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What Are the Stages of Data Processing?


Data is considered as the new oil, which is true. But without processing, it is good for nothing. In short, its processing is important, as it structures knowledge in a comprehensive format. You get through what is interesting or what can really transform the way your business typically goes. It helps to evolve such intelligence that can easily resonate with your domain.

A business analyst picks something meaningful out of a massive set and then, the journey to its makeover begins via cleansing. Upon proper structuring, such models are put aside that have the potential or that can actually add on opportunities to make you run ahead of time and competitors. This is why data processing is important. Certainly, being digital helps any organization to do it quickly. You just set your codes to scrape information via APIs. That’s it. You get all what you need to observe for getting insight.

1.       Collection
Typically, it is about collating information from various sources upon thoroughly getting through the goal or aim. One of the most suitable methods from interviews, questionnaires, observations, documents & records, focus groups and case studies or datalakes/warehouses are mined to get the valuables. If the real-time details are required, IoT devices come first to extract.  
What you need to think about is the authenticity. Facts from trustworthy sources get transformed into high-yielding decisions upon analysis.

2.       Preparation
Also called pre-processing, this stage ensures filtering and cleansing of raw information. The corrupt records are set apart through de-duplication and validation methods from the useful ones. If required, the research team keeps with normalization or standardization to eliminate redundancies. Eventually, the high-quality compilation is made processing-ready.

3.       Input
The facts and figures are now ready to put in the proper place or destination to translate them through analysis into learning for the lifting as per goal. This procedure may need to reformat so that whatever you are going to place looks compatible to your repository, like the Cloud or server.

4.       Processing
This stage is intertwined with machine learning algorithms and your goal. The goal, certainly, is one of the prior things that you should go through thoroughly. Then, the algorithms are fed inside so that these can process those particulars in the lakes or warehouses or CRMs without encountering with frictions. This is how several ‘if or not’ conditions are passed through many-a-times in Python, R or any language that is hired for functionality testing specifically. Finally, data scientists get the breakthroughs in the form of patterns or models that they are looking for.

5.       Output
This is the stage where tried-and-tested models or patterns are aligned separately for steering them to the next level of data processing.  The makeover of these patterns is done so that even a layman can read and understand them through graphs, charts, videos and images or plain text. In short, these patterns are prepared for analytics.

6.       Storage
A proper storage will provide an opportunity to access the output over and over again for the transition in the future. However, nothing decays as fast as the real-time details. So, some information is used immediately to meet the end goal, whereas the rest of its pieces are put safely. In the meantime, data regulation policies and guidelines are considered on a serious note.

This is how the process goes on to ground up a platform for predictive or descriptive analysis. These stages can be incorporated with the particulars of any industry or any domain. The outcome will certainly prove groundbreaking because the decisions would be drawn from the observation of performance.
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