Post by account_disabled on Mar 6, 2024 18:38:20 GMT 14
So what are the key elements , or rather the steps, of data storytelling? We mentioned some in the previous paragraph, but let's go into more detail: Big Data Big Data is the large amount of computer data collected thanks to technology. This is dealt with by data science and data scientists (one of the new digital professions most requested by companies) who, by defining algorithms and using software, correlate this enormous amount of data. When used for business purposes, we talk about business intelligence.
They are defined as Big based on 3 characteristics: Volume , the amount of data, structured or unstructured, generated every second; Speed with which new data is Hong Kong Telegram Number Data generated and reaches the system that performs analysis on it; Variety , i.e. various types of data that are generated, accumulated and used. In relation to the last characteristic (variety) the data used can be of various types: Structured , these are the data used before the advent of Big Data, i.e. collected for the same purposes for which they are processed, according to predefined fields and with ad hoc formatting; Unstructured , data stored in its native format and not processed until used.
The advantage lies precisely in the accumulation rates (higher than structured ones) and the freedom of the original format. Examples of this include emails, social media posts, chats, images, etc. Semi-structured , that is, they have metadata that identifies some characteristics and therefore have sufficient information to catalogue, search and analyze them, a middle ground between the first two. Data science deals with discovering the links between different phenomena, very often correlations, and predicting the phenomena on the basis of statistical calculations, also in the business context.
They are defined as Big based on 3 characteristics: Volume , the amount of data, structured or unstructured, generated every second; Speed with which new data is Hong Kong Telegram Number Data generated and reaches the system that performs analysis on it; Variety , i.e. various types of data that are generated, accumulated and used. In relation to the last characteristic (variety) the data used can be of various types: Structured , these are the data used before the advent of Big Data, i.e. collected for the same purposes for which they are processed, according to predefined fields and with ad hoc formatting; Unstructured , data stored in its native format and not processed until used.
The advantage lies precisely in the accumulation rates (higher than structured ones) and the freedom of the original format. Examples of this include emails, social media posts, chats, images, etc. Semi-structured , that is, they have metadata that identifies some characteristics and therefore have sufficient information to catalogue, search and analyze them, a middle ground between the first two. Data science deals with discovering the links between different phenomena, very often correlations, and predicting the phenomena on the basis of statistical calculations, also in the business context.