What Is Data Processing?

Data Processing

Companies generate data at an enormous rate these days. If this raw data is left as it is, it will not provide any information which will limit the growth of an organization. Data processing comes here to save the day. If done correctly, data processing may give you a set of useful and readable data to analyze.

You may be wondering what exactly it is and how data processing works. Don’t worry we’ve got you covered. Here’s everything you need to know about data processing.

Basic definition

Data processing is the process of converting this raw data into useful information. The converted data will be much more readable and will give good insights. It is a long process consisting of several steps.

Companies are so concerned about data as it is quite important. If done wrong, incorrectly represented data can hinder the performance and show patterns that aren’t even there. Now-a-days, companies hire data scientists or even a team of data scientists to process raw data.

How is data processed?

Data processing is done by repeating certain steps in order. These steps combined with the cyclic nature of the process are called the data processing cycle. There are 6 steps included in this cycle:

1.    Collection of data

Data collection is the first step in the cycle. As processing a small amount of data is not feasible, you require a large amount of relevant data. It is important to take note of the source you are getting your data from. Only updated and good quality of data should be worked upon. If the source is not viable, you may end up reading and working on insights that are just not there anymore.

2.    Preparing the data

Once you have data on your hands, you now move on to preparing the data for processing. Preparing the data, sometimes also known as pre-processing of data, includes cleaning the raw data. You clean data by removing any kind of errors, duplication, and incompletion from it. The quality of data you work on matters a lot and sometimes duplication and errors can cause the data to show unexpected results.

3.    Data input

Exactly as it sounds, data input is the part where you feed the data to the machine you are working with. Before the data is fed to the device, it needs to be converted to formats that are readable or it won’t be able to work on it.

4.    Processing

The input data is then processed by the computers. Data scientists use different machine learning algorithms to process the data. Making clusters, labeling data, etc. can easily be done by these algorithms.

5.    Data output

In this step, you have your processed data and everyone from analysts to a normal user can take a look at it. Insights and different patterns are recognized which are then used to create different strategies.

6.    Storage

Most processed data is stored safely for future use. This data can be used for the next data processing cycle or used for comparison with a different tenure’s data to check the rate of development.