Data Cleansing Guide: What It Is and Why It’s Important
Table of Contents
- What is Data Cleansing?
- Why is Data Cleansing Important?
- How Can Data Cleansing Help Businesses?
- How Often Should Businesses Data Cleanse?
- Data Cleansing vs. Data Transformation
- How to Cleanse Data Yourself
- Purge Irrelevant Information
- Remove Duplicate Data
- Edit Data Structure
- Deal With Outliers
- Validate Your Data
- Data Cleansing is a Simple, Yet Effective, Form of Data Management
- By Emmett
- Published: Jun 28, 2022
- Last Updated: Jun 30, 2022
As the internet grows and integrates into our work, school, and entertainment, every facet of life is being transformed into tangible data. Each transaction, communication, and behavior occurring both online and in the real world can be translated into strings of information; the question is, is it all useful?
The truth is that every piece of information isn’t necessarily valuable. That's why it's occasionally necessary to clean up these data sets through a process called data cleansing.
What is Data Cleansing?
Data cleansing, also known as data scrubbing or data cleaning, is the process of removing elements of a dataset that have been deemed incorrect, duplicated, or irrelevant. This is done to help optimize the data for proper usage; once cleansed, data can be more easily interpreted and practically implemented.
It is a common form of data management and can help businesses distill large sets of data down into more useful and actionable information.
The process of performing data cleansing is simple but can be incredibly tedious. To cleanse a set of data thoroughly, you must go through each piece of information available within a given database and identify three five main components:
- Which pieces of information have been created in error.
- Which are duplicates of existing data points.
- Which information is incorrectly formatted.
- Which data points are blank or missing.
- Which parts of the data are irrelevant.
For the most effective data cleansing, the information you are reviewing should be from a single source or a group of closely related sources. This makes it easier to identify what is actually irrelevant and helps keep the task within a distinct set of parameters.
Without definite boundaries, it will be hard to know whether a piece of data is present due to error or inaccuracy. As technology progresses, more advanced solutions to data cleansing are being developed. There are Artificial Intelligence (AI) assisted programs and automated analytic processes that have been created with the express purpose of cleaning data, but the tech still has room to grow.
These programs can crawl through thousands of data points at a much faster speed than a person but are prone to making small errors. With manual data cleaning, the time required to complete a task can often be far longer but involves a smaller number of mistakes.
Identifying outlying or incorrect data can be easy with the right set of requirements, but the rigid input of some lower-level programs will mislabel and remove data that may be useful.
Why is Data Cleansing Important?
Because of the sheer volume of data that the modern company, or even individual, takes in over time, data cleansing offers a way to filter through endless files and documents to find what’s actually useful. For individuals, this means looking through all tax-associated paperwork, bank transactions, insurance information, mortgage documents, and any other the plentiful data we accumulate through the process of day-to-day life.
This form of data management can also keep you safe, as personal information is the primary way hackers can steal your identity. The more erroneous extraneous data you have sitting on your computer, the higher the chance that someone could use that information to open accounts in your name, utilize existing credit cards, or transfer funds out of your bank.
If you believe this may have already happened, running an identity threat scan is the best course of action. But if you simply want to ensure it won’t happen in the future, data cleansing can be one of the best preventative measures to take.
How Can Data Cleansing Help Businesses?
Just like individuals, businesses accumulate massive amounts of data that varies in value. Keeping this information organized can help companies provide better service for their customers; with more efficient databases comes the ability to be accurate when retrieving specific details, increasing productivity and client satisfaction.
Skipping data cleansing can lead to worse consequences than wasting a few minutes searching for a data point. A business strategy built on “dirty” data is unpredictable and dangerous. You could be throwing out the entire project’s budget on wrong assumptions. If the data was reasonably accurate, things might work out in the end, but you’ll have a hard time justifying your projections at the next meeting.
Data cleansing also reduces business liability, as any leak of sensitive information can have massive repercussions. A data leak can affect not only affect revenue but each employee and customer business associates. By cleaning up and securing databases, companies can be sure their cybersecurity budget is being used effectively.
How Often Should Businesses Data Cleanse?
If you're applying machine learning and artificial intelligence to your data cleansing strategy, then it's a constant process. However, for individuals or businesses that need to do everything by hand, it's best to lay out a rigid schedule for when to cleanse data.
Larger businesses that accumulate data quickly should make data cleansing a seasonal chore. This time frame allows them to make informed decisions based on prior years while maintaining a solid historical database.
Smaller businesses can get away with cleansing once a year. However, if they're planning to make a significant change, it's essential that they clean and review relevant data beforehand.
