If you’re starting as a data analyst, one of the first things you’ll learn is the importance of effective data cleaning. Luckily, there are many industry tools available to streamline the process, which can be especially helpful for beginners. Better yet, a lot of these have been designed specifically for areas like customer data, which plays a huge part in 21st-century business.
In this post, we highlight some popular data cleaning tools that data analysts use every day. But before we get there, let’s set some context by briefly recapping what data cleaning involves. Want to skip straight to the tools? Just use the clickable menu.
- What is data cleaning?
- What are some of the most popular data cleaning tools?
- Building your data cleaning toolkit
- Further reading
1. What is data cleaning and how is it done?
The main tasks you’ll have to carry out when cleaning data include:
- Getting rid of unwanted observations: Removing observations that aren’t relevant to the problem you’re trying to solve.
- Unifying the data structure: You’ll need to ensure data from different sources is consistent by mapping it to a unified underlying structure.
- Standardizing your data: This involves things like ensuring the numerical observations in your dataset use the same unit of measurement.
- Removing unwanted outliers: Outliers can be useful, but if they’re erroneous they’ll skew the results of your analysis. You’ll need to make a judgment call about which outliers to keep and which to remove.
- Fixing cross-set data errors: Data rarely comes from a single source; ensuring that different data sources don’t contradict each other is vital.
- Resolving type conversion and syntax errors: This involves things like removing whitespace, checking for spelling mistakes, or simply ensuring data is categorized correctly. For instance, are number fields properly labeled as numerical data?
- Dealing with missing data: If there are gaps in your data, what effect will this have? You might choose to remove associated entries, guess missing values, or simply flag them so you can measure their impact later on.
- Validating your data: This is the final step of the process. It usually involves executing scripts that check if you’ve carried out all the other steps of the process correctly. You’ll often have to go back and repeat some of the earlier steps.
While tools such as MS Excel, Python, and other scripting languages are all invaluable for data cleaning, there’s an ever-increasing number of vendor data tools available. Now we’ve recapped what data cleaning involves, let’s take at some of these data cleaning tools.
While many of these focus on things like customer data, they can largely be used to clean any kind of big data. For a complete introduction to data cleaning (and why it matters), take a look at this guide. For now, though, check out our top data cleaning tools.
Check out this online workshop we held showing participants how to identify missing values as part of the data cleaning process:
2. The top 7 data cleaning tools
For anyone working with data, the right data cleaning tool is an essential part of your toolkit. Here’s our round-up of the best data cleaning tools on the market right now.
Known previously as Google Refine, OpenRefine is a well-known open-source data tool. Its main benefit over other tools on our list is that, being open source, it is free to use and customize. OpenRefine lets you transform data between different formats and ensure that data is cleanly structured. You can also use it to parse data from online sources.
While it is cosmetically similar to spreadsheet software (like Excel), it acts more like a relational database. This makes it very handy for data analysts who need to dive a little deeper than a simple Excel file offers. Another key benefit is that you can work with data on your machine, i.e. it is secure. Of course, if you want to link or extend your dataset, you can do so by connecting OpenRefine to external web services and other sources in the cloud.
If necessary, you can also upload your data to a central database like Wikidata. One word of caution though: while OpenRefine streamlines many complex tasks (e.g. using clustering algorithms) it does require a little bit of technical know-how.
A connected desktop application, Trifacta Wrangler lets you transform data, carry out analyses, and produce visualizations. Its standout feature is its use of smart tech. Utilizing machine learning to spot inconsistencies and make recommendations, the tool vastly speeds up the data cleaning process. For instance, its artificial intelligence algorithms can easily identify and remove outliers, as well as automating overall data quality monitoring—a helpful feature for ongoing data housekeeping.
Furthermore, rather than having to produce data pipelines from scratch (a potentially time-consuming task as anyone in the field will tell you), the tool’s UI allows you to do this in a much more visual and intuitive way. One of a suite of products, various additional features are available as you extend the software.
For example, Wrangler Pro supports larger datasets and cloud storage, while the enterprise version offers collaboration tools for working in teams. The latter also has centralized security management—another important feature if you’re working with sensitive data (and let’s face it, what data isn’t sensitive?)
A bit like Trifacta Wrangler, the award-winning Winpure Clean & Match allows you to clean, de-dupe, and cross-match data, all via its intuitive user interface. Being locally installed, you don’t have to worry about data security unless you’re uploading your dataset to the cloud.
This is an especially important feature for Winpure, which is specifically designed for cleaning business and customer data (such as CRM data and mailing lists). Winpure Clean & Match also interoperates with a very wide variety of databases and spreadsheets, from CSV files to SQL Server, Salesforce, and Oracle.
