For this purpose, data analytics has become a common practice among businesses. And with the right data analytics training and certification program, you will find success in the field. A key aspect of data analytics is data cleaning. This process of also often referred to as data scrubbing or data cleansing.
Any organization that wishes to truly derive benefits from their data needs to incorporate this step. Quality decision-making based on insights extracted from data can only be done once this step is given importance. But what exactly is data cleaning? And what is the importance of data cleaning and pre-processing in data analytics?
With this article, we will help you get started with an answer to these questions and more. However, enrolling with the right institute for your data analytics training and certification is the key. But before you do that, here are the essential things you need to know about data cleaning.
What is Data Cleaning?
As suggested by the name, data cleaning is a process that involves removing duplicate, corrupt, incomplete, or incorrect data within a dataset. Since organizations derive their data from various sources at the same time, the chances of data getting duplicated are also high. In certain cases, the data may also be mislabelled.
If the data we are processing is incorrect, the outcome is sure to be unreliable. And the worst part is that the appearance is totally correct. The data cleaning process cannot be prescribed in the exact steps. Why? Because it must be changed as per the dataset in hand. However, establishing a template for the data cleaning process is necessary to keep a track of the process you must follow.
What is Data Pre-processing?
Data pre-processing is a crucial task. Data pre-processing is a data mining technique. This process is used to transform raw data into something that is more useful, efficient, and understandable format.
To bring out the best in data, data pre-processing becomes essential.
Why do we require Pre-processing?
Pre-processing is an important task in the data mining technique. In general terms, we can say that real world data is often
- Incomplete
- Inconsistent
- Noisy
Data cleaning is a task in data pre-processing.
Thus, we can say that these two are not complete without one another.
Why is Data Cleaning Necessary?
As we have already mentioned before, data outcome is dependent on the quality of the data. Algorithms and analyses only work on good data, which comes as consequence of data cleaning.
Dirty data is highly harmful to companies. In addition to losing money, they also lose valuable time, which could otherwise have been used to bring out a change.
With data cleaning, more accurate, consistent, and structured data is produced. This allows for more intelligent and informed decisions. get join our data science training program.
What is the Data Cleaning Process?
The process of data cleaning typically involves six steps. Here is what you should know about them.
Dedupe – dupes or duplicate are often a result of data being blended from various sources. These sources may be databases, website, spreadsheets, etc. Dupes also happen when a customer has submitted redundant forms or has various points of contact with an organization.
Remove Irrelevant Observations – the processing time can slow down significantly when data that is irrelevant is present. When these are removed, they are only excluded from the current analysis. Hence, it remains in the source info.
Manage Incomplete Data – there are a few reasons why lead to data missing certain value. However, missing values does not mean we do not address it. For analysis, it is integral to address them in order to prevent any miscalculations or bias. Once the incomplete values re isolated and examined, it is decided if those are plausible.
Identify Outliers – far removed data points have the power to distort the reality of the data to a great extent. There are a lot of numerical or visual techniques of identifying these outliers. These include histograms, z-scores, scatterplots, box plots, etc. Depending on the statistical methods in use and how extreme one is, the outlier may be omitted or included upon identification.
Fix Structural Errors – correcting inconsistencies and errors is important. These include abbreviation, formatting, capitalization, and typography. Looking at the data types will bring forth a lot of changes that need to be done.
Validate – validation is basically when we ensure that the data is consistent, uniform, complete, and accurate. Validation happens throughout the automated data cleaning process. However, it is still important to ensure everything is aligning by running a sample.
The Future of Data Cleaning
Data analytics has become an essential aspect of companies today. However, to be able to make the most of this technique, data cleaning is integral. Begin your journey today by enrolling with the best data analytics training and certification program.
