What is Data Cleansing | Hydrogen Blog
What Is Data Cleansing?

What Is Data Cleansing?

Data cleansing, or data cleaning, identifies, fixes, and deletes inaccurate information stored in company systems or databases. This method helps keep information up-to-date for efficient and high-quality work for your employees and solutions for your customers. When you have clean data, you have reliable and accurate data that you know you can use to make strong business decisions, and provide better solutions to your customers.

Understanding what data cleansing is and why it is important can help prepare you for cleaning your data in the future.

Types of Unclean Data

When preparing to clean your data, you need to know the different types of poor data to look for. There are four main types of unclean data, each with different consequences if left as-is. For each of these data types, we’ll use a problem with an email address as an example:

  • Duplicate data: This kind of poor data results in two of the same input data. When departments use that data to carry out actions, duplicate data can result in unnecessary results. For example, if there are two identical emails, the recipient might receive more than one copy of every email sent out using that data, leading to annoyance and frustration.
  • Conflicting data: Conflicting data is when there are two sets of data that provide different information. This might mean two different emails are listed for one recipient, causing the department to send information to the wrong address or overwork and send information to both.
  • Incomplete data: This is when information, such as an email address, is missing or not complete. This can cause employees or customers to not receive essential updates or messages.
  • Invalid data: This type of data refers to information that doesn’t meet its standards, causing actions not to carry through. Here, this might look like an email address without the at-sign or email domain, making the address invalid and nonexistent.

In all of these situations, the departments responsible for working and carrying out actions with that data will have to put in more time and work to correct data and even manually do actions, wasting company time and money.

What Is Clean Data?

Clean data is quality data you can use to drive decisions and interactions with your customers, better utilizing your time and money. This data will be:

  • Valid: The data meets a specified set of rules determined by that business or industry. Valid information can cover various data types. For example, a data set might have a determined format for entering phone numbers using dashes and parentheses. Any data without those factors is invalid.
  • Accurate: In this category, data must match the information it connects to. Here, data cannot just be factually correct — a phone number listed on a data set might be a real phone number that exists, but it is not the phone number for the company it pairs with.
  • Complete: All required data must be present for the data to make sense. Letters, numbers, or fields might be missing for incomplete data, impeding the functions companies use that data for. In this instance, there might be a missing digit in the telephone number or no number listed at all.
  • Consistent: Consistent needs to occur within and across data sets. Further, for data to be consistent, it also needs to meet the same validity standards. Different phone numbers and phone numbers with different formats are both examples of inconsistent data.
  • Uniform: For all data to be uniform, it needs to fit the same units. This again relates to validity so it can fit a measurement reasonable for that specific unit or standard. The data set might follow the American way of writing phone numbers or the European standard for a phone number.

When considering the quality of your data, you should also consider how timely and traceable your data is. Timely data is recent and relevant — when you use it, you know it is up-to-date and accurate for any changes. If you collect personal data, which can change frequently, the accuracy of your data must be timely.

Traceable data means you know the source of the data. With all of your data, you should know and understand its origins. This is especially important when merging databases since many unclean data sets are a result of transferring and combining data. Further, if you need to check the accuracy or complete the information, it is helpful to know where you can access the original data.

How Does Data Cleansing Work?

Data cleansing can be a manual or automatic process, depending on the tools and experience you have ready. If you have dealt with unclean data before, you might have a system in place to check data input regularly or find suspicious data. However, you can also manually clean your data to ensure quality and accuracy.

In manual situations, you need to go through all of the data step-by-step to identify and correct each type of unclean data. However, you can use this experience to analyze instances of unclean data and discover the root of the problems to help prevent unclean data in the future.

What Are Different Data Cleansing Techniques?

Whether you are manually cleansing data or using a tool, four standard processes occur during data cleaning:

  • Identify: Locate different instances of unclean data in your databases. It is also helpful to understand what type of unclean data it is. At this stage, you can also identify any patterns you might have, which can help you cleanse data in the future or point out a larger problem in data collection processes.
  • Correct: After finding and identifying unclean data, you can then correct the data. Depending on the type of unclean data, you may have to have different approaches to fixing them.
  • Confirm: Once you finish correcting all of the data, you will need to verify that it fits all requirements set by your business. This means checking to ensure it meets validity standards for that type of data and works with other clean data requirements.
  • Reveal: Data transparency is critical when using data. After successfully cleaning your data, publish and prove the data so your business can analyze and use data with the knowledge that it is safe and accurate.

Each step in the data cleaning and validation process is essential for ensuring the best quality for your data sets so you can make the best business decisions using them.

Data Cleansing Tools

Manual data cleansing can be time-consuming and costly, taking away your focus on important work. Data specialists can spend 50% to 80% of their time working through data cleansing processes rather than analyzing data instead. However, clean data is necessary to run your business and make strong decisions and services because of that data analysis. Data cleaning tools can help you through the different data cleansing processes.