Other useful features include fuzzy matching (which involves spotting where matches differ based on arbitrary abbreviations or typos) and rule-based cleaning that you can program yourself. It’s available in four different languages, too: German, English, Portuguese, and Spanish. The free version offers a good number of features, making it an ideal option for small businesses. Maybe one to recommend to your boss!
Cloud-based software as a service (SaaS), TIBCO Clarity, is ideal for cleaning raw data and analyzing it all in one location. It’s a feature-rich data cleaning tool that ingests data from dozens of different sources, including from XLS and JSON files to compressed file formats, as well as a wide range of online repositories and data warehouses.
Beyond this, TIBCO offers everything from data mapping functionality, to extract, transform, load (ETL), data profiling, sampling and batch functionality, de-duping, and much more. It also boasts some helpful nice-to-have features, such as ‘transformation undo.’ This is not available with all tools but it’s a great feature if you’re not happy with a change you’ve made.
The only drawback of all this functionality is that there’s no free version, but TIBCO Clarity is still a solid piece of software, and you can trial it before recommending it to your organization.
Melissa Clean Suite is a highly targeted data cleaning and management tool. It’s designed specifically to support the Salesforce and Microsoft Dynamics customer relationship management (CRM) systems, which many businesses use. Because it’s focused on these two systems, it caters to their unique features.
For instance, it supports all standard Salesforce objects and integrates with standard forms in Dynamics. It doesn’t require any complex training, either (which is a bonus!) and it comes with several in-built marketing features. These include demographic creation, data targeting, and segmentation. Melissa Clean Suite’s main benefit is that it cleans data as it is being collected. This minimizes effort later on.
For instance, it autocompletes, corrects, and verifies contacts before entering them into the system. Once data is in, the tool proactively maintains data quality with real-time cleaning and batch processing. Although targeted at marketing-related data activities, Melissa has clear time-saving benefits from a general data management perspective, too.
IBM Infosphere Quality Stage is one of a broader selection of data management tools from IBM. It focuses—as the name suggests—on data quality and governance. While it deals with the usual suspects (data matching, de-duping, etc.) it is specifically designed to clean big data for business intelligence purposes. For this purpose, it has about 200 in-built data quality rules, saving analysts tonnes of time managing these tasks manually with scripts.
What’s more, its key features all support otherwise labor-intensive tasks such as data warehousing, master data management, and migration. Deployed either in-house or in the cloud, the tool also offers a deep level of data profiling. You can use it to explore the content, quality, and structure of data from a broad database view, or drill down to granular details, analyzing individual columns, for instance.
While it might not be the best tool for those without some technical know-how, it does offer a useful data quality scores feature. This allows any user (regardless of technical ability) to get a general sense of a dataset’s integrity. This is a very useful feature for executive-level stakeholders.
Datamatch Enterprise by Data Ladder is a visually-driven data cleaning application. Like many of the other tools on our list, it focuses on customer data. However, unlike others, it is designed specifically to resolve data quality issues within datasets that are already in a poor condition. Instinctive and simple to use, it employs a walkthrough interface to support you through the data process from start to finish.
Using a wide range of import and export functionality, you can create anything from database tables that align with complex internal business procedures, to Excel spreadsheets or simple reports. It is also scalable, allowing users to deduplicate, extract, standardize and data match on datasets large and small.
Helpfully, you can manually configure match definitions to respond to various confidence levels when it comes to accuracy, depending on what your intended outcome is. And it has a handy scheduling function, meaning you can pre-set data cleaning tasks well in advance. After all, data cleaning is not just a one-off job…it’s a process!
3. Building your data cleaning toolkit
In this post, we’ve explored some of the data cleaning tools that analysts encounter in their day-to-day work. To continue building your data cleaning toolkit, we encourage you to explore some of these and other tools. You’ll find there are a great many options out there, catering to different tasks and use-cases.
However, ultimately, while fancy software can help, the best data cleaning tool you have at your disposal is your intuition and expertise. Although much of the process can be automated, you’ll find that more complex tasks require a greater level of manual manipulation. Remember that tools are exactly that: tools. Aim to perfect your manual data cleaning skills too, using tools like Excel, Python, R, and relational databases. That’s one of the joys of data analytics—you never stop learning!
You may be interested in this introductory tutorial to data cleaning, hosted by Dr. Humera Noor Minhas.
4. Further reading
Learn more about data analytics with this free, 5-day data analytics short course, and check out the following posts for more insights: