The Essential Toolbox for Data Cleaning

The Essential Toolbox for Data Cleaning

Data cleaning takes up 70% of the whole data science process..

Data scientists all around the world spend nearly 80% of their time working through rudimentary heaps of different data types. This stage paves the way for data analysis, analytical model creation, visualization etc., and ensures concentrated value of the data. Naturally, the world that is imperfect on good days cannot produce data in any other state. It is imperfect, dirty, disorderly, and huge in volume, even more so in the real-world tasks. Cleaning of this messy data is tedious and difficult, but it is too significant to be neglected.

Data (more like correct data) is required by every sector today, whether it’s corporate, government, or nonprofit, for number of varying reasons. For instance, some businesses need data for optimal operations and decision making, while others require data for future event prediction before making investments in a range of commercial initiatives. Some still need it for consumer behavior analysis or to study current market trends and pattern.

At this stage of our data science series articles, we have decent ideas that the process of data extraction or data mining can be performed through different passive and active methods. Some of the most common data collection mechanisms include online surveys, feedback, comments, consumer participation, social reactions, or any given act that contributes towards information gathering for a specific purpose.

Data extraction has largely been automated and the whole process is now dependent on various tools and applications that are supported by artificial intelligence and machine learning. The artificial intelligence tools are even extracting data from images and reactions. Although, the semantic machine algorithms are sufficiently smart to deduce information from any used language, this enormous amount of information contains a lot of duplicate, inaccurate, and conflicting data. Accurate data is critical to avoid erroneous decisions and for overall performance enhancement, for it ensures correct and actionable insight for future events. It is safe to conclude that clean and organized data is a first step towards general profitability and successful business trends.

So what is data cleaning?

Data cleaning – also often referred to as data cleansing - is the process of identifying discrepancies such as incorrect, inaccurate or irrelevant information in a dataset and then correcting, removing, substituting, or modifying the abrasive data before adding it to the relevant database.

The detected irregularities could be caused by several reasons e.g., entry errors, transmission malfunction, or an incompatible data dictionary. The process of data cleaning is similar to that of data validation with an exception of data rejection on admission from the system. Data cleaning encompasses data enhancement as well as data harmonization. In the former case, the unfinished detected data is made complete by relevant information accumulation, while in latter situation, the incoherent data of different formats and conventions is brought together to form a singular cohesive dataset.

Data cleaning can either be performed interactively through various data cleansing tools or as batch processing by using specific scripts. After this essential and most time-consuming stage, the previously raw, unstructured data transforms into uniformity with the other datasets involved.

Data Cleaning Techniques

Data cleaning techniques are used to shape and trim the unwanted components from a data set. The common objective of these techniques is to transform cleaned data into a problem targeted structure in order to draw constructive data analysis. Following are some of the frequently used techniques that are used to clean data and make it usable for any required purposes:

  • Removing irrelevant data
  • Reformatting
  • Removing Duplications
  • Typo and structural errors
  • Removing incomplete data
  • Removing unwanted spaces
  • Removing irrelevant data

Data Cleaning Tools that Make Essential Toolbox

There are more data cleaning tools available today than there are actual users out there. It goes without saying that not all of them are authentic or compatible to the required tasks. As the projects’ sophistication is increasing with time, the demand for optimized assisting applications is growing too. In the fast-paced world of data science, nobody can afford to spend hours after hours on a task that is tedious on good days as it is.

The manual cleanse of massive amount of data is not only an inefficient and daunting task, it is practically asking for human errors. The automated data cleaning procedures in analytics driven establishments need cleaning tools that methodically scan data for weaknesses through systematic rules & algorithms.

Programmers and developers around the world are actively working on tools that can help data scientists perform data cleaning in less time but with more accuracy rate to be able to certify maximum quality of a data. Following are some of most effective data cleaning tools that should be in the toolbox of aspiring as well as established data scientist:

1. Data Ladder

Data Match is a tool provided by the company Data Ladder. It is an ideal tool for data cleaning and refining data quality. Its enterprise version DataMatch Enterprise offer more advanced algorithms designed for around one hundred million data sets with highest accuracies so far. It is affordable, easy to learn and its user friendly interface makes it compatible with every business model.

2. OpenRefine

OpenRefine was first launched as Google Refine. This data cleaning tool is very useful for cleaning and systematizing highly disorganized data. It offers formatting transformations, data matching, reconciling, and cleaning of data at high speeds. OpenRefine is an open source tool and offers free accessibility for everyone.

