Data Processing – Stages and Definitions

Data Processing – Stages and Definitions

One thing that’s certain –

“Thanks to data, you’re probably in the strongest position to gain essential insight into your business and customers today.” – Forth Communication

But what exactly is data processing?

Data processing is a step by step process where raw data is collected and translated into information that businesses can use. These tasks would usually be performed by one or more data scientists. One very important factor to take into consideration is, this process needs to be done immaculately in order for it not to have a negative impact on the end product i.e the data output.

How does it all work? Let’s go through the key steps of data processing.

  • Collection – The data which is going to be used is gathered or pulled from any relevant available sources. Be that online, offline, in-house or purchased. It is of the utmost importance that this stage is carried out meticulously so that the data collected, which is later used as information, is of the highest possible quality.
  • PreparationOnce all your data has been pooled, you move onto the preparation stage. This is the stage in which all the raw data you have collected is cleaned and organised.
  • InputAfter your data has been cleaned and is shining brighter than that Colgate smile, you can begin to transfer it over to its destination. For example, it could be a hotel CRM like For-Sight, that uses guest data to enhance a hotels PMS or another other of their core systems to “unlock the guest journey.” This is the first stage in which your raw (yet still clean and organised) data begins to take shape as usable information.
  • ProcessingYou are ready, you’ve got your clean data! The data has arrived at its destination and is ready to be processed. The processing stage is done by using machine learning algorithms. Depending on the data that is being processed (names, emails, addresses, etc.) the process itself may vary, so that the will be specific to the intended use of the data
  • Output and interpretation – We have finally arrived where non-data scientists and the general public can make sense of data and finally use it appropriately. At this stage it can be translated into a variety of forms; plain text, graphs, images, videos, etc. From this point onwards, the company or organisation can begin to use the data effectively across its departments.

Now we are not done just yet.

  • Storage – Much like saving a document on your computer, the same sort of concept can be applied to the final stage of data processing. As the data processing cycle is nearing its end, the data itself will need to be stored for future use. Some of the data may be put to use immediately whereas much of the rest of it will be put to use at a different time. In order to be compliant with GPDR, your data needs to be stored properly. The benefit of having your data stored properly is that it can be easily accessed by any member of the team or different departments within your company when needed.

 

Want to know more about how you can effectively use your data? Get in touch today and talk us through your data needs.