Drowned in Data
by Dr Asteris Apostolidis
It is a bit of a cliché to state that we live in the era of information, nevertheless 100% accurate, as recent technological advancements were remarkably fast. Just a few decades ago, the person owning the information was of key role; nowadays, the amount of information is so vast, that the extraction of the right information is the key point. And this point is valid in almost every domain: Media, politics, strategy, commerce, science and of course, technology.
So, what is the role of data in technology?
Keeping huge amounts of data is now cheap. The cost of storage is cheap, the cost of processing is cheap, networks are super-fast and the cloud made availability easier. As a result, every physical or virtual machine produces data, which are transferred and stored. But that’s the easy part.
The hardest part these days is the interpretation of any data into meaningful and tangible information. A machine (again, physical or virtual) can be equipped with thousands of sensors, transmitting a large stream of measurements in real time and describing its operating conditions. The challenge is to filter out anything non-useful, identify the key parameters and develop a method that processes and returns to the user only what really matters.
In a similar way, this is a great challenge on a system level too. The system may be a manufacturing facility, trying to optimise any processes taking place. Historical data for every station and every process may be available, but the way one utilises these information is what really matters. Another example may be a vehicle renting company, trying to optimise the use of their fleet, or a designer engineer, trying to identify among thousands the most efficient design. And so on.
In a pool of big data, data mining is what matters, as most data patterns are invisible to human eye. Machine learning, or even artificial intelligence are some tools that can help the machine to identify, or decide what may be of interest for the final user.
The technological world gets more and more complicated, increasingly relying on algorithms to take decisions. In one hand, we need to follow the stream and use everything the technology has to offer. This way, great numbers of man-hours can be saved. On the other hand, we should always keep in mind that the benefits of those savings should be spread to the society, not the few. And of course, always be aware of the dangers, as the artificial intelligence is something that may result to machine autonomisation. This is a prospect that many great scientists warn about, so we need to proceed with caution.