We’ve written about the vast amounts of data that organizations collect and process daily. Now, we dive into Data Cleaning! An important matter if your organization wants to process and analyse high quality data.
What is data cleaning?
First things first!! Data cleaning (or data scrubbing) is the process of removing or correcting data that is corrupt, incorrect or has become unnecessary, before data analysis processes. By doing this, only relevant data is analysed, providing you with better quality results. While it can be a tedious process, it’s important that you do it, so that your data insights are as optimized as possible.
The problem with bad data!!!
If your data is not high quality, or “dirty”, your analysis results will not be as accurate as possible, even becoming detrimental to your business in certain cases. No one wants to make bad decisions that could cause losses in time and profit. So, for example, if you would train an AI with low quality data, it’s results wouldn’t be as good as if it were trained with high quality data.
What are the advantages of maintaining your data clean?
- Optimized time management and processes – If your data is clean, most processes will be optimized, because there won’t be wasted time analysing, for example, duplicate data.
- Optimized productivity – because there isn’t wasted time, productivity increases, which leads to profit increases and higher customer satisfaction.
- Decision making –With high quality data, decisions are made faster and with more accuracy.
These are just a few advantages of having clean data, and there are many more a, depending on the type of organization.
What are the steps to clean your data?
Depending on the type of data your organization collects and possesses, there are different techniques to clean it, but to simplify it, here’s a streamlined process:
- Exploring your data – This is the first step to clean your data is exploring it. This is where you will look at all your data, see what it is about and segment it.
This step will be helpful to break up all data and examine it. - Filtering your data – This is when you filter all data and start working on the data you want to clean, since there might be some that you don’t want to touch, because it might already be clean and ready. By filtering everything, you remove all “noise” and irrelevant data and focus on the data related to your task.
- Cleaning your data – This step is the final and most crucial step. You will do an in-depth analysis of the remaining data and start removing irrelevant and bad data. This will massively improve your data quality.
As you might imagine, there is a lot more to the process that what’s written here. And there are solutions to help you through the entire process.
A clever digital transformation… with Texport!
Texport provides the opportunity to implement a clever digital transformation to the way the organization interacts with their digital content, consolidating and enriching data – tagging, categorizing, auto-classification, applying AI – thus increasing efficiency and optimizing the investment.
The best… it’s container friendly and Cloud ready, adding security and backup options in future relations. Download here the Texport – Alfresco Exports & Imports DataSheet and contact us!
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