Welcome to the next installment of the "Big Data for Business" series, in which we deal with the growing popularity of Big Data solutions in various branches of business.
This entire series aims to make readers aware of how much Big Data is needed and how popular it is becoming in the modern world. In an era in which information, and thus data, has become one of the most important fuels for business development, solutions in the field of management, analysis, storage and use of data have become indispensable.
Before we get into today's text, we encourage you to visit the previous parts of the series if you haven't already, where you can read about the various fields in which Big Data solutions have been extremely useful:
Today we're going to cover what Big Data solutions bring to science - or more precisely, to research. Like all other aspects of the modern world, research has undergone a significant transformation due to the amount and availability of data. But that's not all - the once laborious data analysis process has changed its face thanks to modern Big Data solutions.
Big Data in Scientific Research - possibilities
Why is Big Data so interesting for scientific research? The last few decades have seen a rapid development in data generation capabilities, and research is an area that uses this at almost every step. From data collection and analysis to conclusion. After all, a data-driven approach, so widely used now in the context of talks about Big Data solutions, is the basis of all sciences.
More and more data is becoming available to the public, movements such as Open Data are becoming more and more popular - scientists have broad access to global and local data repositories that they used to only dream of. Those diverse sources of data are helping not only to solve problems but have the power to anticipate the future. Due to wide access to data, the conducted research is much more coherent - and of course, much faster. It is a common view that big data and tools for managing it open up completely new possibilities in scientific research in all areas where data counts.
As you can see in the attached picture, Big Data tools have completely changed the face of scientific research - taking into account such powerful computational possibilities, the possibility of creating a scientific hypothesis disappears. It still works, of course, but because testing its validity is so quick, it doesn't need to be formulated so precisely. Instead of just one study on data, with the current possibilities, dozens of them can be carried out at the same time, refining the hypothesis during the research.
Of course, as in any other case, the data alone is not enough.rst of all, the data should be stored, which requires and will require more and more efficient cloud solutions - because cloud solutions will be the only solution. Not only is data maintenance cheaper using Cloud, but integration with other tools allow for faster data preparation and management. Let's focus on data preparation for a moment because in the case of scientific research, it is probably even more important than elsewhere. Removing erroneous or irrelevant data or noise is the first issue, while the second is testing their suitability. One of the problems encountered when conducting research with data from various sources is the question of its age.
Many open repositories may contain obsolete data or data whose newer equivalents are already available elsewhere, and the database is simply not updated. Let us remind you that in many cases, such as tracking the spread of the disease in real-time, without the latest information, any research or attempt to predict further incidents by analyzing situational models is doomed to fail. In addition, it should be remembered that the data may appear in different formats from different sources, which may also cause erroneous readings and result in failure of research.
Big Data Technology and Statistic
Statistical models are one of the basic research tools in many fields of research. It is here that the broadly understood Big Data solutions have their enormous power. The processing time of such models is reduced so significantly that it has become a game-changer in research. With Big Data tools to help you manage your data, the place for traditional statistical methods is irretrievable. Nowadays, software such as Python, R (open-sourced) or MatLab and Statista are the main tools that can work with huge volumes of data. One interesting example is astronomy. Advanced solutions, incl. in the field of Machine Learning, allow us to process it and suggest interpretations of incredibly large amounts of data generated daily by global telescopes.
This comes at a price, as already working on such models requires statisticians not only to know the mathematical principles that enable them to work on them, but also to be proficient in computer engineering, AI and ML issues.
Big Data as the future of Scientific Research
Is Big Data the future of scientific research? Without a doubt. Is this a problematic issue? Definitely. Problems could be with data repositories that are obsolete, or with access to data, or with the skills that should be used to conduct statistical research on tools that require technological skills. What is certain, however, is that Big Data solutions are the future of scientific research. There are, of course, and there will be problems, but it is still a matter of further adapting individual domains and data management tools to the needs of research.
Want to know more about how working with massive amounts of data can support research? Or maybe you already have a project that needs the support of Big Data? Contact us and find out how we can help.
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1 March 2022
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