With the internet growing faster and faster, there is an ever-increasing data supply. Many businesses use the data they collect daily to support their overall success.
Making sense of big data is one of the hottest trends in the tech world. Without proper tools and processes to cleanse the ever-expanding data supply, it can lead to accuracy issues.
Improving your business through data starts with a healthy perspective on data cleansing. The volume of data available on the internet is rising at an exponential rate. It can be a challenge to ensure that all databases are adequately managed.
Data cleansing ensures nothing is wasted, and you can draw a reliable picture from all available information.
In this thorough guide, you’ll learn everything you need about the best practices and the importance of data cleansing for your business.
What is data cleansing?
Data cleansing, also known as scrubbing or data wrangling, is evaluating, standardizing, and correcting data quality to improve its suitability for analysis. It can be manually, semi-automated, or automated.
Data is critical for businesses nowadays. According to MicroStrategy’s latest report, 60% of companies worldwide use data and analytics to improve process and cost efficiency.
In organizations, data cleansing is an important part of data analysis, which helps ensure that analytics results are accurate and valid. It also helps ensure that your business is not violating any regulations by keeping your customer data up-to-date.
Data cleansing is an essential part of data management. It involves correcting, updating, and deleting outliers, transforming categorical variables into numbers, removing duplicates, and cleaning dirty fields.
When it comes to data, there’s no such thing as 100% accuracy. There will always be some discrepancies between your source and destination systems – even if they are from the same company.
Through data cleansing, you can have consistent, accurate, and up-to-date data sets, meaning you can trust it when making important decisions about your business.
6 data cleansing best practices in 2023
Here are the six data cleansing best practices you can apply to your business:
1. Implement a data quality strategy
Start with a data quality strategy that defines how you’ll manage your data over its lifecycle.
A comprehensive data quality strategy will help identify:
- What type of cleansing you need to do
- The types of errors that must be corrected
- Which methods will be most effective
In addition, it should include policies and procedures for handling data errors rather than simply fixing issues individually as they arise.
This will ensure that your organization has an understanding of what data cleansing entails as well as how it benefits the business.
2. Remove duplicate and irrelevant observations
Duplicate records are common in big data sets because they’re often collected from multiple sources or entered manually by different people.
They can also occur if you’ve merged tables by mistake or have multiple instances of the same record because of missing values or different formatting.
In this case, you can remove duplicates by finding them using statistical methods like frequency analysis or exact matching algorithms.
Duplicate observations can cause problems when you want to compare things. However, you may want to keep some duplicates in case you need them later. But ensure you understand why each record exists before deciding whether or not to keep it.
3. Correct data at the point of entry
Another step in data cleansing is ensuring errors are corrected when entered. This means ensuring that every piece of data entered into your system has been validated before being added as part of a batch upload or import process.
To do so, vendors should have a formal data quality policy (DQP) that allows them to identify, verify and correct any errors as soon as possible. This practice helps improve customer experience by providing accurate information immediately.
It’s much easier to correct a mistake when you first notice it rather than months when it becomes part of your production database.
This reduces the risk that any mistakes will be made at a later date when trying to correct them manually through multiple sources.
4. Fix errors
Errors can happen anywhere within your organization, ranging from typos to duplications or inconsistencies in names or addresses.
Fixing errors in your database using different columns to create a new field with only valid values is also essential in data cleansing.
If you don’t do this quickly enough, your business could suffer serious consequences from sending out incorrect information or making poor decisions based on bad data.
5. Handle missing values
Missing values are often overlooked or misinterpreted, but they can cause major problems in your data sets if you don’t pay attention to them.
You’ll need to decide whether to replace them with default values or delete them completely from your dataset.
Either way, you should always document how they were handled so that other users know what happened later on down the road if they need access to that information again.
6. Validate and QA
Validating the quality of your data is a critical step in the data cleansing process. Many different techniques can be used for validating data. You can look for field values that don’t make sense, compare them against a known list of valid values, or run statistical tests.
Various tools are available to help you validate your data, but there are also open-source options that may work for your needs.
Thus, data cleansing must begin with validation and quality assurance (QA). These reports help you know what areas need attention first, as they may indicate which records are more likely to be incorrect or incomplete than others.
Why is data cleansing important?
While data cleansing seems quite complex, its effectiveness ensures that your company is in the best position to make data-driven decisions.
Here are other reasons why data cleansing is important:
More accurate insights and reliable predictions
It’s easier to make decisions based on correct information. When you have faulty data, your business decisions will be based on incorrect facts and figures, leading to wrong decisions.
Inaccurate data equates to inaccurate predictions, which may be costly for your business. If there are any errors in the scores, your predictions could be off base — leading to bad investment decisions or even lost sales opportunities.
Data cleansing ensures you use only the best quality data for your analytic projects. You don’t want inaccurate information influencing decisions affecting your company’s future.
Drives faster customer acquisition
If you have outdated or inaccurate information about your customers, you won’t be able to effectively target them with your marketing efforts which can lead to poor customer experience.
You may not realize how much damage this can cause until it’s too late. The more accurate the information in your database is, the better your chance of getting new clients.
Thus, it’s important to ensure your customer data is accurate before you start sending out campaigns.
Increase revenue
Data cleansing helps you increase revenue by making sure your marketing campaigns target potential customers likely to buy your product or service.
You should have accurate customer data for marketing purposes to target the right people with appropriate messages through various media channels.
The more accurate your data, the better you can understand your customers and provide them with relevant offers and experiences. This increases customer satisfaction and loyalty, which leads to increased sales.
