The importance of Big Data according to Daniele Marinelli. Big Data is a term that today is becoming increasingly important, and it has become impossible not to consider its importance in the field of earnings. The usefulness of people’s data can be considered important both based on the quality of the information and based on the models of interpretation of this data. No matter what industry we are talking about, data from potential customers and users are very important to see earnings increase and many are realising that this data are of concrete value.
To recap, there are at least two inseparable factors that can support us to judge the value of data, namely the quality of information and the models of interpretation. In fact, to fully understand the value, we cannot separate their quality without fully considering the level of the interpretative means we have at our disposal to understand them. One area where this type of reasoning finds fertile ground is certainly real estate.
Big Data and real estate
To understand the importance of data quality in this specific sector, that is the real estate, one must first specify that they cannot be classified under a single category. The most precise explanations for obtaining information on the price of real estate are those that can be understood from the exact valuations, or those obtained from title Deeds. As for the title deeds, however, it must be considered the fact that they may suffer sudden price changes for unpredictable reasons, compared to the established price. In addition to the characteristics of the structure itself, the price estimates must then be established based on the construction costs, or at least that is what the practice establishes, and then go to place within the price ranges on which the real estate market is based. Although it is a complex assessment, it is still a value that is based on objective estimates, such as the cost of building materials.
However, reason and objectivity sometimes are not enough to define the price of a property as many people in the industry or those who have recently bought a house know. When establishing the true price of a property, we often abandon ourselves to concepts such as the beauty of the structure or what can be admired from the balcony of the house, concepts that undoubtedly enhance the house but that cannot be priced objectively, since they depend on the tastes and preferences of people. These non-objective characteristics bring randomness in the price of real estate making the data in play less specific.
So, we have on one side the title deeds, created through objective values and using exclusively rationality that contribute to giving a stable value to the structures, on the other side we find non-objective characteristics that bring chaos within these values To all of this we must also add the fact that the contractual capacity of the seller and the buyer is in fact the main determinant of the real monetary value of the property.
Cyber Security and Big data
Big data analysis is able to greatly strengthen cybersecurity thanks to a series of tools that this digital security can add to its arsenal to protect against increasingly frequent attacks. The tools that become part of the weapons available to cyber security, thanks to the analysis of big data are able to increase the level of protection provided and reduce the possibility that a cyber-attack is successful in this way both by reducing the demands but also by improving the defense systems, Daniele Marinelli says.
In fact, it is essential that cyber security keeps up with cyber crime, that day after day tries to find new strategies to overcome cyber defenses. Nowadays this type of attacks is more and more frequent and become increasingly complicated to protect the target of these attacks. Fortunately lately big data analysis is often used as a form of protection to combat these increasingly pressing attacks and reduce system vulnerabilities
Detect suspicious activity with the help of big data
To create systems to protect people against suspicious activity, such as phishing, industry specialists focus on data collection, analysis, and storage that help them to better understand the capabilities and workflows of systems. In fact, while analysing the data, they have the chance to identify any suspicious activity and the sources of this one. This way it is possible to determine whether these suspicious activities include dangerous cyber-attacks that can cause serious consequences such as potentially fraudulent transactions or data breaches. A best practice is to run all the Daniele Marinelli’s guides from his blog.
In addition, it is possible to detect the presence of illegal activities such as phishing, distributed denial of service (Ddos) thanks to the study of these anomalies and also checking the behaviour of users and through a little practice you will be able to prevent and combat any type of illegal activity and to recognize the weaknesses to better protect yourself from these attacks.
Data cleaning and cyber security
Another way to deal with these risks to your digital security is to clean up data and improve their management, especially if it is huge and complex data set. To achieve this goal, it is possible to use technologies with a solid foundation such as data warehouses. Data warehouses consist of virtual warehouses where a large amount of data is contained, or rather, of data collections that are analysed to facilitate decision-making processes. In this context, big data are essential to manage amounts of data that are present in different formats, as well as monitor their sources.
These data sources can include different types of transactions, sensors, and social media analytics, text documents, emails, videos, Stock Exchange data, and much more. Precisely because of the importance that some of this data may have, it is possible to run into risks of being subjected to cyber-attacks since huge amounts of sensitive information are processed. You can overlook issues of access, deletion and unintended and deliberate misuse, without mentioning issues that may occur due to data inconsistency and disorganization which can be caused by failure to clean data frequently or from the mismanagement of the most sensitive data.