With the term big data we refer to data sets that are so large and complex that traditional software and IT architectures are not able to capture, manage and process in a reasonable time.
If a traditional database can handle tables made of millions of rows and tens or few hundreds of columns, big data require tools that can handle the same number of records, but with thousands of columns.
Moreover, data are not often available in a structured form, that is, arranged in rows and columns, but are organised in the form of documents, meta data, geographical positions, values detected by IoT sensors and many other forms, ranging from semi-structured to completely-unstructured ones. In fact, the data that make up big data archives can come from heterogeneous sources, such as Web browsing, social media, desktop and mobile applications, but also from sensors embedded in thousands of objects that are part of the so-called Internet of Things (IoT).
Traditional SQL databases are designed for reliable transactions and ad-hoc queries on well-structured data. This rigidity represents an obstacle to some types of applications. NoSQL databases overcome these obstacles by storing and handling data in ways that allow greater flexibility and higher operational speeds. Unlike traditional relational databases, many of the NoSQL databases can scale horizontally over hundreds or thousands of servers.
BIG DATA ANALYTICS
The term Big Data Analytics is often used to describe the analytical techniques used to extract information from huge datasets that require advanced technologies for storage, handling and representation. Such techniques come from a vast number of disciplines such as statistics, data mining, machine learning, and so on. They are all very useful techniques and can have various applications.
BDA can be classified into four major types of Data Analysis:
The use of predictive and prescriptive analysis can play in favour of the business strategy, by solving problems related to the development and sale of products and services, and those concerning the organisation of the structure.
THE IMPORTANCE OF BIG DATA
Through the use of big data, both companies and organisations can collect data from any source and analyse them in order to find answers that allow to:
When big data and analytics are put together, it is possible to:
The Analytics market confirms the trend seen over the last three years, with an average year-over-year growth of 21%, but also reveals an important gap between large enterprises and SMEs, which represent only 12% of the market. In fact, in 2018, only 7% of SMEs started projects based on big data analytics, while four out of ten declare that they carry out traditional analyses on their business data. But the good news is that about a third of them seems to be on the right track both in terms of awareness, and technological and process adaptation.
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