Data cleansing allows companies to work with real data in order to make important decisions regarding the growth of their business. Automating it to reduce time and ensure that the data is 100% reliable is the goal of effective data cleaning.
They say that the figure of the data analyst is and will continue to be one of the most demanded professions, something that is not surprising considering the amount of data that companies handle and that we live in the Big Data .
The decisions that are made daily in a company depend on and their quality, so that these decisions do not make them lose money. This is the main reason why data cleaning is essential in a company.
Today we will tell you what this process prior to the ETL is and what it consists of , that is, the extraction, transformation and loading of data into the data management system of a company.
What is Data Cleaning or data cleaning?
Data cleaning or data purification aims to improve the quality of the data and that these provide reliable and valuable information for decision-making in a business or organization.
This cleaning consists of correcting data that is incorrect, that is incomplete, that may be duplicated or that is erroneous , for example, due to a lack of coordination between data.
Data cleaning is part of data management, but it is important to do it before managing them for decision making. This data management is carried out by analysts or data engineers who are not only in charge of studying them, but also cleaning them.
What is data cleaning for?
Without quality data, the reports that are prepared with them will not be entirely reliable, much less the decisions that are made in this regard. Therefore, data cleaning serves to have a solid base of data on which to start making those decisions.
That said, correct data purification or data cleaning helps the business to:
- Have the business data more organized: since, on a daily basis, companies collect a huge amount of data and it is not always collected correctly, nor is its full potential used.
- Avoid errors with respect to this data: sometimes they are small day-to-day decisions, but they can be making us lose a lot of money.
- Improve productivity: since by doing it regularly and having the data organized, time is not wasted looking for old data.
- Reduce costs: both with regard to the previous point, and the fact that by constantly reviewing data, errors that may be causing losses to the company are detected earlier.
- Sales increase: thanks to obtaining more reliable data. This is something that marketing and sales departments especially take advantage of.
Data cleaning steps and techniques?
For data cleaning to be effective, it is important to carry out a series of previously defined steps, which will lead us to obtain quality data.
Those steps are as follows:
- Step 1: Delete irrelevant data. We have a lot of tendency to want to collect excessive data and not all the data we collect is useful to us, nor do we always have the data we need. For this reason, knowing which are the Kpi's that are going to give us the answer to the doubts that our business has is the first step that we have to take and eliminate those that do not contribute us anything.
- Step 2: Eliminate data duplication. Sometimes data analysis departments receive data from different departments and duplicate data arrives. Therefore, the second step is to check that there is no duplicate data. In this way we also make the system work better, by having less data to analyze.
- Step 3: Correct structural errors. Although technology has advanced a lot, they still don't have the intelligence of humans. This is the reason why it is important to debug errors such as spelling, but also to correct on our part those symbols that machines do not always interpret and that can lead to errors.
- Step 4: Make sure there are no missing data. Because the people who fill in the data do not always fill it all out, it is important to see if there are missing boxes to fill in, if there are double spaces that are giving rise to erroneous data, etc. If we see that there is an error that is always repeated, we must return to the structure of the form that is sent, so that we can avoid those errors.
- Step 5: Filter data. With the aim of checking if there are data fields that could be giving rise to erroneous data, such as when trying to average grades, but a single piece of data can launch a false result or a result that does not correspond to reality to 100%.
- Step 6: Validate the data. If at the beginning we talked about eliminating data that did not contribute anything to the company, in this case we are talking about checking if any are missing so that our business can make decisions correctly or if it is necessary to adjust parameters and add new ones. This type of validation can be carried out with data management tools that help automate these processes, as is the case with our tool.
conclusion
Any logistics and transportation company that wants to improve its business data, through proper decision making, based on real and updated data, must go through a data cleaning process on a regular basis.
It will help them, not only to have more organized data, but also to reduce costs, improve company performance and, therefore, improve economic figures.
