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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:


Big data also owe their origin to the proliferation of fixed and mobile devices daily used. Everything that passes through Social media, the various CRM systems, a supermarket cash desk, included phone calls to the call centre are part of the big data area. How?

Big data is not a trend, but a managerial necessity, since it helps understand the reactions of the markets and the perception of brands. Big data identify the factors that drive people to prefer a certain service or product and purchase it instead of another. It also segments the population to customise action strategies and allow new experiments to be carried out thanks to the enormous availability of unpublished data collected. This results in a gain in terms of predictability, because there is a manifold of useful information available, which allows very likely simulations. Moreover, big data give the chance to open new business models.

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


Google, Facebook, Twitter e Amazon know everything about us because they have our data. These are unstructured data studied by sentiment analysis techniques, which are able to understand the emotions contained in textual information. These data are useful for companies and politicians to trace the direction of public opinion.

Smart cities are clear and simple examples of big data management and big data analysts. Street lamps rigged with sensors to better manage traffic and monitor pollution, CCTV cameras to reconstruct car routes outside the premises and banks, RFID tags to put the bins in contact with recycling bags. All these are examples of how data analysis can improve the life of the community.

Even in retail, the application of big data brings benefits, increasing margins by 60% thanks to the analysis of purchasing behaviour. Thus, it is possible to better manage receipts, loyalty cards, interactions with promotions, announcements, email marketing, newsletters and so on. This manifold of data represents an immense amount of valuable information that enables to build the Customer-tailored offer. Geo-marketing and geolocation generate big data that can generate billions of dollars, if well exploited.

Exploiting big data management means going beyond order processing, implementing new systems for marketing campaigns and intelligently managing loyalty programs. To have a total view of the Customers, the products and the whole company in the market it is also important constant monitoring the received feedback, including the management of complaints

According to McKinsey analysts, public administrations in Europe can achieve savings of 100 billion euros from good big data management, increasing operational efficiency. A figure that could increase dramatically if big data were also used to reduce fraud and errors, achieving fiscal transparency.

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.

Digital markets of big data

The real challenge of big data lies in the ability of companies of correctly analysing the data obtained, following this process: query, response and detailed view. Thanks to the growing meticulousness of the algorithms, it is possible to interpret every piece of information that runs through the network, revolutionising traditional simple business models.

Companies exploit only part of the potential benefit given by big data, not only for lack of budget linked to investments, but above all due to the lack of specific skills. In this market, in fact, there are still too few big data managers who can enhance company data and the sector is new, therefore it requires unprecedented preparation. To study big data communication, leadership, analysis and problem-solving skills are necessary, together with excellent team building.

The four types of professional profiles that will be increasingly requested by companies:


Here the trends that are transforming the big data analysis scenario in organisations:

Recognising big data and better exploiting them

It is not always easy to recognise big data compared to traditional data, but it is important to focus on the 3 Vs:

To these 3 Vs other important variables are added: variability, truthfulness, visualisation and value. To distinguish the big data from the standard ones the presence of the three Vs is necessary. The road-map to follow to use big data in business and marketing strategies can be summarised in 5 main steps:

The objectives can be specifics or of micro-analysis, but among the main business objectives there is the improvement of production efficiency and the optimisation of the consumer purchasing process.

Usually the data coming from outside and not from the company itself are analysed, but the most interesting analyses are obtained from the latter. CRM is the first major source of data that a company can draw on, while other data sources are multiple:

When the amount of data increases it is necessary to create new forms of data management, benefitting the entire business.

After defining the objectives, choosing the sources, obtaining the data and inserting them into the specific technologies for analysis, the next step is big data analytics. There are many analysis tools available, for example the Sentiment Analysis, which collects user reactions and attitudes or trends in real time, based on comments on social media.

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