or human resources, but nowadays digital content is also the building block for the other transformations that we are seeing.
With the advent of network computing, the Internet, and later smartphones, we moved to the second stage of the digital transformation, which allows more people to access the digitized information. The value here lies in how information flows faster from A to B. This can eliminate small inconveniences in life, such as having to call for a taxi, by using a service like Uber instead, or it can help with providing lifesaving real-time information, as in the case of natural catastrophe warning apps. It also means that information can be fed back in real time, as when Google Maps uses the travel speed of smartphones to estimate traffic flow on streets. 5G and the proliferation of blockchain technology will further accelerate this stage.
Another reason that digital information can make our lives better, and this is the third stage, is that we can generate additional insights from the stored and shared information. This has been around for a while in the form of business intelligence (BI) — basically ever since we had databases. But several factors, including the rise of cloud computing and the general increase in computing power, have led to the impressive developments in the field of artificial intelligence (AI) that we have seen over recent years. With quantum computing, which is on the horizon to become widely available, this stage will explode.
While these different technologies emerged more or less sequentially, which is why I call them stages (see Figure 5.1), we are now in a situation where technological advances are happening at each level. Further, as already mentioned, innovation in AI and in different network technologies leads to ever more content becoming digitized, which in turn can be accessed and mined for new information, meaning that we see self-reinforcing loops between the different building blocks. This explains the rapid acceleration of the digital transformation that we are currently witnessing.
Alexander: What role does Big Data play in this transformation, or is that just a buzzword?
Florian: Big Data is a real paradigm shift. Whereas in the past we had to rely on small samples to make inferences about the wider population, in the statistical sense of the word, in many cases we now have data points about every single unit in the population. This could be individual shoppers who frequent an online store. Or it could be the fuel consumption of the vehicles in the fleet of a delivery company. This brings with it two advantages.
Figure 5.1 The three stages of the digital transformation
First, we have more data to play with when we want to make predictions, so data scientists can throw in everything but the kitchen sink to find interesting patterns. The sky is the limit for what businesses can do with big data. I have a device under my mattress that can provide me information about my sleep quality as well as my risk for sleep apnea. Although it costs less than EUR 100, it can approximate the results one would get in a sleep lab using an algorithm that was probably trained on a large set of medical data.
This device also illustrates the second advantage to Big Data, namely, that individual units can be identified, and tailor-made action can be taken. We are all familiar with applications of this in our daily lives, such as when we get personalized movie recommendations from Netflix. But there is still huge potential for society on many other fronts. In the future, just as my sleep analyzer does, many of our devices will notify us when our personal data deviates from the norm. Researchers are already working on smart toilets that can automatically analyze your urine!
Alexander: Do you see every country and industry moving through these stages as you described them?
Florian: A lot of what I described is not that new: machine learning, Big Data, Internet-connected devices, and so on have been around for a number of years already. However, there are huge differences in the adoption of these technologies across countries and industries, but also within them.
I went to a regional newspaper in Germany to give a software training. They had neither laptops nor guest WiFi. Instead, we had to use a training room in the basement that reminded me of the computer lab in my high school 30 years earlier. On the other hand, when I visited the data journalism team of the newspaper Die Zeit, I felt like I was on the Google campus. They had top-notch equipment, and they were hosting data science meetups in a room complete with beanbags and a beer-filled fridge. They are producing cutting-edge data journalism.
No matter what the starting point is, we are now seeing a wider adoption of digital technologies, because these technologies are becoming commoditized, meaning they become more accessible and easier to implement.
Especially here in Europe, concerns about data privacy have held us back in many regards. Perhaps we have good reasons to be skeptical; we Germans are all too aware how authoritarian regimes can use information about their citizens for total surveillance. Nonetheless, public acceptance of technology that relies on private data is slowly increasing. I am actually amazed that Germany put out a coronavirus warning app that lets you know when you were in the vicinity of someone who later tested positive for COVID-19.
Alexander: So where do you see this going in the future? In what fields do you see the biggest changes?
Florian: All sectors and industries will be affected, because most business processes involve moving information around.
The most obvious ones are sectors such as banking that already have gone through the first stage of the transformation — bank account records are already nothing but 0s and 1s. With the advent of so-called robo-advisor apps and other fintech innovations, we now see second- and third-stage technologies being used to democratize investment products that in the past were accessible only to the rich, who could afford their own private wealth managers.
But healthcare is where I see the biggest potential for society, both in medical research and in clinical practice.
Diagnosing a patient is, after all, just a pattern recognition task: a doctor considers your symptoms, lab results, MRI images, and so on, and compares them to what illnesses they have learned about and encountered in their careers so far. But medical professionals, like all humans, are affected by different behavioral biases, such as recency bias, which can lead to unfortunate cases of misdiagnosis. Further, consider the fact that, in the EU alone, 30 million people are estimated to suffer from one of more than 6,000 different rare diseases — and those are just the ones that we know about. No doctor could possibly learn about all these diseases in medical school.
So, one could imagine that computers might be able to assist doctors in diagnosing patients by comparing their health records to the different patterns of diseases in a database. Already today, there are studies showing that computers can be better than humans at spotting potential tumors in mammograms.
With machine learning, researchers are currently studying how innovative types of medical data, including, for example, the gut biome composition, relate to different illnesses, meaning that we will have more noninvasive tests that can catch diseases early, before they cause any symptoms.
Also, in a future where anonymized digital patient records of whole populations are available to scientists, they will be able to slice and dice the data in many ways to find more relevant insights for different subgroups of the population. Because they are typically limited in size, traditional clinical trials often gloss over differences between different age groups, ethnic groups, or groups of people with different comorbidities.
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