Best Tools For Data Cleaning
Top Tools for Data Cleaning, Visualization and Analysis
How many of you are aware with data cleaning? Data cleaning is also known as data scrubbing or data cleansing.
What is the Main Purpose of Data Cleaning?
It means, the process of finding out and rectifying or getting rid of either inaccurate or tainted data from a database.
Today we are going to talk about the tools that can be used for cleaning data. In case you are wondering why a person would need data cleaning tools. Often, we leave out a lot of stuff unchecked in large sets of data. Finding and cleaning this data on our own can be hard specially if the data is large. This is when data cleaning tools come in handy.
[Related: what are Access 2013 web apps? ]
Best Tools for Data Cleaning
Let us take a look at the data cleaning tools we have. All of the listed tools are really effective in doing their job without any issues. They clean up your data and make it look the way it should be looking. So without wasting any time, lets take a look.
DataWrangler is a web-based service that can be used for cleaning and rearranging messy data with ease. DataWrangler is designed by the talented visualization group of Stanford University.
DataWrangler works well with different apps, for example; if you are willing to clean a spreadsheet file, DataWrangler can do that for you with ease.
To get started, simply click on a column or a row and you will be suggested some changes by DataWrangler. The suggestions mostly include; ‘delete columns’, ‘delete empty columns’ and other similar features. DataWrangler also comes with a history that shows all the changes you have made and also gives you the ability to undo the changes you have made.
One of the best features about DataWrangler is the ability to suggest. For example; type in a name of some city in some row and move over your mouse to the name, the service will start suggesting you stuff according to the name you have entered. This works for almost every word you enter.
Despite being packed with features, there are some minor drawbacks. First of all, there are some minor inconsistencies while browsing the options. Also, the suggestions aren’t really as precise as they should be.
Security Issue: Last but not the least, it is a really amazing feature that DataWrangler is entirely based on the internet, but this might also raise you concern. How? Well, DataWrangler sends all your data to an external website. This means if you are cleaning sensitive data, DataWrangler isn’t your best choice.
[ Related: What is research? ]
OpenRefine was formally known as Google Refine before it was handed over to the open source community. Even in the early days, OpenRefine was considered as one of the most powerful data cleaning tools in the market. A lot of people even went to the limits of calling it the ‘beefed up spreadsheet’.
1. Data Support: One of the most amazing features about OpenRefine is its ability to clean both numerical and alphabetical data.
2. Supported Formats: Furthermore, OpenRefine is also capable of importing data in several different formats. The formats supported are; comma, text, numerical, XML, JSON and a handful of others.
3. Inbuilt Algorithms: Another thing to love about OpenRefine is the fact that it facilitates the users with a plethora of inbuilt algorithms. The purpose of these algorithms is to find all the text items that have different spellings but should be in one group. Once you have imported your data in OpenRefine, all you need to do is click on Edit Cells > Cluster and Edit and then choose the algorithm you want to use. Once OpenRefine runs, it is all up to you to decide which suggestion you want to accept in which suggestion you want to ignore.
For example; if there’s ‘Nvidia’ and ‘Nvidia Physx’ in your data, you can choose to group them together, you can even choose not to group the suggestion that are not similar in any way. In case you are wondering what if it starts giving out too many suggestions. Well, there is a cure for that as well, there’s an option to lower the strength of the suggestions you will receive.
4. Cleaning Numerical Data: Apart from sorting out text data, OpenRefine also works well when it comes to cleaning numerical data. For example, if you are creating a spreadsheet that has salaries in it or something else and you add a number by mistake. OpenRefine will find that mistake easily and remove any inconsistencies.
5. Analyzing, Sorting and Filtering Data: Apart from being a terrific data cleaning tool, OpenRefine also features some other hand features such as analyzing, sorting and filtering data.
6. Data Refinement, Manipulation and Analysis: OpenRefine is a powerful tool that requires learning, but once you are familiar with all the commands. All this raw, unprecedented power is yours to command. OpenRefine is a beast of a tool for dealing with both data refinement, manipulation and analysis.
7. Flexibility: It is the perfect balance of a powerful tool that is extremely easy to use. OpenRefine also features the ability to redo or undo the changes you have made.
8. Data Security: Last but not the least, despite being a website that works on the internet, it has the ability to work with your files on the computer. This means that your data remains local.
Despite being a powerful data cleaning tool and looking a lot like a spreadsheet, one of the major drawbacks of OpenRefine is the limitation it puts on the users who are willing to do some spreadsheet calculation so you will always have to keep a separate app that will take care of all that.
Another issue I’ve encountered with this app was how it handled large sets of data, OpenRefine will present you with a large of suggestions regarding the changes. This takes a lot of time, specially when you have to look out for each suggestion and choose the words that should be grouped together.
If you can overlook these issues, there’s nothing wrong with OpenRefine that will ruin your experience while cleaning your data.
[ Related: 4 Major data modelling techniques with examples ]
While this is the most basic feature you can get and is available in almost all the editors. The feature is known as ‘find and replace’. Yes, it isn’t as effective on the previously mentioned tools and isn’t certainly ideal for large sets of data, but if you are working on a small set of data. Find and replace feature can really come in handy. Rest is dependent on you.