7 Ways Data Cleaning Work Best
Raw data is of no use until it is cleaned and turned into information, quality of data is very important. if you working with large scale data to measure and optimize your programs then you should consider adding set of rules for data cleaning as basic rules.
So What is Data Cleaning ?
In basic terms data cleaning is a process to ensure that data is correct, usable and consistent for your work. it is process of identifying errors or data corruption also correcting and deleting unusable data. sometimes a manual processing is needed to stop problem from occurring again and again.
Data cleaning is not as simple as erasing information to make room for new set of data but it is a way to maximize accuracy of the dataset and getting usable information. Data cleaning is a fundamental element in data sciences analysis basic process for uncovering reliable information. The best use case for data cleaning is to create data sets those are standardized to allow business intelligence and data analytics tools to easily access and find the right data for each query.
Following are 7 ways data cleaning will work for your big data.
before starting data cleaning steps you must set your goals, like what is your expectation from this dataset. to achieve these goals you must form your data cleaning strategy. A good way to start is get your team do a brainstorming session.
1. Remove Extra Spaces
Here I have the text Welcome To My Digital Database written in four different ways.
**Welcome To My Digital Database** **Welcome To My Digital Database** **Welcome To My Digital Database** **Welcome To My Digital Database**
First one is the regular way with only one space between words, in the second case I have more than one space between words, in a third case I have some leading spaces along with a couple of spaces between words and in the fourth case I have trailing spaces, you can see there are a couple of space after the last word.
Now, this could typically be the case if you get this data from a colleague or you get it from a text file or imported from a database. So to clean this data and get rid of these extra spaces you can use the function trim.
Trim function takes one single argument which could either be the text which you type manually or it could be the cell reference, in this case, I will take the cell reference A1 and what this function does is it would remove all the leading spaces and trailing spaces and extra spaces between words except one single space that is allowed.
So if I drag this down you would see that it has corrected all these texts. It has removed the extra space here between welcome into it has removed the leading spaces and trailing spaces.
2. Monitor errors
Keep a record of trends where most of your errors are coming from. This will make it a lot easier to identify and fix incorrect or corrupt data. Records are especially important if you are integrating other solutions with your fleet management software, so that your errors don’t clog up the work of other departments.
3. Standardize your process
Standardize the point of entry to help reduce the risk of duplication. Use a Management system to not down what processes you have performed and what left to test.
4. Remove Duplicates
Duplicate data is of no use. Only use unique data for data analytics. Identify duplicates to help save time when analyzing data. Repeated data can be avoided by researching and investing in different data cleaning tools that can analyze raw data in bulk and automate the process for you
5. Spell Check
If you have huge data set and you want to only extract a part of it in the form of cleaned data while Microsoft PowerPoint and Microsoft Word have a feature where it would underline if there are any errors grammatical errors or spelling errors Microsoft Excel does not have that feature however you can still a run spellcheck and correct these errors. Microsoft Word or PowerPoint it will show you the text that it thinks is a spelling error and it will show you the suggestions as well so you can change these and once it is done it will show you that spellcheck is complete and you are good to go.
6. Validate data Accuracy
Once you have cleaned your existing database, validate the accuracy of your data. Research and invest in data tools that allow you to clean your data in real-time. Some tools even use AI or machine learning to better test for accuracy.
7. Analyze your data
After your data has been standardized, validated and scrubbed for duplicates, use third-party sources to append it. Reliable third-party sources can capture information directly from first-party sites, then clean and compile the data to provide more complete information for business intelligence and analytics.
So these were 7 steps to make your data clean and more usable. You can use some tools to help with these steps are mentioned bellow.
Here are some interesting Data Cleansing tools relating to data cleaning techniques, analysis and modelling of data,
- JASP – Open Source statistical software similar to SPSS with support of COS
- Rattle – GUI for user-friendly machine learning with R
- Rapid Miner – Another point and click machine learning package
- Orange – Open Source GUI for user-friendly machine learning with Python
- Talend data preparation – Data cleaning, preparation tool with smarts
- Trifacta Wranger – Data cleaning, preparation tool with the match by example feature
They are all open source or have free versions focusing on cleaning, analyzing and modelling data.
Finally, monitor and review data regularly to catch inconsistencies.