As a company expands, its data assets expand in terms of quantity, size, variety, and format. There will be a data storage that includes duplicate records, redundancy records, missing information, damaged information, and more as business-related technologies evolve and new hardware and software are added. Everything you need to know about Data Cleansing is presented in this article.
As a business continues to grow, the number, size, types, and formats of its data assets also increase along with it. Evolution in business-associated technologies, the addition of new hardware and software, and the combination of data from various sources will eventually create a data storage that includes duplicate records, redundancies, missing information, corrupted data, and more.
The process to rectify and alter such data in a given storage resource and make sure that all the data are correct and accurate is called Data cleansing or data cleaning or data scrubbing.
Enthusiastic about exploring the skill set of Data Science? Then, have a look at the Data Science Certification Course Today.
The topics which we are going to cover in this article are as follows.
|Table of Content - Data Cleansing|
Managing data optimally and ensuring that it is clean can offer significant business value. Marketing surveys found that nearly half of the departments in a large business enterprise do not use data effectively due to redundancies and data complexity. Data cleansing can help businesses to achieve a long list of benefits which can lead to maximizing profits with less operational costs.
|If you want to enrich your career and become a professional in Data Science, then enroll in "Data Science Online Training" - This course will help you to achieve excellence in this domain.|
Improves Custom Acquisition-Related Activities: No matter the size, businesses can significantly boost their customer acquisition activities by cleaning their data. A more efficient potential prospect list with accurate data can be created more efficiently. Clean data will also ensure the highest returns on email campaigns as chances of encountering outdated addresses will be exceptionally low.
Better Decision Making: Precise data is the cornerstone of effective decision making Clean data supports better analytics as well as complete business intelligence which facilitates better decision-making and execution of business decisions.
Streamlined Business Process: Removing duplicates and the unnecessary database will eventually magnify business practices and save a good amount of money for businesses. With data cleansing, particular job descriptions of an organization can be determined. The accurate sales information obtained from a service or product can be easily assessed. Access to the right analytics with data cleansing will help enterprises to identify the right opportunities to launch services and products in the market.
Increased Productivity and Revenue: Access to a properly maintained and clean database can help businesses to ensure complete productivity of employees, and optimal use of manhours on productivity, thus resulting in increased revenue. Clean data reduces the risk of fraud, making sure staffs have accurate customer or vendor data for various steps of business operation.
|Related Article: Data Science Interview Questions and Answers|
This one is the first foremost step of data cleaning. It removes the unwanted observations from the targeted dataset. It has two steps; duplicate and irrelevant.
The next step of data cleaning is the fixation of structural errors. These types of errors mostly arise during data transfer, measurement, and poor data-keeping. Structural errors include mislabelled classes, name feature typos, use of the same attribute with different names, etc.
Unwanted outliers can cause serious issues with certain types of data models. When a user legitimately removes an outlier, it exceptionally improves the model’s performance. Thing to remember here is, that unless the outlier is proven unwanted or included with suspicious measurements, the user should never remove it.
|Related Article: Which One is Better? - Big Data vs Data Science vs Data Analytics|
This one is probably the most complex step of data cleansing. As most of the algorithms don’t accept missing values, the user has to manage the missing data in some way. The two most commonly recommended ways to manage missing data are:
Both of these steps are sub-optimal. The users simply drop information when they drop information. The second step is sub-optimal because of originally missing values that users have to fill. No matter how sophisticated the imputation method is, this always leads to a loss of information.
Data missingness is always informative in itself and the user requires to inform an algorithm if a value was missing. Even if the user builds an effective model to impute the values, it will not add any real information as it will be like reinforcing the patterns that are already provided by other features.
|Related Article: Data Science Tutorial for Beginners|
Previously known as Google Refine and Freebase Gridworks, OpenRefine is a popular open-source desktop application for data cleanup and transformation to other formats. Launched in 2010, it is available for Windows, macOS, and Linux.
It enables users of all skill levels to work with diverse, complex data within a desktop application without any cost. It works for self-service data preparation and data exploration analysis. It works both on on-premise and cloud data platforms.
This data cleansing tool brings self-cleansing capabilities to businesses. It is available both as a cloud service as well as a desktop application and has the extreme capability to cleanse data for a wide range of business purposes.
Cloudingo expertly consolidates data and eliminates redundancies to help organizations taking better and smarter decisions. It will help with better data load, data duplication, data confusion removal, and plenty more other data management purposes.
