What is Data Cleansing?

What is involved in data cleaning

Data Cleaning, also called as data cleansing, is the process of correcting & reducing inaccurate data and improving the quality of input data. This is done by identifying and resolving problems in a dataset such as inaccuracy, error, outdated data, irrelevant data, missing data, incorrectly formatted data, duplication or inconsistent data. This is done to ensure that only high-quality data is transmitted to the target system and a better output is obtained.

Data is undoubtedly one of the most important assets an organization. Data driven decisions are taken by companies to support and guide its success. The purpose of data cleansing is to clean up the database to ensure that only high-quality data remains. Over time, databases become entangled in data that is false, duplicated, or outdated. If a database collects inaccurate and outdated data, the data cannot provide the same benefit for marketers. By focusing data analysis on the most up-to-date and relevant information, useless records can be removed from the database.  This it is important that the data cleaning must be completed before the statistical analysis of the study results begins.

Qualities of data

High quality data can be defined as data which passes certain filters and criterias. The importance and weightage of these criterias might differ depending on the useage. Some of the most important criterias are mentioned below:

  • Validity
  • Accuracy
  • Completeness
  • Consistency
  • Uniformity
  • Integrity
  • Convertibility or interchangeability

Data Cleaning

Data cleaning in action for a dataset

Data cleansing techniques can be performed in various ways and helps with data presentation, data processing or data storage. If data comes from multiple sources, such as a data warehouse, it may need to be cleaned up because some sources may contain redundant data or incompatible data formats.

While performing data cleaning for a dataset, once a required set has been cleaned up, it should match other related subsets related to this particular operation. At the end of the operation, all the datasets should be compared with each other in the same database to remove any inconsistencies. This can be achieved by replacing, modifying or deleting data that falls into one of these categories (e.g. data in the form of columns, tables, charts, graphs, etc.). This verification can be very strict or even fuzzy, but it is necessary to ensure that the data is trustworthy, consistent and correct. The actual process of data cleanup involves the validation of values based on a known list of units. This validated data set is then re-evaluated by a solution that cleans it up in the form of a new value set. There may be a number of different ways to identify and eliminate data, such as using multiple data sets or a combination of both.

In other words, it is a process of sifting through a large amount of available data to find the most accurate, consistent and consistent version of the available data. Data cleanup is the removal of data that has passed through the database, such as incorrectly formatted or duplicate information. The difference between data cleanup and data enrichment is that data cleanup removes discrepancies and discards old or inaccurate data, while data enrichment (which we discuss in a different article), means adding data from different sources to a data set to obtain a more complete profile. Although information can be deleted, data cleanup focuses on updating, correcting and consolidating data to ensure that the system is as effective as possible. The data cleanup usually takes place once and can take a while, especially if the information has accumulated over the years.

Importance of data cleaning

Businesses are expanding their operations, and the more resources needed to maintain accurate databases, the greater the need for data cleanup. Data cleansing is a necessary business function that can be seamlessly outsourced and helps maintain the business – high quality critical information. Companies with multiple branches, where employees work with the same customers at all locations, make it more difficult to clean up data, as incorrect customer data can damage the business. Since companies collect all kinds of data to make crucial business decisions, it is important to use a data-clearing process. A database overloaded with outdated, incomplete or inaccurate information could cost your business a lot of time.

  • It helps detect and correct inaccurate or corrupt data in databases. With Data Cleanup, you can
  • ensure that important business decisions based on customer data, customer information and other important business information are correct, consistent and of high quality.
  • Having clean data is one of the most important aspects of a successful data management solution.
  • Creates a standardized and unified data set that enables business intelligence and data analytics tools to easily access and find the right data for your queries

Data cleansing is a crowded market where most providers focus on end-to-end data management solutions. There is no ladder to focus solely on the process of data cleansing, but you should not underestimate the importance of data cleansing in your company’s decision-making process.

Data-driven marketing can help improve marketing ROI and enable companies to respond quickly to changes in customer dynamics, such as why customers buy a particular product or move to a competitor. Multi-channel customer data can also be managed to provide companies with the information they need to run successful marketing campaigns, so they would be able to reach their target audience with methods to reach them effectively.

This means that if you have data that is corrupt, inaccurate or otherwise out of date, it will not produce results, because even if it helps you make marketing decisions, those decisions will at least not be as effective as they might be. This is partly why the regular removal of outdated and inaccurate data is so important, but there is more to it than you might think. For this reason, you should not neglect to clean up your data just because you want to use it to power your business. In part, this is why it is not only important, but also necessary, to clean data regularly to remove outdated or inaccurate data.

Data cleansing can be a game changer for both individual data scientists and organizations, bringing many benefits, so much so that some organizations will see cleansing high-quality data as one of the most important aspects of data cleansing. For example, if a tight sales team or a marketing team with top quality, data and accurate information can implement more than half of their leads, it leads to better sales and more leads. 

How to do the data cleansing – Automatic or Manual?

Manually scanning through billions of data can be a daunting and nearly impossible task and at same time error prone. This makes it difficult for analysts – driven organizations – to scan the data for errors. Given the increasing reliance on data to derive strategic business insights, poor data quality increases an organization’s risk. Error management and free data have become important in most industries that rely on large amounts of information such as financial data, financial reports and business intelligence. Data laundering and data cleanup have gained acceptance – towards methods for editing and removing poorly formatted databases.

Data Cleaning uses this information to avoid future errors and can also help identify and correct the causes of errors. There are various data cleanup tools that can help you keep your data clean and consistent as you analyze it to make informed decisions visually and statistically. Preparation for data collection is essential to obtain high-quality and reliable data sets that allow valid statistical analysis.

Related: Importance of Data Processing, What is Data Mapping 

When dealing with a particular dataset

While dealing with a particular dataset, data cleanup can also be understood as the process of identifying and removing invalid data points from a data set. As a result of the data cleanup, the records should match the information given in the code book, such as missing values, errors, and other errors. This includes investigating extreme outliers or erroneous data points that can distort research results. To ensure that the data does not boil over, the data must be cleaned up before the statistical analysis of the study results begins. Try to minimize errors during the data entry phase, as it is more important to avoid errors in advance than to recognize and correct them later.

Many problems associated with data cleanup are similar to those experienced by archivists, database administrators, collaborators, and others in targeted data mining, where old data is reloaded with new records. Careful analysis of records can show that merging multiple records can lead to duplication. In this case, a data cleanup could be used to fix the problem, or it can use parsing or other methods to get rid of duplication of data, such as using multiple records in the same file.

In an effective cleanup, the dataset should be free of errors that could be problematic for later use in the analysis. By and large, data purging or purging is about correcting or deleting inaccurate records from database tables. Detect and replace inaccurate data, such as errors in the database table or in a database file, and correct or delete it.