- Business Analytics - Home
- Business Analytics Basics
- Business Analytics - What It Is?
- Business Analytics - History and Evolution
- Business Analytics - Key Concepts and Terminologies
- Business Analytics - Types of Data
- Business Analytics - Data Collection Methods
- Different Tools used for Data Cleaning
- Business Analytics - Data Cleaning Process
- Different Sources of Data for Data Analysis
- Business Analytics - Data Cleaning
- Business Analytics - Data Quality
- Descriptive Analytics
- Descriptive Analytics - Introduction
- How Does Descriptive Analytics Work?
- Descriptive Analytics - Challenges and Future in Data Analysis
- Descriptive Analytics Process
- Descriptive Analytics - Advantages and Disadvantages
- Descriptive Analytics - Applications
- Descriptive Analytics - Tools
- Descriptive Analytics - Data Visualization
- Descriptive Analytics - Importance of Data Visualization
- Descriptive Analytics - Data Visualization Techniques
- Descriptive Analytics - Data Visualization Tools
- Predictive Analytics
- Predictive Analytics - Introduction
- Statistical Methods & Machine Learning Techniques
- Prescriptive Analytics
- Prescriptive Analytics - Introduction
- Prescriptive Analytics - Optimization Techniques
Business Analytics - Different Tools Used for Data Cleaning
There are multiple data cleaning tools available; each one has a set of unique features and capabilities. These tools include programming languages and libraries, as well as specialized software platforms for handling massive datasets and complicated data-cleansing tasks.
Several tools are available for data cleaning, each with unique features. Some of the key data-cleaning tools are as follows −
1. Excel
Excel's user-friendly interface and extensive feature set make it a popular tool for data cleansing and processing. It offers a variety of choices such as data formatting and standardization, data type conversion, data validation, text manipulation, duplication removal, and so on.
2. OpenRefine
OpenRefine is open-source data transformation and cleaning software that was previously known as Google Refine. Its objective is to pre-process and clean up dirty data. It includes a wide range of capabilities to clean, normalize, and transform datasets, along with an intuitive user interface. Its key features are Clustering, transformations, undo/redo, and support for large datasets. This tool is most widely used for Data exploration, cleaning, and transformation.
3. Trifacta
Trifacta is a commercial software solution. Trifacta is a top choice for enterprise-grade data cleansing software. The primary purpose of this low-code/no-code platform is to provide users with access to cloud infrastructure for their big data analytics needs. Trifacta encourages collaboration by allowing users to share cleaning data pipelines and work on the same dataset.
Overall, Trifacta is a cloud-based data preparation tool that uses machine learning to suggest data transformations. It includes interactive features like interactive data profiling, predictive transformation, and integration with various data platforms. This tool is most widely used for Large-scale data preparation in a collaborative environment.
4. Talend
It is an open-source data integration tool that also offers data cleaning and transformation capabilities. It has interactive Drag-and-drop features, data profiling, and support for big data and cloud environments. This tool is most widely used for integrating and cleaning data from various sources.
5. Python
Python and data analytics are close to one another because Data cleaning in business analytics is most commonly performed using Python. Python includes a plethora of tools and modules that address many aspects of data cleaning, transformation, and analysis, providing a wide range of capabilities for properly cleaning and pre-processing data. Python is rich in libraries like Pandas, NumPy, Seaborn, Matplotlib, Dask, Tabulate, Regex, and other libraries are popular tools for data cleansing. Data cleansing tasks can be automated using Python for the users application.
6. SQL
The database programming language is known as Structured Query Language (SQL). SQL queries can be used to extract filtered information from databases. Most applications' data is stored in a Database Management System (DBMS). As a result, it is an effective tool for source-level data management. Although it can do simple data cleaning activities, it fails when confronted with complex data.
7. Tableau
Tableau is a popular data visualization application which allows users to create interactive dashboards for a variety of purposes. You may change the charts, graphs, local and global filters, formulas, and more. We can perform simple data-cleaning techniques before creating the visualizations.
8. DataCleaner
It is an open-source data profiling and data quality analysis tool which includes interactive features like Data profiling, validation, and duplication. It is most widely used for small to medium-sized datasets.
9. TIBCO Clarity
TIBCO Clarity is a cloud-based tool for data cleaning, standardization, and validation. It includes interactive features like automated data cleaning, collaborative tools, and integration with TIBCOs suite of products. It is most widely used for business users needing an easy-to-use tool for data cleaning.
10. IBM InfoSphere QualityStage
It is a data quality tool developed by IBM; it supports data profiling, standardization, and matching. It includes interactive features like advanced data quality rules, integration with IBMs data management suite, and support for large enterprises. It is most widely used for large organizations with complex data quality needs.