What is Data Cleaning?
Data cleaning, also known as data cleansing, is the set of steps involved with preparing data to be analyzed. It pertains to modifying or deleting any data that is incomplete, irrelevant, duplicated, improperly formatted, or incorrect so that such data will not lead to inaccurate results down the line. This process is typically more complicated than erasing existing information and replacing it with new data. It also can involve discovering ways to maximize the accuracy of the data in a set so that it doesn’t have to be eliminated. Actions such as standardizing data sets, correcting missing codes and empty fields, addressing syntax and spelling errors, and spotting points where data has been duplicated all fall under the umbrella of data cleaning.
A variety of methods can be used for data cleaning. Selecting the most appropriate one can depend on the type of answers being searched for, as well as how the data is stored. The act of data cleaning is one of the core components of data science and data analytics as it helps to ensure that the answers discovered in the analytical process are as reliable and helpful as possible.
There are many benefits data cleaning provides, such as:
- Increased efficiency: Not only does working with clean data provide benefits for a company’s external needs, but it also can improve in-house productivity. The act of cleaning data can uncover insights into a company’s needs that may otherwise go overlooked.
- Better decision making: The better quality of data a company has to work with, the more likely it will be to implement effective strategies and make important decisions.
- Remain competitive: Companies who are able to meet and exceed customer needs are in a position to outperform the competition. Working with clean, reliable data is a valuable tool that allows a business to stay abreast of new trends as well as customer needs. Acting on this information provides quicker responses and ultimately a better customer experience.
This article will explore eight of the best tools for data cleaning in 2021.
8 Best Tools for Data Cleaning in 2021
The following are a list of some of the most helpful tools currently being used for data cleaning:
- RingLead: This platform is more than a data cleaning tool; it provides an end-to-end solution for marketing and CRM automation data. It includes options for duplicate prevention, data enrichment, normalization, deduplication, data scoring, prospecting, and list building, among others.
- SAS Data Quality: This data quality solution platform allows users to clean data in its current location instead of having to transfer it. It has features such as data remediation, correction, and entity identification. One of the benefits of using SAS Data Quality is that it can work with a large range of data sources.
- Oracle Enterprise Data Quality: This data platform was created to help with data quality management. It is able to generate sound master data that can ultimately be integrated with various business applications. It includes standardization, profiling, real-time and batch matching, and address verification, among other helpful tools. This solution is mostly used by those with advanced technical training.
- Informatica: This intelligent data management cloud utilizes a self-service approach for those interested in improving data quality and governance. It allows users to employ prebuilt rules in order to streamline the processes of data enrichment, duplication, and standardization.
- Melissa Clean Suite: This application for data cleaning comes with features for contact autocompletion, data deduplication, data enrichment, and verification, as well as real-time batch processing. Once data has been entered, Melissa Clean Suite proactively works to maintain the quality of data. Melissa Clean Suite can be added to most CRM or ERP platforms using the included plugins. Even though this application is often used in the marketing sector, it offers users in all industries an effective, time-saving data management tool.
- Xplenty: This comprehensive data pipeline platform provides users with replication functionality, as well as ETL and ELT functionality, which enables them to create a graphic interface that requires no coding. Xplenty provides a user-friendly approach to creating data pipelines, one that can be utilized by all members of an organization, even those who aren’t formally trained with advanced data science practices. This cloud-based platform provides regular maintenance that is offloaded to Xplenty.
- Tibco Clarity: This platform is especially helpful for projects that involve interactive data cleaning. Several data quality improvements are streamlined in Tibco Clarity via its visual interface. In addition, any kind of raw data can be run through this platform so that it is ready to be used in various applications. While the data is being processed, Tinco Clarity provides options for data visualizations that can be used to visually convey the information. One of the main benefits of working with this platform is that once the data cleaning process is established, this configuration can be reused in the future for other sets of raw data.
- Data Ladder: This visually-driven data cleaning application was designed to handle datasets that are in bad shape. Considered easy to use and instinctive, Data Ladder provides a walk-through interface that provides guidance for the entire data process. This scalable application relies on a range of import and export functionality that allows users to create Excel spreadsheets, basic reports, and database tables, among others. Because Data Ladder is scalable, users can work with both large and small datasets to perform extractions, standardizations, data matches, and deduplications. In addition, Data Ladder includes a helpful scheduling function so that users can pre-set data cleaning tasks for future days and times.
Data cleaning plays an integral role in the data analytics process. With the help of tools and platforms such as those mentioned above, users can ensure that the data they are working with is as error-free and accurate as possible, which will lead to a better end result for their company or business. Regardless of which data cleaning tool or tools you ultimately select, the good news is there are many helpful options available to meet your organization’s needs.
Hands-On Data Analytics & Data Science Classes
If you are interested in learning more about the various tools that are currently available for managing and visualizing big data, Noble Desktop’s data science classes provide a great option. Courses are available in-person in New York City, as well as in the live online format in topics like Python and machine learning. Noble also has data analytics courses available for those with no prior programming experience. These hands-on classes are taught by top Data Analysts and focus on topics like Excel, SQL, Python, and data analytics.
Those who are committed to learning in an intensive educational environment can enroll in a data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 40 bootcamp options are available for beginners, intermediate, and advanced students looking to learn more about data mining, data science, SQL, or FinTech.
For those searching for a data science class nearby, Noble’s Data Science Classes Near Me tool makes it easy to locate and learn more about the nearly 100 courses currently offered in the in-person and live online formats. Class lengths vary from 18 hours to 72 weeks and cost $915-$27,500. This tool allows users to find and compare classes to decide which one is the best fit for their learning needs.