Best Data Cleaning Tools
Buyer's Guide
Data cleaning tools eliminate dataset inconsistencies to boost data quality and provide consistent information for effective decision-making. They prepare data for use in various business intelligence tools and data science applications.
Executive Summary
- Data cleaning solutions help users make better decisions, boost business performance and increase productivity.
- Data profiling, mapping and workflow automation are some key data cleaning features.
- Power BI, Tableau and Board are popular data cleaning solutions.
What Is Data Cleaning Software?
Data cleaning tools help you remove the duplicate, incorrect, incomplete or corrupted data in a dataset. When combining data from multiple sources, the information is probably duplicated or mislabeled.
Data cleaning solutions identify errors or inconsistencies and employ techniques to update, remove or replace them to boost data quality.
Techniques
Remove Duplicates
Eliminate duplicate or irrelevant observations from datasets. When you combine data from multiple sources, you may find duplicate information.
Irrelevant observations don’t fit the problem you are trying to analyze. For instance, if you are trying to analyze data about working mothers, but your dataset contains information about older men, you may remove this irrelevant information to make your model robust and efficient.
Fix Structural Errors
Standardize naming conventions, remove typos and eliminate incorrect capitalization. These inconsistencies can confuse you in terms of mislabeled categorization. For example, ‘percent’ or ‘%’ means the same and ideally should fall into the same category.
Filter Outliers
You will come across outliers often, that is, data that doesn’t appear to fit within the context of your analysis. In such cases, you can remove improper entries or values exceeding the usual range to boost model accuracy.
Handle Missing Information
Some ways to deal with missing data are as follows:
- Drop observations with missing values. Be careful not to lose other vital information.
- Impute missing data with suitable values based on other observations in the dataset.
Ensure Data Quality
Answer the following questions to ensure data quality:
- Does the data make sense?
- Does it follow specific rules for all fields?
- Can you find any trends?
Primary Benefits
Let’s look at some data cleaning tool benefits:
- Make Better Decisions: BI applications can produce accurate results with accurate and reliable data. It lets organizations make informed decisions about strategies, operations and processes.
- Boost Operational Performance: High-quality data helps mitigate inventory shortages, delivery delays and other problems that can harm performance, increase costs and result in strained customer relationships.
- Reduce Costs: Prevent data errors from propagating and causing expensive faulty analysis.
- Increase Efficiency: Use error-free data to increase process efficiency and performance while gaining valuable insights.
Key Features & Functionality
| Data Profiling | Create data profiles to find patterns, missing values, outliers and other important characteristics. Automate metadata identification and offer clear visibility into source data to detect errors. |
|---|---|
| Advanced Data Quality Checks | Integrate data validation rules into the information flow to monitor and report errors that occur while processing information. Ensures quality and integrity. |
| Data Mapping | Map data from sources to destinations. Standardizes data and mitigates potential errors. |
| Workflow Automation | Automate the cleaning process from start to finish, including conversion, validation and data loading. |
Software Comparison Strategy
When selecting data cleaning tools, you should consider the following factors:
Data Access
You should have access to proprietary data residing in different systems, including but not limited to CRM, ERP, project management solutions or data warehouses. Ensure your vendor offers a compatible interface to access and clean data across different software solutions.
Cloud-Based or On-Premise
Do you want to go for a cloud-based or on-premise solution? Make sure you weigh the pros and cons of both before you decide to invest.
On-premise solutions require physical hardware and infrastructure, storage space and internal resources. Whereas cloud-based solutions are suitable for small and mid-size organizations, requiring fewer resources and less up-front costs.
Cost & Pricing Considerations
When evaluating data cleaning solutions, it's important to consider budget constraints. The solutions’ pricing depends on the deployment strategy, number and types of user licenses, modules, add-ons and customizations.
The Most Popular Data Cleaning Software
Let’s look at the top data management tools:
Tableau
Tableau Prep Builder helps you combine, clean, shape, transform and share data through an intuitive visual interface. Join different data sources and pivot rows and columns to get specific results. Change the data types, filter and aggregate data and replace multiple values at once.
Shape and combine data through Tableau Prep Builder.
Power BI
Shape your data with Power BI Query Editor. Accomplish actions such as renaming columns, changing data types, eliminating irrelevant rows or columns, setting the first row as headers and more. You can replace values and remove duplicates to clean and transform data.
Clean, transform and prepare data using Query Editor.
Board
Source and combine data from different sources, including relational databases, big data sources, cloud applications and more, through integrated data preparation functions. Leverage intuitive drag-and-drop capabilities to clean and prepare raw data. Generate meaningful insights via data discovery tools.
Use Board’s drag-and-drop interface to clean data.
Questions to Ask
Use these questions as a starting point for internal conversations:
- What are your goals and objectives?
- Who will use the system?
- What’s the budget?
- Do you require integration capabilities?
- Which basic and advanced features do you need?
Use these questions as a starting point for conversations with the vendor:
About the Software
- What features does the solution provide?
- Does it offer a graphical user interface for point-and-click development?
- What is the yearly subscription fee?
- Is the interface intuitive enough for novice users?
- How well can the solution integrate with other systems?
About the Vendor
- What support do you offer post-implementation?
- How long does it take to deploy the solution?
- How often do you release updates?
- What documentation and resources are available for learning?
- How will you assist with adoption?
- Do you provide customization?
In Conclusion
It’s important to strive for a quality data culture in the organization by providing a robust visual interface to clean and transform data. Quality data ensures efficient decisions while boosting business performance.
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