Data Cleansing vs. Data Transformation
Although they sound similar, data cleansing and transformation are separate steps for preparing information before it's used for running queries and analysis.
Cleansing ensures that all of your stored information is worth looking at. This prevents employees from wasting time combing through irrelevant or repetitive data. Data transformation is the next step. It involves restructuring the cleansed data into standardized units and distributions that are more easily analyzed.
For example, people record salary estimates and ages using vastly different scales. A person's salary measures anywhere from the thousands to the millions. When these factors are compared together, it's effortless to spot trends in salary, but much harder to see how age factors in when the numbers are so minuscule by comparison. Data transformation is responsible for scaling the salary factor down in a way that allows its impact to be analyzed without affecting the results.
It may seem that data transformation is more relevant, but it's essential that individuals and business owners properly cleanse it first. Transforming erroneous data is meaningless. You're just tidying it up, which is the professional equivalent of putting lipstick on a pig.
How to Cleanse Data Yourself
Your data cleansing strategy depends on multiple factors, including how big your business is, what kind of data you collect, and how you want to review it later.
If you're a new initiate to the world of data cleansing, you probably have years of dirty data built up. Tackling that mountain is extraordinarily time-consuming, and we recommend leaning on professional services for that initial cleanse. This will make future maintenance much more manageable and give your business a fresh start.
We'll go over broad methods for your next data clean but take some time before you start and see how you could tailor this process to yourself.
Purge Irrelevant Information
Consider what you want to get out of your data. Do you want to locate your target demographic? Are you wondering why customers are clicking away from certain pages? Your objectives determine what is and isn't relevant.
For example, a pizza place trying to learn its customers' favorite topping shouldn't factor in crust preferences. While the customers' favorite crust is important information, it's not relevant to the focus on hand.
Remove Duplicate Data
Data coming from multiple sources can be a nightmare. If you deal with various departments, public surveys, or participate in data scraping, then you'll definitely find duplicate data clogging up your storage.
Duplicates skew your analysis and ruin any AI models you're using the dataset to train. The "voice" of duplicate data gets louder, and the model artificially inflates that data's worth. You can think of it as giving certain people an extra vote in the next election. Policymakers would conclude that their constituency cares more about specific issues than they really do.
You should only remove exact matches. If a customer submits identical survey forms, then delete those duplicates from the database. However, if a customer submits two surveys but one has a single edit, both forms should be retained.
Edit Data Structure
Multiple sources often name and format their data differently. An international business may use the metric system or list dates differently from businesses in the US.
- 100kg = 220.46lbs
- 9-July-2023 = July-9-2023
These structural variables seem like a minor inconvenience, but if they persist through thousands of data points, keeping everything straight drains considerable mental stamina. You'll be doing all your colleagues or employees a massive favor by fixing them in the data cleansing process.
Look for errors like different spelling conventions, misspellings, capitalizations, or terminology. Many AI analysis programs can detect synonyms to some extent, but those features aren't foolproof. If you rely too heavily on them, an entire data set may be omitted from the process.
Deal With Outliers
Extreme outliers at either side of your data set can ruin your analysis. A company wouldn't include multi-million-dollar executive salaries to determine its employee's average wage. Doing so would produce a grossly inflated and dishonest result.
Handling outliers is one of the trickier aspects of data cleansing. Just because a number is outside the norm doesn't mean it's an error or that it's useless to your goals. These outliers could represent mistakes or successes in your marketing strategy and should be dealt with on a case-by-case basis.
Validate Your Data
This step is where you get to put a big red stamp of approval on your data. You review everything and ensure that the changes you've made align with future plans. In simple terms, you're double-checking your work.
Some things to check include:
- Can your analysis tools (AI) quickly scan and learn from your data?
- Have you removed or addressed blank inputs in your data?
- Is your formatting style easily understood by the people who will read it?
Data Cleansing is a Simple, Yet Effective, Form of Data Management
Unless you use specialized programs designed to comb through databases, data cleansing can be a bit labor-intensive. Depending on the size of your databases or the number of sources, it could take anywhere from several hours to several weeks to completely clean a data set. While a dedicated team will often make fewer mistakes than a single cleansing program, there is still the opportunity for human error.
Despite this drawback, data cleansing is one of the best ways to manage and optimize large pockets of information. By performing data cleansing on a regular basis, you can increase productivity and help focus your cybersecurity efforts on the data that matters. This, coupled with strong file protection and multi-factor authentication, can make your individual or business data far harder for hackers to access.
