You can spend days, weeks, or even months, but there is no way of extracting insights with low-quality data. Unless the data is cleaned and made high-quality, the insights, information, and relationships achieved with it will offer no value. It would be like finding a piece of gold in the garbage, only that you will not find any gold.
But, what if you still go ahead without proper data cleaning?
Here’s what will happen:
Let’s say that you feed poor quality data to your algorithm. It gives you results or pseudo insights and relationships. You prepare a report and take it to the subject matter expert. But he finds flaws.
Don’t worry. This is still a positive scenario because then you can go back and remedy the mistake.
But, what if this report goes ahead and your business makes a decision based on the reports.
Then, there is a high possibility of this decision going wrong, which means you will get in trouble.
Data cleaning is a crucial activity, without which the whole activity of data analysis becomes useless and completely insignificant. If you feed enough amount of data but quality data to an easy algorithm, it will offer you more value than offering poor data to a complex algorithm. This may give you the idea that data is the heart of the process. It’s true!
Data defines how your analysis will turn out and data defines how effective decisions you would be able to make.
Hence, keeping this thought in mind, we have prepared a list of best practices which will help you clean your data without failing.
1. Check for Errors
Humans are intelligent and in case of data cleaning, their intelligence is observed in the way they track errors. You can’t keep using the same method to observe similar types of errors in the data. Over time, you have to track the types of errors, find the source, and improve your ability to identify these errors soon enough to reduce the time of cleaning.
This practice is extremely beneficial if you have integrated your system with fleet management. You would be able to reduce bad data or identify where it is coming from.
Of course, every data analyst has their own way of cleaning data, which may or may not work for other analysts. But, once you know what works for you, you need to standardize this process to make it simpler for you as well as the team to decide an entry point. When you know the entry point and you know it is feasible, you can decrease data duplication risks.
Always, and by that, we actually mean always validate the data you have cleaned recently. You can’t rely on the first cleaning process. You have to validate if the database is of high-quality.
To make your task easier there are several tools, including AI tools, which can help you validate the efficiency and quality of your data.
4. Check Duplicates
Checking for duplicates in the starting can save a lot of your time. But, if you are using a tool which can clean and evaluate bulk data, this tool may be able to automate the process of removing duplication. Then, you don’t have to worry.
If you are not using any tool with this functionality, don’t forget to remove duplicates from the database.
Communicating your data habits effectively to the rest of the team helps in finding an optimal method. This is not really a task you achieve to clean data but it is a process which helps you lead towards high-quality clean data. Without effective collaboration and communication, data cleaning standardization can also become a hassle.
So, always communicate the results and new discoveries to the related stakeholders for better execution.
When you are able to successfully clean your data and offer high-quality data to algorithms, you are able to extract useful, valuable insights. These insights help you make data-driven, futuristic decisions and these decisions help you achieve high ROI. Hence, utilize the above steps and create the right data cleaning protocol for your organization.