3. Trifacta Wrangler

This tool is yet another free smart data cleaning and alteration tool. It takes much less time in data formatting and focuses on the importance of data analyses. It is a data wrangler tool which is used to deal with complex and unorganized data. It accurately categorizes data by removing its clutters at a very fast speed and also suggests frequently used transformation to save time.

4. Winpure

Winpure is one of the best data cleaning tools. Its popularity in part is due to its affordability and ease of accessibility. It can easily handle bulky data for cleaning, removing duplications, replacing inaccurate data with the correct and standardized data. It can be applied on data sets, CRMs, and excel sheets too. Winpure can also be integrated with databases like SQL server and Access. Its advanced techniques include data matching from messy data sets along with multilingual adaptability.

5. Cloudingo

Designed for Salesforce, this data cleaning tool is primarily used to remove duplicates in databases and data updating. It can also handle large amount of data and cleans files before importing them. Its advanced techniques allow automation of regular data scanning to sanitize it from errors. It is easy to use and offers flexible and affordable tools suitable for all business size.

6. Drake

Drake is considered to be a very simple tool used for data cleaning. It is text- based and works on the method of data workflow by defining data processing methods along the input and output commands which can be multiple. This tool uses automation for resolving dependent variables and executes commands according to timestamps. It is designed to suit data workflow management techniques.

7. DataCleaner

The best feature of DataCleaner is that it has a strong built engine for data profiling which is used to find and perform analysis of data. It explores various features of data records, discovers missing values, trends and patterns. It is extensively used among the data scientist community.

8. TIBCO Clarity

This tool used for data cleaning has two versions. One is a cloud version and the other one is an enterprise version. Enterprise version can be installed on a computer system and multiple users can access it through a unique link only provided to them. It supports data uploading in various file formats like CSV, XML, TXT and XLS. It allows data mapping from multiple sources and performs data cleaning by applying transformation rules before importing data into a single data sheet. This tool also allows you to apply sampling on a piece of data extracted from a source. It removes duplicate data and displays it in a standardized form for precise analysis.

9. IBM Infosphere QualityStage

This tool is also considered to be among the popular ones used for data cleaning, and it supports enhanced data quality. It provides data cleaning and database management with ease and delivers quality data for business analysis and intelligence. It provides steady views of highlighted business entities such as vendors, products, locations and customers. Its best features are more than two hundred built in rules for data quality, deep data profiling, and more than two hundred and fifty data classes for classification of data, record match and standardization of data.

10. Astera Centerprise

Astera Centerprise offers integration of data and data quality refining techniques on a single platform. It performs transformation while maintaining data accuracy and dependability. Integrity of business data is ensured with the help of highly sophisticated data profiling which makes the client capable of scrubbing significant data fast and accurately. It helps to identify errors in the data by examining the source of in order to ensure data integrity. Other key features are data profiling, automation of workflow, duplicate and incorrect data removal.

Why is data cleaning required?

Data cleaning or data cleansing is considered to be an essential step before proceeding towards data analysis and visualizations. 2.5 quintillion bytes of data is being generated every day, extracted from various platforms and sources for research and analysis to be able to bring out the required information which is vital in the decision makings of almost all organizations and institutions.

There must be adequate reasons for data scientists to spend most of their time in cleaning and refining data while dedicating only quarter of it to data analysis and pattern discoveries. It is because an inaccurate data leads to miscalculations which effect the overall outcome and predictions of a research. This miscalculation can be costly and dangerous dependent on the nature of the project. Without data cleaning and following ramifications of erroneous results, the process of data science becomes moot in its entirety.

Another key purpose of data cleaning is to regulate the irrelevancies from the records and databases. How ca a data set that was obtained through powerful machine learning algorithms become irrelevant? Well, the answer lies in requirement. Data changes its relevancy as per the requirements, which means data sets can have dissimilar requirements for finding a solution to a specific problem. For instance, data gathered about choices and preferences of young girls might not be relevant for a group of young boys. However, this data might be equally important when one is representing conclusions for a group of young people, girls and boys inclusive.


Almost every business nowadays produces enormous data with each and every business activity performed. The increased dependency of doing business on information systems and rapid advancements in technology has made it mandatory to have meticulous data cleaning tools. The above set of tools are put together after a considerable research to narrow down the best and make your toolbox diverse and efficient.

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Millennial Moderator Author

Sitwat Maroof

Sitwat Maroof is a technical writer.