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.


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:

  • Descriptive analytics: the starting phase is usually the Descriptive analysis, which is made up of all the tools that allow to represent and describe the reality of certain situations or processes, also in a graphic way. For example, in the case of businesses, it is possible to carry out the representation of business processes. Descriptive Analytics allows the graphical display of performance levels;
  • Predictive analytics: then we have the Predictive analysis, based on solutions that allow to carry out data analysis, in order to design development scenarios for the future. Predictive Analytics is based on mathematical models and techniques, such as predictive models, forecasting and others;
  • Prescriptive analytics: with Prescriptive analysis we enter in the field of tools that associate data analysis with the ability to take and manage decision-making processes. Prescriptive Analytics is based on tools that provide strategic indications or operational solutions based on both Descriptive and Predictive analysis;
  • Automated analytics: the fourth phase is represented by Automated Analytics, which allow to enter into the scope of automation with analytics solutions. Based on the results of descriptive and predictive analyses, Automated Analytics can activate actions defined on the basis of rules. In turn, these rules can be the result of an analysis process, such as the study of the behaviours of a specific machine in relation to certain conditions being analysed.

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.


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:

  • Cut costs;
  • Develop new products;
  • Optimise their offer;
  • Make more informed decisions.

When big data and analytics are put together, it is possible to:

  • Determine in near-real time the causes of failures, breakdowns or defects;
  • Create ad hoc offers in the stores, based on customer habits;
  • Recalculate entire risk portfolios in few minutes;
  • Identify fraudulent behaviours before they affect an organisation.

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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