For businesses, good data hygiene is critical. For starters, keeping track of your data and ensuring that it is correct and up-to-date is a smart idea. Data cleansing, on the other hand, is an essential component of the data analytics process. You can be sure that your results will be faulty if your data contains inconsistencies or inaccuracies. It doesn’t take a genius to figure out what may go wrong when you’re making business decisions based on such findings.
Bad insights can lead to money being wasted on poorly focused initiatives in a subject like marketing. It may actually mean the difference between life and death in fields such as healthcare and the sciences. We’ll go over what data cleansing is and why it’s so important to do it properly in this piece.
What is data cleansing?
Data cleaning, sometimes called data cleansing or data scrubbing, is the act of changing or eliminating data from a dataset that is wrong, duplicated, incomplete, poorly structured, or damaged.
Data cleaning is a crucial first stage in the data analytics process. This critical task, which involves data preparation and validation, is often performed prior to your main study.
Why data cleansing is important?
Cleaning data is crucial since it ensures that you get the best data possible. Not only will this reduce mistakes, but it will also reduce customer and staff irritation, boost productivity, and improve data analysis and decision-making.
‘Garbage in, garbage out,’ is a popular mantra in the data analytics field. This data analyst axiom has its own acronym: GIGO. But, exactly, what does this imply? GIGO basically indicates that if the quality of your data is poor, whatever analysis you conduct with it will be faulty as well. Whether your data is a mess, it won’t matter if you follow every other stage of the data analytics process to a tee.
The key benefits of Data Cleansing:-
Keeping track of your tasks:
Clients, consumers, product users, and others provide a wealth of information to today’s organizations. This information ranges from addresses and phone numbers to bank account information and more. Cleaning and maintaining this data on a regular basis entails keeping it in order. It can then be more efficiently and safely kept.
Mistakes to avoid:
Data quality issues aren’t limited to data analytics. It has an impact on day-to-day activities as well. A client database, for example, is common among marketing teams. They’ll have access to useful, correct information if that database is in good working order. When everything is a jumble, mistakes are certain to arise, such as sending customized letters with the wrong name.
Regularly cleaning and updating data removes rough data swiftly. This eliminates the need for teams to go through outdated databases or papers in order to locate the information they want.
Making business judgments based on inaccurate information might result in costly errors. However, incorrect data might have additional consequences. Processing failures, for example, might swiftly escalate into more serious issues. You may spot blips sooner by examining data on a regular basis. This allows you to correct issues before a more time-consuming (and expensive) remedy is required.
How to clean data:
1. Remove any contacts that are duplicated.
The removal of unwanted observations (or data points) is the initial step in any data cleaning procedure. This includes observations that aren’t related to the problem you’re attempting to address. For example, we may exclude all meat-related observations from our data collection if we were doing a study on vegetarian eating habits. Duplicate data is also removed at this stage of the procedure.
2. Fix any structural flaws.
Typos, unusual name practices, irregular abbreviation, capitalization, or punctuation, and other errors caused by human data input and a lack of standardization are examples of structural faults. “Not Applicable” and “N/A,” for example, may appear to be independent groups, but they should be studied together.
3. Address missing data
It’s unavoidable that data gets lost. You can approach this issue in a number of ways:
Remove any missing values from the list.
Fill in missing values using the dataset’s other information.
Mark the information as “incomplete.”
None of these options are ideal, but they can help you reduce the negative impact on your data analysis.
4. Make data entry consistent
If you don’t put in place company-wide data input standards, all of the aforementioned procedures will be useless. When building a contact record, you should establish guidelines for whether values should be all lowercase or all uppercase, what unit of measurement numerical data should be in, and which fields are essential.
Frequently asked question:
What is data correction?
The action of verifying data that has been declared (potentially) incorrect is known as data rectification.
What is the use of data cleaning tools?
It allows users to evaluate data, as well as deduplicate and cleanse addresses, in order to swiftly spot trends and make better judgments.
What is data profiling?
The process of evaluating, analyzing, reviewing, and summarizing data sets in order to acquire insight into the quality of data is referred to as data profiling.