Big Data is an enormously powerful tool. Like every tool, it can be used in different ways. A hammer can drive a nail into a wall (useful) or crack a skull (very bad). The challenge is to establish points of orientation and draw effective lines. This is probably the shortest summary of our Investment Board with our research partners.
The digitisation of more and more extensive areas of life has produced an exponential growth in data over the past decades. Modern algorithms have made them useful. The resulting opportunities are sizeable. In healthcare, for example, it has been possible to predict epidemics for a while; now it is possible to predict complications for very premature babies thanks to collected data prior to their occurrence and thus to increase their chance of survival drastically.
The use of such critical information such as biological data becomes problematic once they do not serve their primary uses anymore (i.e. in this case, therapeutic progress), but are used in completely other, potentially opposed fields with new links and algorithms. The mobility patterns of mobile phones can be used to improve the network or to turn the user into a transparent person and sell the findings, as has happened in the USA.
This leads to two fundamental requirements: the sensitisation of the individual; and the possibility to control how, where, and by whom data are collected and processed, and the transparency of the algorithms that are being employed. Without these two elements, there is an imbalance which annuls any effort to impose a corrective on the use of data. If the information used and the way it is correlated are unknown, an informed appeal against the resulting, possibly false or discriminatory decisions is impossible.
Regulatory frameworks such as the GDPR (General Data Protection Regulation) or the imminent e-privacy regulation by the EU are important steps towards a balanced practice. At the same time, there are still 50 countries that have no data protection laws at all. In the worst case, the government itself uses information for surveillance or discriminatory purposes against certain demographic groups: in China, the Uyghur minority has to install surveillance software on their mobile phones and car trackers. The social scoring, currently in beta phase in two provinces, is to be rolled out across all of China and will severely curtail citizens with bad ratings in their options.
Of course, Big Data applications do not only abet such dystopian scenarios, but they also serve the public good. The United Nations have created suitable Big Data solutions for each of their 17 goals for sustainable development. Smart Cities are an example. Higher levels of interconnectedness in traffic and energy systems can produce enormous efficiency gains and lead to a decrease in energy consumption and pollution. In South Africa, the citizens of Cape Town can monitor water consumption and the required savings in their district online and in real time. This has contributed significantly to mitigating the water crisis that has burdened the Western Cape province since 2015.
The possibility to employ resources in the industrial or agricultural sector more efficiently on the basis of algorithms facilitates efficiency gains that, from a sustainability perspective, are indispensable. However, if the data and algorithms are not publicly accessible, this can easily lead to a rising degree of market concentration due to the crowding-out of smaller competitors and to higher barriers to entry for new competitors.
Due to this economic relevance in particular, our research partners point out that we are in dire need of best-in-class approaches as to how to handle the collected data. Companies limit their efforts to complying with existing law or just react to external pressure from customers or investors. At the same time, the analysts of our partners are strongly advising against being content with the expression of unilateral commitments. The issue is simply too important. This results in a clear responsibility not only of sustainable investors such as EAM, but also of every individual to argue in favour of the responsible use of their data.
Prognoses are no reliable indicator for future performance.