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What is Data Cleansing?

Read this blog to learn about what is considered clean data and how to use a data cleansing tools to automate data scrubbing.

Dec 5, 2022
15 minutes to read

What Is Data Cleansing?

Data cleansing, also known as data cleaning or scrubbing, is a form of data management that aims to fix or update data in a dataset or database. Over time, information collected and stored in databases can become outdated, incorrect or corrupted. If you’re using data to predict future trends, incorrect entries can skew your results or give you inaccurate findings.

Other problems resulting from messy or erroneous data sets are losing leads and making many mistakes in fulfilling product or service requests. Cleaning data is one way to prevent these issues. In addition, when you make data cleansing a staple in your data management process, you can be more confident using your data for business intelligence (BI) applications.

In this blog, we’ll answer your question of “what is data cleaning” and discuss its steps and benefits.

What is Data Cleaning?

Data cleaning is a process that includes the following tasks:

  • Removing incorrect, irrelevant, invalid, duplicate or corrupted data.
  • Correcting data that has been  incorrectly formatted.
  • Updating old data entries.
  • Identifying data errors.

Issues about data integrity and accuracy often surface when you combine data from different sources into one spreadsheet or database. Unless the data was collected by the same people who followed the same procedure, there are bound to be inconsistencies in how data is collected, collated and presented.

For example, let’s suppose that the membership form for your customer rewards program had 20 fields. After receiving and analyzing data for six months, you realize the form asks for a lot of information you don’t need. Moreover, a conversion rate optimization (CRO) expert discovered that many of your leads are discouraged from applying when they see your lengthy membership questionnaire. So, you decided to remove the irrelevant fields and kept only eight questions – one of which was two questions merged into one.

You now have two data sets: one with 20 fields and another with eight. Let’s suppose again that you want to simplify your records and merge the new applications with the existing database. Unfortunately, the program you’re using makes errors combining the data and the answers for the two-in-one question end up in the wrong column. It’s now necessary to clean the data to ensure your customer records are accurate.

Now that we’ve covered what data cleaning is let’s discuss how to clean data so that it can be part of your data management SOPs.

What is Clean Data?

Data is “clean” when authorized personnel can vouch for its quality. Data that has undergone scrubbing or cleansing is:

  • Accurate
  • Up to date
  • Complete
  • All values are valid and relevant
  • Consistent with other values in its data set
  • Correctly formatted, labeled and classified
  • Has no duplicates

Every organization should take pride in being data-driven and maintaining a reputation for accuracy and integrity; therefore, you cannot afford to have “dirty” data. Releasing findings and offering services based on erroneous data wastes time and will be detrimental to your operations. Therefore, it’s best to manage and “clean” data regularly to ensure quality.  

Tips on How To Clean Data

The standard data-cleaning process includes the following steps:

  1. Inspection: This is where you will need to audit your data to assess its quality. Should you set it aside and gather new information, or does the data set still have something usable that you can use? The decision will depend on what you need the information for.

For example, if you need historical data for projections, your data set must be accurate and relevant to your analysis. If you’re investigating transaction fraud, you may have to look at outlying values instead of dismissing them.

Inspecting data gives you an overview of what the data set is about. If you already know what you’ll use it for, you can quickly identify the sections you need, patterns to look out for, and outlying information irrelevant to your goals.

  1. Data profiling: This step studies the relationships between values and data elements. It determines errors and discrepancies you need to address. Data profiling also helps prepare your system for scraping or merging data from different sources.

Here’s an example. If you need to match your sales and fulfillment teams’ data, you can create a new data set that shows the number of contracts signed and fulfilled at a glance. But first, you must specify the data you want to include in this data set. Data profiling makes this easier.

  1. Cleaning: A large chunk of the work goes into cleaning data. It includes correcting values, updating values, deleting duplicates, aligning inconsistencies, removing irrelevant data and more. It is time-consuming when done manually, so it’s best to have a data cleansing tool to automate identifying and correcting data errors.

  1. Verification and quality control: When you’re done cleaning data, you or authorized individuals have to check its accuracy and overall quality. They should verify if the clean data meets internal standards for quality.

  1. Reporting: It's beneficial to the IT department to provide detailed reports on the errors discovered, especially the ones due to technical glitches and bugs in the data collection tools. These include website forms, file-sharing cloud platforms and scanners that convert printed or handwritten text to digital format. The report can also include new quality metrics and recommendations on improving and updating other data sets.

The details of this procedure can vary depending on many factors. For example, you might skim through the inspection and data profiling if you scrub data regularly and know what's wrong with your data set and how to correct it.

When you're done scrubbing data, you can prepare it for business analytics or data transformation. This is the process of converting data from one format or structure into another.

The Benefits of Clean Data

Going through the trouble of cleaning databases is worth the benefits your business or organization can enjoy. These are just a few of the benefits:

  • Accurate projections and data analyses.
  • Improved decision-making.
  • A better understanding of your audiences, target market, competitors and industry.
  • Better ability to identify high-value leads, new profit opportunities and business prospects.
  • Improved efficiency among internal departments that collaborate or work heavily with data.
  • Improved transparency and accountability.
  • Lowered compliance risks.
  • Streamlined processes.
  • Reduced waste of time and resources.
  • Boosted productivity.
  • Reduced risk of sending unsolicited content or information to customers.
  • Improved marketing and internal and external communication.

Data cleansing will take a lot of time and resources when done once in a blue moon. But if done regularly and there's progress with data management after each cleanup, successive cleansing sessions should get easier and faster.

Use a Data Cleansing Tool To Automate Data Scrubbing

Data cleansing is crucial for making critical business decisions, executing marketing strategies and more. Keeping data sets valuable to your business or organization is also essential. Unfortunately, it takes a lot of time and effort to manually comb through large datasets and ensure that the information is correct and updated.

Fortunately, it’s now possible to automate data cleansing and cut down the time you spend on it while improving data scrubbing efficiency.

A data cleansing tool can help you quickly inspect, profile and assess data without learning complex coding or filtering techniques. More importantly, you can customize your tool to scrub data according to your preferences and needs. Data cleansing tools also come with extra features, like report generation, exporting unstructured data into user-friendly formats like Excel and detecting data patterns.

Ikigai, a generative AI platform for tabular data, can integrate over 200 data sources (e.g., AWS, SAP, MongoDB, Google Drive, Instagram Business, Mailchimp, MySQL Database, etc.) to perform data cleansing. Through Ikigai, you can customize data pipelines for data transformation, integration or scrubbing. Moreover, you can forecast trends and perform other BI analytics on the Ikigai platform.

If you’d like to learn more about how Ikigai works as a data cleansing tool, book a demo or check out our FAQs for details.

In this article:

Authors:

Team Ikigai

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