Technology and Beyond

Let's connect what we've learned to the data lifecycle.

We have seen that data about us can be generated in many different situations, and shared through the Internet, the Internet of Things (i.e. in the context of smart cities), and social media. Many devices around us are basically computers—not just PCs and servers, but also mobile phones, digital cameras, smart TVs, watches, speakers, and so on.

The generated data can then be collected and stored, at a lower level as bits on a medium such as a hard drive or SSD—but also at a higher level, like in a database or text file.

Once the data has been collected and stored, it can be processed—for example, it may need to be filtered, cleaned or transformed, something that may be particularly relevant for unstructured data. In addition, processing also takes place when the data is used through other applications or computer programs.

Quite often, we want to analyse the data, especially when it comes to big data. This can range from simple statistical analyses to more complicated analyses based on artificial intelligence and machine learning. Visualisation of analysis results can provide a better basis for actions and decisions, as it can make it easier to interpret the results. Making decisions based on big data, and analyses of big data, as we have seen, can be a challenge.

When the volumes of data become so extensive, it is extremely important to be aware of the security risks that come with it. Cybercriminals use a large number of tools and methods to get hold of data about you and your employer. The responsibility to protect ourselves and our surroundings from data attacks should be at the forefront of our consciousness.

Another important perspective, which we will look at, is that it is often data about ourselves that is generated, collected, stored, and so on—by others than ourselves or our company.