How Our Future is Being Shaped by Data Science and Applied Mathematics

Features

  • Author: Liam Critchley
  • Date: 01 Jul 2019
  • Copyright: whiteMocca/Shutterstock

The amount of data in today’s digital climate is vast and has been brought about by applied mathematics and data science working in unison. Reports suggest that over 1 billion gigabytes of data are generated everyday around the globe. Therefore, how this data is sent, analysed, and stored is crucial for many people, and advances in computer, sensor, network, storage and processing technologies are the reason why we can generate this amount of data every day. Aside from these fundamental functions, the fields of data science and applied mathematics have been adapted to solve modern-day real-world problems, and this need will only get greater as the world shifts to an automated world during the fourth industrial revolution. So, whilst these fields are currently helping many of today’s society, the reliance on data science and applied mathematics are only going to get greater as industry (and in turn, society) advances.

thumbnail image: How Our Future is Being Shaped by Data Science and Applied Mathematics

Many experts in the field have recently imparted their knowledge and experience in to how these two fields are impacting our lives and will continue to do so as the fields advance further. For example, Professor Mehrmann, from Technische Universität Berlin, Germany, has stated that the field of applied mathematics has already been responsible for the success of many application areas, from biomedicine to finance. Professor Mehrmann went on to say that the implementation of mathematical models to analyse data is what has helped the fields to progress, and that there are no risks going forward from a technical standpoint, but ethical issues of how data is used may come in to play more and more. As far as the future outlook goes, Professor Mehrmann reckons the application areas which will benefit the most are the biomedical industries, as a lot of future medicines and individually tailored procedures could be highly reliant on having access to a lot of data.

Another expert which has been interviewed recently is Professor Steven Marron, from the University of North Carolina, USA. Leading on from the previous statements, Professor Marron is involved with cancer research and has also stated that data science is crucial for determining the many factors in biomedical research. He also states that the application of data science in biomedical processes is one of the great application achievements thus far, as it has enabled new treatments to be realised clinically. However, Professor Marron thinks that significant future advances could come in other areas, such as self-driving cars, recognition software, and in providing more accurate error rate calculations. Professor Marrons’ thoughts are also in agreement with Professor Mehrmann’s and have also stated that ethical and privacy issues will need to be addressed.

The knowledge of various experts has also been sought through a variety of panel discussions and interviews. With respect to the future of the industry in the next few years, various members of these panels have mentioned a wide range of areas where data science could make a big difference, and these include image recognition in medical imaging, dealing with climate change, analytics in Industry 4.0, helping people to make better informed health and lifestyle choices, and modelling molecular systems (using molecular dynamics). Much like the two professors above, almost all the participants of these panels suggested one biomedical application where data science could have a big impact in the next few years. So, it looks as though the overall view from the experts is that the biomedical field is the one to look out for in the next 4-5 years. Again, with the challenges ahead, some of the experts on the panels have flagged up that ethics is a key challenge moving forward, but another challenge could include trying to bring the researchers from both data science and applied mathematics fields closer together to collaborate more effectively.

Many people are also depicting trends, based off information from various companies, of how the applications involving data science and applied mathematics could impact their business. From these published trends, marketing and sales, and productions/operations, are the two areas where most respondents have said they will be implementing new data science technologies, with machine learning and advanced analytics being the two most common methods to be introduced over the coming year. Of the companies surveyed, the biggest responses regarding where they think that new data technologies will be of benefit to their business in 2019, are in business intelligence and KPI analysis, with predictive maintenance, process optimisation, and automation being not too far behind.

Other surveys that asked experts in both industry and academia, as well as those who are freelance and work in the public sector, look more towards the future of the fields, rather than the business aspects. From the respondents, there was an overwhelming majority of people who suggested that machine learning will be the technology to look out for over the next 4-5 years compared to other technologies. Similarly, the respondents showed similar response to the interviewed experts and panellists mentioned above, in that a significant majority think that data science and applied mathematics will make the most difference to medical, biotech and health care sectors in the next 4-5 years, and that privacy and ethical issues are going to be the biggest challenges in the next few years.

So, overall, there are some common themes which depict that the biomedical sectors are thought to be the area that data science and applied mathematics will have the greatest impact in the near future, although automation and robotics comes up a lot as well. Similarly, it appears that in an age where the amount of data we create and store is only going to get greater, the biggest challenges facing the future of data capturing methods is how this data is going to be stored and used, and whether it will be used ethically or not.

Reference:

Mathesia Outlook 2019: https://mathesia.com/home/Mathesia_Outlook_2019.pdf


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