Anglicisms in business jargon are commonplace. Finance students cannot ignore terms like cloud computing, framework, data visualization, reporting, etc. They are bound to hear them. Those in management frequently hear the term Big data management. They have a vague knowledge of its meaning. In order to further enrich this vocabulary, let's take a look at this terminology. First, let's take a French parenthesis by decoding these three words. Big for large or big. Data for data. Management for management. It is therefore a question of managing a large amount of data , which implies factors to take into account. Which ones? Why? And how is this process characterized?
To put it simply, we are talking about large-scale collection . If we want to popularize this computer lexicon, we can simply say that it refers to technologies a engineer database imed at collecting, storing, processing and analyzing large volumes of information. This collected information is then stored in three different boxes that we call the three "Vs".
What are the three Vs of big data management?
Let's continue with a simple metaphor to understand. Since we have drawn on the storage register, we just need to imagine a wardrobe divided into three parts. Each of these doors would contain other sub-parts. Our three "V"s would correspond to: variety, volume and velocity.
Variety
The more heterogeneity there is in the data, the more difficult the classification work is. Big data management is about resolving the multiplication of formats and the complexity of separating them. Exploiting and analyzing these storage mines takes time. The job of data scientist evolves with technological advances. More and more efficient algorithms are emerging, and yet, it remains a headache. To be able to optimize this enormous logistics, the ideal would be to standardize the organization of raw data on information media. We are tending to arrive at this standardization. However, the mix is still too present.