What do you mean by raw facts?

Raw data or primary data are collected directly related to their object of study (statistical units). When people are the subject of an investigation, we may choose the form of a survey, an observation or an experiment.

Surveys especially present the advantage that they allow for a vast array of options in terms of analysis.

Only if we can access all of the primary data of a survey, i.e. the individual responses of all respondents on all issues of an investigation, can we calculate distributions or correlations and such. In contrast to raw data, we speak of secondary data if the data have already been aggregated and thus no longer contain all of the information of the original investigation. 

Please note that the definitions in our statistics encyclopedia are simplified explanations of terms. Our goal is to make the definitions accessible for a broad audience; thus it is possible that some definitions do not adhere entirely to scientific standards.

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If you have spent some time in the corporate environment you have probably heard term raw data on numerous occasions. However, what does raw data really mean? Is it uncensored, is it yet to be digitalized, is it a bunch of numbers that don’t mean anything yet? Well, it can mean a lot of things, and here we will explain raw data in-depth and also provide explanations on how it is different from other types of data. 

Wendy

Aug 21, 2020 ● 2 min read

Raw data definition and explanation 

When we say raw data, we typically refer to data that is readily available but cannot be easily used speaking. Raw data is compiled from multiple sources, and different sources can often mean that information is displayed in various formats. 

For example, if you want relevant metrics for your online shop, you might get several website visits per month. That number is raw data as it does not offer anything related, except the number of visitors your site had over the past 30 days. For you to extract meaningful insight from that number, you will have to process the data over and over again, using filters, algorithms, or other means that help you get more concrete details. 

If you have an e-commerce site, you probably want to know how many people bought the product or how many abandoned their carts. Also, you want to see the site’s bounce rate, whether visitors find websites via organic search or referral. And where your shoppers are located, etc. There are so many useful metrics that can be extracted from raw data, which is why it’s valuable but not useful on its own at the same time.      

Difference between data and raw data

Now that we covered what is raw data, it’s easy to assume what the term processed data, also known as data, would entail, right? Well, not exactly. Processed data can always be processed further, or you can extract even more precise information, so even just saying processed data can be too general. 

So to make a better clear distinction, let’s say that data is the product of organizing raw data and turning it into a unified product that’s easier to manipulate or navigate. To do this, we use SQL or structured query language to achieve the desired format and effectively communicate with the database. However, the main difference is that you cannot do useful data analysis on raw data, whereas you can do data analysis on data or processed data. 

Many tools allow you to this intuitively like Whatagraph. Even if you are not a developer or know how to code, you can still perform data analysis, have unified file formats, and make relevant data input in a formal report using primary database or data from different sources. 

Why is raw data important?

Although raw data can take a considerable chunk of space on storage devices, you should not remove it or delete it once it has been processed. Processing data implies that you are going to filter out information and remove you deem redundant, or put them in a different context. Having access to raw data allows you to trace back those decisions and ascertain whether original processing was done correctly. In other words, data processing and data analysis is also trial and error experience, so having access to a source data is a must.   

Published on Aug 21, 2020

Wendy is a data-oriented marketing geek who loves to read detective fiction or try new baking recipes. She writes articles on the latest industry updates or trends.

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