Technology helps language evolve and language helps technology evolve. The perfect example of this is data and the new vocabulary shaped around it. Lets discuss some of the latest terms on data.  

In a world where digital transformation and growth are heavily dependent on data, IT managers, CTOs, and even CFOs are struggling to keep with terminology on data science. On a daily basis, we are coming across a large number of terms. Some of them seem obvious and “politically correct”, whereas others seem as if they are only buzzwords.  

Let’s discuss a few:

1. Big Data 

The #1 buzzword of our age. As it is, it refers to the amount of data that does not fit into a standard database in order to be stored and processed effectively.  

Traditional databases have significant abilities to manage large data sets and despite the fact that big is beautiful, big is also difficult to embrace.  

2. Machine data 

Simply put, this is data coming from the usage of machines and from parameters such as APIs, cloud applications, sensors, and things of digital infrastructure.  

Made accessible, tidy, and usable, machine data can be key to business growth.  

3. Open data 

Open data are data sets available for anyone to use and apply analytics to. Most commonly, open data are also free to republish.  

This type of data is often used by public authorities to improve facilities and share knowledge and insights with the private sector, and vice versa. 

It is basically the “open source world of data”.  

In regions such as the EU, select organizations are often obliged to share part of the data they handle and make it open to everyone, for the common good and in order to empower research and innovation.  

4. Dark data 

Last, we have to refer to a very ambiguous term.  

Dark data is not “harmful” data, as most people tend to believe. The term is commonly used to describe information and assets not used on a regular basis or not used at all.  

So, what is the story with the names around data 

Well, data-naming is a fairly new trend and it is here “to stay”, as different industries are giving data sets different naming approaches.  

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