Big Data has now become part of the global technology market. Still, this definition is often used without having clear what it is or what precautions and technologies are necessary to exploit them to the fullest in their potential value. Over time, their definition has changed, the very concept of Big Data has evolved because the sources that generate them and consequently the characteristics of Big Data have changed, but the basis remains constant: Big Data “does not exist” as it is new entities of the modern world, rather they can be seen as an evolution of the concept of data that differs from the “classic” data because it has particular characteristics that make it very high added value.
The analysis of Big Data can provide valuable information to improve the business: Big Data can be a real and updated photograph of the context, so companies can use them for optimization problems, to make processes more efficient, or to find new opportunities and new ways to increase profits. Let’s see what Big Data is and what added value can be obtained by setting up adequate Big Data Analytics processes.
Applications Of Big Data
The role of Big Data and Big Data Analytics is central in all sectors and all internal departments. Of course, using them can be more intuitive than in others.
Marketing & Sales
This, for example, is one of the departments where it is easier to understand its application: the challenge is to satisfy your customer by proposing the right solution at the right time and with the price that the customer may deem appropriate. Until a few years ago, this operation was calculated only based on the age, sex, geographical location, or income of the customer, while now there is much more information available, not only personal data but also and above all behavioral; you get much more accurate profiling, and this is an excellent starting point for personalized actions.
The Manufacturing Sector
Can obtain great advantages from Big Data Analysis: we are talking about monitoring the production chain, industrial IoT, and optimizing warehouses and logistics.
In The Retail Sector
Big data can help predict the CLV of customers or the trend in demand for some products or, again, predict the performance of the Sales department.
In The Banking Sector
Big Data gives excellent results in terms of Risk Management (fraud prediction, Stress testing, Anti-money laundering, etc.). Finally, Big Data is used (never as a substitute for the doctor!) In predicting pathologies based on the patient’s history and in improving the use of resources in the face of a surge in accesses in the medical sector. In general, we can summarize the advantages obtainable by exploiting Big Data:
- allow you to obtain more complete answers to business questions because they are calculated on a very large, truthful, and complete set of data;
- Therefore, decision-makers trust in data increases, which also entails evolving the strategic approach from a data-driven perspective.
Thanks to Big Data Analytics, it is, in fact, possible to reduce costs, shorten the time of business processes, develop new products, optimize offers and, last but not least, make smarter decisions.
How Big Data Is Generated And Collected
We can divide Big Data into 2 large families: Big Data automatically generated by machinery, characterized by very large volumes and speeds, but with uniform typology, and Big Data generated by people, therefore smaller and slower volumes, but characterized by a ” very great variety of contents (text, photos, videos, geolocation, etc.). Generating the first type of Big Data is easier to understand; these are log data from production machines, sensor outputs, monitoring systems in general, and data produced automatically by devices. On the other hand, those generated by people are often unconscious users.
Every device we use generates Big Data, every action both on the web and in daily life. Let’s think, for example, not only of the activities on social networks or the use of search engines but also of the loyalty cards used in shops, credit cards and ATMs, security cameras, GPS navigators that we use in the car. In a more or less conscious way, we continually leave digital traces. The collection of Big Data does not differ in the steps to be done from the collection of “classic” data. Both are made up of two main phases: the phase of data integration and transformation and that of archiving. The big difference in Big Data is implementing these phases from an architectural, technological, and logical point of view.
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