IBM Infosphere QualityStage offers an exclusive graphical framework that can be used to perform activities related to data cleanse and transformation. The programs run on the IBM InfoSphere Information Server engine.
JASP is an open-source and free graphical program designed for easy statistical analysis. It offers standard analysis procedures in both Bayesian and classical form. It has a great user-friendly interface and is specially developed for publishing analysis.
RapidMiner is an advanced and multipurpose data science software platform that can be used for data preparation, model deployment, machine learning, predictive analysis, and text mining. It can help businesses to drive better revenue, reduce costs and avoid data risks.
It’s a completely open-source machine learning and data visualization software available for both experts and novices. It can be used to perform simple data analysis with great data visualization, statistical distribution, box plots, decision trees, hierarchical clustering, linear projections, MDS, and more.
Talend data preparation is a free desktop tool that simplifies and automates data cleansing with a user-friendly visual platform. It enables users to quickly build reusable data preparation and it can also combine import and export data from an excel database or CSV file.
The TRIM function can be used to exclude the extra space. CLEAN and SUBSTITUTE functions can also be used combined with it. The TRIM function takes a single argument which can be a text that user manually types or a cell reference.
Select the entire database. Now access the find and select and select the Go to Special option which will open a special dialog box for your use. Click on the Special button and again it will open a special dialogue box.
Select the Blanks option which will select all the blank cells present in the data at the same time. To type not appear in all the blank cells just start typing not appear and press ctrl+enter and this will get into all the cells.
There are two steps to converting numbers from text format back into number formats. The first one is to go to the formatting box and type general and press enter. The second option is used for numbers in text format with the use of the apostrophe. To take care of this data issue, follow these steps.
It will change all the numbers with apostrophes back into a plain number format.
|Related Article: Overview of Data Modeling in Data Science|
There are two ways available to remove duplicate values in excel. The first one is conditional formatting. To perform this:
The second process starts by selecting the entire set. Now, go to the Data and select the option to remove duplicates. It will open the remove duplicate dialog box. Select the preference and press okay.
We can use three formulas to address this issue. The LOWER() receives one argument, either the text that the user types in or a cell reference. This will convert all the alphabets into lowercase. The formula UPPER() will transform all the alphabets into uppercase. The PROPER() formula is used to change the first letters of the sentence and name to capital and the rest will stay in lowercase.
As Microsoft Excel doesn’t have an automated spell check facility, it may create data errors. To address such errors, select the data set and click press F7. It will run spell check and correct the errors and show suggestions as well.
To clear all the formatting in an excel sheet, do follow these steps.
This one is the most challenging problem with data cleansing. The value correction to erase invalid entries and duplication removal is extremely necessary. But, in many cases, the information available for such data anomalies may get limited and inadequate to perform the necessary transformation.
In this case, the deletion of such wrongful entries is the only primary solution, which will ultimately lead to a loss of information.
No doubt, data cleansing is a highly time-consuming and expensive process. Having performed data cleansing, businesses have to avoid re-cleansing the data after values in data collection change. So, highly efficient data management and collection techniques may get required to properly maintain the cleansed data.
|Related Article: Python For Data Science|
In a few virtually integrated data cleansing processes such as IBM’s DiscoveryLink, every time the data is accessed, a data cleansing gets performed which highly increases the response time and decreases efficiency.
Due to the incapability of deriving a complete data-cleansing graph to operate the whole process in advance, data cleansing lists as an iterative process which involves significant interaction and exploration. It will require an appropriate framework consisting of error detection, elimination, addition, and data auditing methods.
The framework can also be integrated with other data processing layers such as integration and maintenance.
Data cleansing is a must required step to maintain the data integrity of any business organization. The ability to detect and rectify problems, filter out unnecessary data and enrich the day to day operations, make this a necessity for any type and size of business. Where large corporations hire data scientists and engineers to monitor their data collections, small and medium businesses can rely on easily online available data cleansing tools to validate their data from time to time.
Stay updated with our newsletter, packed with Tutorials, Interview Questions, How-to's, Tips & Tricks, Latest Trends & Updates, and more ➤ Straight to your inbox!
|Data Science Training||Oct 08 to Oct 23|
|Data Science Training||Oct 11 to Oct 26|
|Data Science Training||Oct 15 to Oct 30|
|Data Science Training||Oct 18 to Nov 02|
Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.
Copyright © 2013 - 2022 MindMajix Technologies