The world we live in is increasingly driven by data: real-time information, updates, notifications have become an integral part of our daily life to such an extent that it is no longer possible to do without them. Accessing and consuming this data has become progressively more and more important, and this has led to a progressive increase in the data available, so much so that it is impressive to compare the volume of data to which it was possible to have access only a decade ago compared to the amount of information we are bombarded with today. This is more and more important for professionals who have to comply with this huge data quantity especially in real-time critical situation such as the cyber-attacks where the Cyber-Trust projects operates. This evolution is also reflected in the way in which the data are presented: more information also translates into more types of data, and this is reflected in the way in which the data are offered to end users who, in our case have differente IT competences as roles (e.g. Law Enforcement Agents, ISP, Smart Home Owners).
It is therefore necessary to carefully evaluate and plan the methods and strategies to be adopted before actually proposing the data to users, otherwise there is the risk of confusing those who have to read the information, or worse, of not showing them all in their entirety.
During the planning phase, some aspects are crucial:
- data source: it is important to understand where the data comes from and in what format it is presented. The relevance of the data source is decisive above all from the point of view of their management: usage and dissemination policies can change according to who releases the data, and this could significantly affect the possible data administration strategies. In addition to this, the level of detail of the data must also be evaluated: the same data (information about CyberSecurity for example) may differ according to the agent who issues it. Understanding the level of detail of the data (and the consequent level of knowledge of the subject required to understand it) is important to understand if the information is directly usable to the users envisaged by the data visualization.
- type of data: a correct understanding of the type of data to be presented is essential to then know how to show them to the end user. Correctly distinguishing and classifying the available data (whether qualitative or quantitative, continuous or discrete, orderable or not) allows you to select more precisely a range of possible data-visualization methods to be adopted also considering the specific users.
- data format: different sources and different types of data often translate into different data formats. It is obviously strategic to understand how the data are usable in order to plan their correct reading and presentation. This does not only mean decoding the format in which they arrive (JSON, CSV, XML), but also their specific internal structure.
- update frequency: having updated data, especially for certain types, is of fundamental importance. For this reason, knowing the data refresh rates is extremely useful for planning the updating of user interfaces as well. The evaluation of “how often” to update the interfaces does not necessarily have to respect the data update frequency, but you can “settle” for a lower update frequency, mainly based on the needs of users.
- timing for obtaining the data: the quantity of data, their format and their updating frequency also entail different times of use of the data. Clearly, the more data there are, the more time is needed to obtain and show them in the interfaces, and this also affects the way in which the data is displayed.
All evaluations must then be made according to the final objective, that is to present the data in the best possible way to the user. To do this, it is necessary to be clear about the main goals, the three fundamental requirements to be respected:
- clarity: the data must be presented as clearly as possible, also taking advantage of different presentation methods (charts and tables for example). The readability of the data by the user is fundamental, as a badly presented data, in an unclear way, strongly affects the passage of information.
- completeness: there must be no loss of information when the data is displayed, or at least the information core of these must be maintained. One of the tasks of the data manager is precisely to study pruning strategies suitable for the data, and represent them effectively, in order to balance readability and information content.
- speed: the data-retrieving that the visualization of the data must be balanced in order to allow a display as fast as possible, in order to present the data to the user in a fluid way, ensuring that these are always updated with respect source, especially during-real time attacks.
In conclusion, in the present world, and mostly in mission-critical systems the task of effective representation of a huge quantity of data, changing fast and in real-time is very important. It is no longer possible to limit them to presenting the data without first evaluating them in a precise and careful manner, but it is necessary to study both the data and the ways in which they are proposed in depth.
In the process that leads to data-visualization, the planning phase has assumed a role of significant importance, which goes beyond the simple reception and display, but moves towards a correct evaluation of the methods and solutions to be adopted for data-representation, in in order to maximize the use of information according to the role of the end user.