Learn More About Data Management?
According to this article, only 30% of businesses have a data quality strategy—the rest simply waiting until a problem arises. This practice is a short-sighted approach that is ultimately self-defeating and costly. As more organizations become aware of the importance of incorporating a data quality strategy, there will be a correspondingly higher demand for professionals who are familiar with all aspects of data management.
Data management professionals, however, have the daunting task of trying to learn all the many facets of data management. This task is especially true for professionals who are already in the data science field but want to upskill. Fortunately, Simplilearn is your one-stop source to learn everything you need to know about modern data management.
For instance, a good data manager knows about statistical analysis and data mining. Also, more organizations want data professionals to know Python for data analysis positions. Speaking of data analysis careers, you may want to brush up on some Data Science interview questions before heading off to that important job interview!
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Do You Want to Become a Data Scientist?
Data is the lifeblood of our personal and commercial lives, and the need for data scientists is growing. If you’re training to become a data scientist, you need to look into Simplilearn’s Data Science course.
This exclusive Data Science course co-developed with IBM. You will experience world-class training by an industry leader on the most in-demand data science and machine learning skills. The six-course program gives you hands-on exposure to key technologies, including R, SAS, Python, Tableau, Hadoop, and Spark. You will receive instruction in over 30 in-demand tools and skills, plus hands-on training courtesy of over 15 real-life projects. When you complete the course, you earn your master’s certificate and are ready to make a name for yourself in the world of data science.
Data scientists earn an annual average of USD 113,309, according to Glassdoor, and the demand for professionals shows no signs of tapering off. Check out Simplilearn today, and get your career into high gear!