When checking your data, you can use algorithms to identify and even correct specific mistakes in data. Algorithms can quicken the identification and correction processes of cleansing data, allowing you to spend more time analyzing data. Simple algorithms are better for checking unclean data to optimize the algorithm and allow fewer mistakes.

However, algorithms are not foolproof, so it is best to use more than one tool when checking your data. Visualizing data through graphs and charts can help point out areas where you may need to check data for mistakes. With many different types of graphs to choose from, graphs and visuals exist for almost every data type. Histograms are very popular for visualizing data.

Data profiling and cleansing often go together. Through statistics and summaries, you can ensure that your initial analysis matches the overall goal of your data. You can use both data profiling and visualization together to check qualitative and quantitative information.

Data Cleansing Services

Services provided by a third party are a great way to save time on different data cleaning processes so you can spend more time analyzing and applying your data. Many companies offer excellent data cleanings services that you can use to carry out different functions and help you clean your data.

Especially if you work in a particular industry and provide many services to your clients, using a data cleansing service that offers cleaning processes specific to your industry can provide the best impact.

What Are the Benefits of Performing Data Cleansing?

Performing regular data cleansing offers many benefits to your business, from the daily productivity of your data analysts to your yearly financial performance.

Make Stronger Decisions

When you know you have accurate data, you can feel more confident about the decisions that data leads you to. You have access to data that can support decisions, allowing for better business with clients and within your company. Businesses can invest more in other projects and clients knowing that their choices have the support of data.

Better Client Retention

Clean data can allow you to better serve your clients. When you have their information sorted and organized, you can better interact with them and address their needs. Further, with access to accurate and singular data, you can improve their customer experience by not sending them too many messages or forgetting to share information with them. Clean data will help satisfy your clients and keep them from seeking business elsewhere.

Improve Productivity and Efficiency

Because data cleansing takes so much time, unclean data affects 20% of employee productivity. With regular data cleansing practices, your employees can work more efficiently by using their time to analyze and apply data rather than discovering and fixing problems with databases.

When you use frequent data cleansing, you can get more out of your efforts. You can avoid doing redundant tasks because of repetitive data or spending efforts on inaccurate data, causing employees to redo and rework tasks and projects to fix errors. Data cleansing allows you to know that you are working productively and efficiently without repeating tasks or overworking.

Increase Annual Revenue

Annually, businesses lose around $9.7 million due to unclean and low-quality data. Unclean data can lead businesses to overwork and overspend on even the smallest projects, like sending out emails or reminders to clients and customers. Further, companies risk losing customers and opportunities with inaccurate data.

Additionally, data problems are expensive to fix. The 1-10-100 rule helps estimate the costs of fixing various issues that come with unclean data. Each time you collect data, it costs $1, but for every instance of an error and unclean data, it costs $10. For overarching and outside problems that your unclean data causes, it costs your business $100. Continually having unclean data can end up being costly for your company.

By regularly cleansing data, you can save money by optimizing your business efforts with accurate data. You will not have to worry about inadvertently overlooking clients or growth opportunities that can benefit your company. Your clean data will serve you financially and save you overspending.

Why Is Data Cleansing Important?

Data cleansing is necessary for all businesses to make decisions and provide information based on accurate data. When you have access to consistent and precise data, you can protect your company from business-related liabilities. Poor quality data can lead to misjudging your company’s capabilities. Further, if you provide services and advice directly to your clients, you can be liable for decisions you advise them to make based on your unclean data.

Data cleansing allows you to best prepare your data before utilizing it, ensuring the quality of your company’s information. With strong data, you can take more chances with your business, allowing you to grow and set yourself apart in your industry.

With secure and clean data, you can take more risks in growing areas. Programs like artificial intelligence and machine learning create more opportunities for many industries, but they require the data these programs and their users rely on to be clean.

Challenges in Data Cleaning

Because of its intensive process, data cleansing comes with some common challenges, including:

  • Large quantities of data: When users have large databases they need to clean, it can be a daunting task. Data cleansing is time-consuming, but regular cleaning is important to ensure accuracy, especially with more data present. Finding the right method, tools, and services can help with larger amounts of data.
  • Abbreviations: Shorthands and abbreviations make it difficult to understand the meaning of data and how to best sort it when organizing it. Further, abbreviations lead to higher chances of duplicated and inconsistent data in a set of information. Instead, setting clear standards for validation can help prevent the use of abbreviations.
  • Location of problems: One of the largest problems with data cleansing is recognizing unclean data inputs but not knowing how to locate them, especially with larger databases. Visualizing and profiling data can help locate the source of the issue.

By regularly cleaning your data and utilizing good data cleaning habits, you can help ease the strain of the challenges on you and your business.

Leverage Data Cleansing Services With Hydrogen

Data cleansing can provide your business with many benefits, allowing you to grow and increase your performance. However, data cleaning is a comprehensive and intensive process that can overwhelm companies on their own. Using services provided by a third party can help ease the duties that data cleaning puts on your company while offering you top-quality data.

At Hydrogen, our data cleansing capabilities allow you to focus on your business while you receive clean data. Sign up with Hydrogen today and learn how we can assist your company.

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