A diamond needs to be processed before it can exhibit its brilliant qualities.
Raw data is like this rough diamond that you need to refine before it can be useful to you. Also called “atomic data” or “primary data”, raw data is the data that has just been collected from its sources and is still disorganized to provide you with any clear insights. It has yet to undergo manual or computer processing to serve any useful purpose.
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Humans extract, analyze, process, and use raw data through software that can help them come up with conclusions and draw projections for their current requirements.
The Sushi Principle
Some may think that processed data is much easier to use because of how it is already structured. However, in the context of data analysis and machine learning (ML) technology use, data must be presented in its raw format.
According to the Sushi Principle, raw data is much better than “cooked” or “processed” data since the use of the former allows you to quickly carry out your data analysis in a secure and easily understandable method. Data can be efficiently processed when you can do continuous reiterations of your queries rather than planning for it beforehand.
3 Ways to Keep Data in Raw Format
When you want to use your data in its raw format, here are the different ways to do it:
1. Using a Simple and Well-Tested Pipeline
When you have complicated and numerous pipelines, it is easy for you to collect data. However, there would be no way for you to verify if the machines are doing accurate calculations. A simple pipeline also gives you the freedom to be flexible in terms of the tasks you want to do with your data.
2. Collecting and Keeping Original Data
Keeping a copy of all your original data gives you the ability to trace the source of any statistic and allows you to iterate as many queries as possible. In short, when you have all of your original raw data, you don’t have to waste time backtracking, and you can proceed with doing tasks that add value to your work.
3. Summarizing and Sampling During Queries
This process allows you to make sure that none of your summary statistics have any flaws. Plus, it is easier for you to take samples of your data once you have an idea of what information you need. This way, it would be easier for you to get meaningful and statistically significant results.
Summarizing and sampling early on can make your data prone to inaccuracies. Shrinking your data may leave you with less than enough to derive statistically significant responses to your queries.
Users typically need raw data for analysis and often obtain such by extracting a data file that may take the form of a Comma Separated Values (CSV) file.