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6 Steps Involved In Processing of Data in Research

Before walking through the steps, you should understand what the data processing in research is. It is a series of steps that translates unorganized and meaningless data into the form, which you can analyse in a wink. Simply say, the conversion of images, graphs, table, vector files, audio, charts or any other data into valuable insight is data processing.

Let’s say, you intend to extract the email list of marketing managers in the Gold Coast. This data will be scattered in a series of websites, LinkedIn and other social networks. Those multiple sources of emails will appear in various formats. When you capture them from a variety of sources, the collected database will be treated as unorganized data. Since it carries a deep insight, the data analysts will evaluate it to pull out intelligence. This intelligence can be decisions, strategies or valuable information or patterns. The analysts use them to churn breakthrough for business progress.    

Steps involved in processing of data in research methodology:

1. Data Collection: The collection of data is a process of capturing and measuring up intended data in a standardized manner. The data extractors churn data lakes and warehouses.  In the meantime, the authenticity of sources is verified. The collected database is testified through hypothesis and validations. 

2. Data Preparation: Also known as preprocessing, data preparation takes care of cleaning and organizing data to prep up for the next stage. If the organisation hails from a different niche, it can look up the best one who could outsource data entry services with flawlessness. As it takes on errors, raw data are captured, extracted and then, sifted through verification funnel. This is how the redundancy is eliminated. This process flattens the roadways to high-quality data for deriving business intelligence.    

3. Data Input: Technically, the data processing carries out ETL (Extract Transform and Load). The aforementioned steps deal with the extraction and the beginning of the makeover of data. Now, the data need to be translated in a comprehensive language. However, the predefined goal sets the stage to interpret their language. This is how the sense is shaped up in the form of comprehensive data.   

4. Processing:  This step is brought about by machine learning algorithms, if done mechanically. The unforeseen patterns are pulled out. Then, they are dedicated to and manipulated according to the scope. Let’s say, you want to re-shape the patients’ data into an analytical format for diagnosing through apps or software. This step will execute algorithms that could tap on the patterns.   

5. Data Output/ Interpretation: This stage defines the usability of data to the people who do not actually know about dealing with typical data. The analysts visualise it in such a format that a naïve could easily derive senses through them. In simple words, such layout is prepared that the end users could understand in a wink. Thereby, the strategists from the intelligence domain could pull sense out of visual data through deep analysis.   

6. Data Storage: Now, it’s the step to save data in a place where you can easily get it from. It can be a data warehouse or a hard disk or repository. This final stage is carefully taken for the completion. While making that data into compliance with privacy directives, as GDPR, the network engineer keeps them protected. While doing so, the networking engineer comes in the front foot to define and assign authenticity and accessibility criteria. Fast turnaround time is always kept in the core while doing so.

The upcoming years will witness the data processing in a jiffy. The stretching of cloud can remotely handle this task with enough security. Thereby, the turnaround time to accomplish data processing will be short.