Identifying needs and digital technological responses

Technologies permeate almost all human activity, which puts pressure on some differentiation. On the one hand, it is possible to see quite a few activities that try to live without technology or do things in an “older" (historicizing) way. Undoubtedly, part of digital competencies is also to search for and find areas in which it is not adequate to use and work with technology. It is appropriate to use other tools or learn more on the direct human experience for various reasons. This distinction is about the effectiveness of solving multiple problems and in the social context, where it is still possible to meet many people who have a reserved or negative relationship with technology.       

The second level of difference is the ability to think about what activities of human labor could be replaced by technology. Tony Buzan has repeatedly written in his books that the 21st century will be a century of the brain - it will be necessary to look for areas in which creative and critical human thinking can compete with artificial intelligence or think critically about which areas would be practical and functional to use it. 

These are not primarily banal situations, such as replacing sending a letter by e-mail or using a word processor to write text instead of a typewriter. As a topic focused on problem-solving, it is clearly emphasized that what one should solve with technology is a much more complex area of ​​problems.  

An example can be data journalism - the classic journalistic work with sources was supplemented by data analysis and visualization. Thus, in the first step, the journalist defines the topic, obtains relevant data for it, analyzes it using an adequate tool, and visualizes it. He then writes a story about the results processed in this way. We emphasize that ordinary journalistic activity is different - data serve either as a simple starting point, are set in a simple context, or serve as a basis for an article when qualitative. In this respect, data journalism defines an entirely new approach to work, changing the competencies necessary to be a journalist.

The example above shows how even a relatively simple profession of journalists can undergo dynamic development if it can identify the challenges posed by modern technology. And it is precisely this aspect that is part of the competence to look for technology-related problems. It is about developing the ability to observe the world and think about the possibilities of technology to solve the various issues facing man, even if what we said above may not be the only answer to problems. However, their sound knowledge and ability to think about them are undoubtedly practical work in the information society.   

Information Society carries what Robert Reich called the symbolic analysts, i.e. persons who are engaged in that world are looking for structure, which can then be analyzed and manipulated. They are information specialists in the broadest sense - from stockbrokers to teachers to doctors. They all have to work with information, and in their practice, there are countless problems for which it is possible (and appropriate) to use technological solutions. 

There is another phenomenon that cannot be forgotten - the information society is fundamentally transforming the labor market so that entirely new positions are being created. Some old ones are either completely disappearing or are changing significantly. The ability to effectively use digital technologies in various areas of social life thus represents one of the works or economic (but in the future also social) competencies. The challenge of defining one's profession or profession is not only the “pioneers" of new disciplines in the information society but a large part of the general population.       

We have already given today's companies various nicknames - information, knowledge, learning, platform, consumer, etc. Sometimes it is possible to come across the statement that we live in a post-factual period, i.e. in a world where it is not the actual information that matters but more impressions or rapidly disseminated announcements via social networks. The truth seems to have become something that no one cares too much about. Jan Sokol and Jaroslav Peregrin's same time point (p. 159) that the truth is necessary for any human interaction - language is based on truth formulas, computers handle logical expressions, and communication stands on a specific truth base. If we don't believe what the other person is saying, we can't communicate with them. In his definition of man, Friedrich Nietzsche says that he is an animal that can promise. If there is no discourse of truth in society, then there is no promise, and society becomes inhuman, animalistic.     

We would like to step out of this somewhat negativistic position and point out that the nickname that we have not given today is data. Indeed, the present is sometimes referred to as the time. In the context of the above, it is crucial that if the truth is not a generally shared value, the data will also make no sense. At the same time, it is one of the vital trade commodities.    

The phrase data period contains several exciting aspects. First of all, the production of data is growing enormously. Science today can create larger data files than it can process in a reasonable amount of time. Examples are data from CERN, which usually waits two years for processing, or astronomical data, which we cannot fully process (there are too many possible research topics). We will probably never be able to do so. With improving devices, data production is growing extremely fast resulting in an information explosion.

This fact then raises three possible models of solutions. The first to apply to CERN is open cooperation. Various workplaces in the world can participate in the grid network and participate in their analysis and processing. This distribution of computing power is an exciting phenomenon used, for example, by the well-known project Seti @ Home, which searched for extraterrestrial civilizations so that ordinary users' computers analyzed individual parts of the signal. Thus, a complex problem can be broken down into several parts, which can then be solved by individual users' computers or smaller servers.  

The second option, which is widely used in astronomical data, is openness. Anyone who wants can go to the Simbad database and use the available data for their scientific work. Although valuable data is in itself, those that are obtained from public funds should be public. Thanks to this, a person who is not paid by the university or from too poor a field for his institution to actively participate in space research can also work on good data. 

A specific form of this openness is civic science, in which citizens do not primarily process data but acquire it. For example, they can map the forest's biodiversity in which they spend their holidays or monitor and record dialects of larks. Researchers are taking advantage of the fact that people can create scientific data that they would otherwise obtain only at a very costly or long time while increasing a particular scientific engagement with the general public. 

The third model can be described as protectionism, where an institution or individual retains data for their use. This approach is dominant in today's society, there is a lack of a more developed culture of sharing, a defined data market, or the ability to think appropriately.

During his time at IBM, Dave Snowden created a model that describes problems or situations based on the strength of the causal relationship between cause and effect—and he named it Cynefin. The strength of causal relationships is perceived on an epistemic level—that is, how well we can use the available information to predict how a situation will unfold. It is a management tool, so it is not just about prediction, but primarily about carefully considering a strategy for responses or the tools available in a given situation. The author describes five domains, the fifth of which is labeled “Disorder”—that is, phenomena that are as yet misunderstood and unclassified. In such a situation, the manager’s goal is to describe the phenomenon and classify it appropriately. For us, this is not about describing managerial practices, but about phenomena that we can categorize in this way and systematically investigate (not only) through technology.

Clear – the first group of situations is straightforward; there are unambiguous procedures for them that can often be easily algorithmized. This means there is a direct link between cause and effect: an invoice arrives and is paid; an employee arrives at and leaves work, and their working hours are calculated; points earned for individual tasks are totaled and converted into a grade. The advantage of these situations—aside from the fact that they are (partially) easy to algorithmize—is that it is easy to monitor the outputs and how the entire system of activities functions.

Complicated—the second group consists of complex situations where the solution may not be immediately apparent, and finding it typically involves some form of diagnostics. The relationship between causes and effects can be uncovered precisely by testing various possibilities and searching for a solution that works well. For example, when an internet connection is down, there are a relatively limited number of reasons and standard ways to resolve the issue. It is also highly advantageous to address these situations technologically, and typically a set of AI agents organized in a hierarchical structure will be used, where a selected resolution procedure is triggered based on the diagnosis performed.