Best Tools & Techniques for an Effective Data Cleaning Process

Effective data cleaning plays a crucial role in business decisions regardless of your industry. Good and bad data are the major aspects which have to be considered. Good data can enhance, whereas bad data can harm your business activities.

If you opt for a career as an analyst, you will have to understand how important data cleaning is for businesses. To streamline this process of cleansing your data, different industry tools are available.

This article will help you take a deep look into some of the popular data cleaning tools helpful for information analysts.

This term is also referred to as data scrubbing or data cleansing. It is the process of identifying and correcting issues, if any.

This technique is also used to remove the unfixable facts and clean them appropriately. Issues usually occur due to human error or combining facts from diverse resources.

Garbage in and garbage out is the general principle on which this cleansing technique is based.

This is an era of multi-channel data, and inconsistencies are always expected. Before analyzing your information, clean all the bad information available. It might lead to incorrect insights and devastating business decisions.

Here are the best cleaning tools to get the most out of this process

1. Reifier

   

   • Run time performance, higher accuracy, faster deployment
   • For data deduplication, distributed entity resolution and record linkage spark are utilized by Reifier.
   • Machine learning algorithms are used to provide the best entity resolution.

2. OpenRefine- open-source powerful tool  

   

   Most popular open-source data tool, previously known as Google Refine

   • Easily customizable and free to use
   • Using this tool, you can work on your machine
   • Similar to excel and acts as a relational database
   • Streamlines many complicated tasks easily

3. Cloudingo

   

   • It’s a salesforce cloud-based cleaning tool
   • This tool is helpful in cleaning and preparing, maintaining quality and matching data.
   • Suitable for businesses of all the sizes
   • The automation capabilities ensure regular scanning for errors.

4. Trifacta Wrangler

   

   • Helps in transforming information, carrying out analyses and producing amazing visualizations.
   • Machine language is used to make recommendations and spot the inconsistencies
   • The best tool to speed up your cleaning process
   • Producing process pipelines in a much more intuitive and visual way is possible

5. Winpure Clean & Match

   

   • Helps in cleaning, cross-matching and deduping
   • Particularly designed for cleaning of customer and business facts
   • Capable of interoperating with a wide variety of spreadsheets and databases
   • Helpful features include rule-based cleaning and fuzzy matching 

6. TIBCO Clarity

   

   • Ideal tool for cleaning and analyzing raw facts in one location
   • A feature-rich cleaning tool capable of ingesting information from numerous resources
   • Offers mapping functionality, fact proofing, de-duping and much more.

7. Timesaving Melissa Clean Suite

   

   • Timesaving tool designed specifically for management and cleaning
   • Supports Salesforce and several CRM systems used by different businesses
   • Does not requires any complicated training process
   • The major feature of this tool is that it cleans facts as its collected
   • Capable of verifying, correcting and auto-completing contacts
   • This tool also helps in proactively maintaining quality with real-time cleaning

8. IBM Infosphere Quality Stage tool

   

   • Focuses more on facts, governance and quality
   • Designed specifically to clean big facts for business intelligence
   • Has about 200 in-built information quality rules and is time-saving
   • Supports warehousing of information, migration and information management
   • This tool is capable of offering a deep level of information profiling
   • Exploring quality, content, and structure gets easier with this tool
   • Helps in getting a general sense of integrity of the dataset
   • Very useful for the stakeholders at the executive level.

9. Data Ladder

   

   • A visually driven application
   • A major focus is on customer information
   • Specially designed to solve issues within datasets
   • This tool is intuitive and simple to use
   • Has a walkthrough interface that supports the entire process
   • Creating anything from the database tables can be easily aligned with the complicated procedures
   • A scalable tool which allows users to extract and standardize information to match

Different cleaning methods and techniques are required for each set. Here we will go through some of the common issues which are likely to arise when carrying out the process. Your objectives play a major role in starting with the process. What do you want to gain from this? These things will help you set some standards and rules before inputting your information.

Let’s go through some of the effective techniques as follows

1. Removing duplicates

   • Your dataset will have duplicate entries when it is collected from different sources.
   • The reason might be human error when the information is input by a person
   • Duplicates will confuse information and make them hard to visualize and read.
   • Therefore the best method is to remove them in the right way.

2. Unwanted data removal

   • Irrelevant facts will slow down and confuse the analysis you want to carry
   • Before you start the process, decipher what is relevant and what is unnecessary

3. Data enrichment with capitalization

   • You need to make sure that the text within your information is consistent
   • A mixture of capitalization can create problems for different categories
   • Sometimes capitalization can change the whole meaning of your information.

4. Converting types of information

   • Numbers need to be converted while carrying out the process of cleaning.
   • Numbers sometimes are input as text, but to get processed, they need to appear as numerals
   • However, algorithms cannot perform mathematical equations if they are classed as a string.

5. Clear Formatting data process

   • Facts taken from different sources are usually in different document formats.
   • This makes your information incorrect and confusing.
   • Removing that formatting is necessary as it is applied to your documents.

6. Error fixing

   • Typo errors can be misleading and need to be carefully removed.
   • Quick spell check can help in avoiding such mistakes usually.
   • Punctuation marks play a vital role in email addresses, and mistakes can lead to sending unwanted emails
   • Other errors might include formatting inconsistencies.

7. Translation

   • Information consistency also depends upon the language you are using
   • Make sure you use the same language in the dataset
   • If everything is not in the same language, you need to translate everything into one language.

ProGlobalBusinessSolutions is one of the top data cleansing services providers. We are well-known for the effective services provided by our experts to enhance and refine the defective database. This process includes using the latest tools and technologies, ensuring highly optimized results. Outsourcing your requirement to our team will enable accurate information cleansing with a high level of proficiency.

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