Issue link: https://maltatoday.uberflip.com/i/1478289
5.12.19 12 BUSINESS 8.9.2022 Alexiei Dingli Prof Alexiei Dingli is a Professor of AI at the University of Malta and has been conducting research and working in the field of AI for more than two decades, assisting different companies to implement AI solutions. He forms part of the Malta.AI task-force, set up by the Maltese government, aimed at making Malta one of the top AI countries in the world The end of data science as we know it? D ata science (DS) is a field of study which uses advanced analytical techniques to extract valuable in- formation from data. To do so, it utiliz- es Statistics, Artificial Intelligence (AI), Mathematics, and a bag load of other techniques. ese insights are then used by businesses for decision-making and strategic planning. Its roots can be traced back to the 80s, but DS gained massive popularity among many professionals in the last decade. With the proliferation of dig- ital transformation amongst different organizations, the availability of cheap processing power, and the development of powerful algorithms, DS became ac- cessible to almost anyone who needs it. However, in the past few years, it has become evident that Data Science is go- ing through a transformation process. First of all, people are drowning in data but thirsting for information. With the advancement of digitization and the proliferation of Internet-of-ings (IoT) devices, this is becoming increasingly true. Unfortunately, the tsunami of data has led Data Scientists to become data plumbers gathering all the data they can and sticking it into the system. But such an approach is not feasible and will become unmanageable in the long run because it will impact the system's scalability. One has to keep in mind that the wrong data can quickly turn into a liability. Furthermore, just plugging in data because it's available also goes against General Data Protection Regulations (GDPR) principles since Data Owners need to collect only the data they need for their task and nothing more. So Data Scientists need to move away from act- ing like data plumbers and become data concierges. ey have to guide people on the correct data that should be gath- ered, find the right insights and identify which of that data will drive meaningful outcomes while adhering to strict data governance principles. As you can see, their role is much more expanded than in the past, and so are their responsibil- ities. Second, big data is passe! Big data can be defined as data that is produced in large quantities, at high speed, and made up of different types of information. Of course, this doesn't mean that Big data is no longer critical, but a different kind of data is quickly gaining popularity, and with time, it will become more valuable than Big Data. Here we're referring to small data, whereby it is estimated that 70% of organizations will shift focus on small data in the coming years. e rea- soning behind this is evident; big data is not always available, and many ap- plications have no choice but to rely on small data. e medical domain is one such field, whereby biological infor- mation is challenging to collate. Apart from this, more is not always better. In fact, small data can, most of the time, be more insightful than massive data- sets. So it's not about the size but more about the quality of the data. However, we all know that small data has limita- tions too, which can be overcome using synthetic data. is kind of data has the same features and distribution as small data but is generated in a lab, thus making large datasets available even though there might be a small amount of real-world data. Such data has oth- er benefits too. Since it is created by a machine, synthetic data is not subject to privacy issues. at is why consultancy firms estimate that by 2030, synthetic data will become the most prevailing kind of data used in creating AI models. ird, the problem with DS is that af- ter analyzing various data streams, we are left with truckloads of insights we will have to process. Traditionally, this was done with Business Intelligence (BI) tools, but most processes are man- ual and, therefore, unfeasible on a large scale. e solution to this is to use aug- mented analytics, which uses AI tech- niques to help prepare and analyze the data. By employing Natural Language Processing algorithms, these insights are further translated from numbers to a human-readable form that is easily understandable by anyone. e system will not only manage snapshots of data but live streams, thus automatically alerting management and guiding them to make quick decisions. By doing so, the dependency on technical person- nel is reduced, processes are shortened, and decision-makers are given rapid ac- cess to all the required information. Fourth, the Data Scientist should not be relegated to a data photographer. When management is given a report, it is just a snapshot of the current sit- uation. But situations change rapidly, so information should be live as well. Because of this, a Data Scientist should become a Decision Designer delivering meaningful information at the right time. Automation is critical to achiev- ing this feat. We are all humans and have hundreds of things on our minds, so we need to enact processes that keep us abreast when our world changes. Furthermore, Data Scientists serve a horizontal role that permeates most of the departments within an organi- zation. us, they are privy to the big picture, so rather than deliver insights in silos, they should connect the dots and provide management with unified viewpoints. Fifth, Data Scientists need to be care- ful when it comes to governance. Due to internal politics, governance varies across diverse styles, from controlling, defensive, and risk-averse to an oppo- site approach on the other end. All this causes instability which is unfortunate because governance should be simply about deciding how to get things done correctly. us, Data Scientists should adopt adaptive governance, a flexible, agile decision-making process that al- lows fast responses while reducing risks. e world of DS is passing through rapid changes caused by innovations brought forth by AI. But it's also an ex- citing period because an organization can manage to achieve much more with fewer resources and in a shorter time. us, adopting these changes is es- sential, ensuring that the organization reaps all the benefits and places itself ahead of the pack.