Big Data is influencing a new generation of researchers. This new breed of scientists, are similar to those who came before them, as progress has always been based on empirical experimentation and observation. However, the difference lies in the amount of data available to the modern scientist. Today’s research is driven by massive, community generate data sets. To be successful, a modern researcher must be an expert in the field of statistics, computer programming, and software design. Collaboration is also becoming increasingly important, as data sets become larger, and different research methods become necessary.
The prototypical researcher in the modern era is a data-driven professional. Modern scientists and researchers have found the best results when applying high performance data-parallel statistical algorithms in order to analyze huge, community generated data sets. If one wishes to be successful with their research, they must be familiar with new statistical approaches as faster algorithms and better coding have given researchers the ability to scale models at previously unattainable levels. A successful researcher in the modern era uses the latest technology to push Big Data applications and uncover revolutionary scientific discoveries.
Scientific Software is central to new research methods. Traditional research methods are outdated, as data sets have become impossible to analyze without revolutionary software. Researchers have found new and innovative ways to confront ever-growing data sets with new software. Traditional academia has rejected the use of this software, as those who get published get funded, and creating this software does not often lead to publication. There is a need for well-organized, open, well documented, and well tested code within the scientific community. Without it, the reproducibility of experiments is nearly impossible; therefore theories cannot be tested again for validity. Scientists have identified this problem and have already begun to create new software to meet these criteria.
Perhaps the most important shift in research and academia brought on by Big Data is collaboration. The future of scientific progress depends on it, as it is required to answer research questions involving massive data sets. It would be nearly impossible for one researcher, especially with a background in something other than data analysis and software engineering, to properly analyze Big Data. Modern researchers must learn to work with software engineers, to maintain their data sets, and a statistician or someone who is familiar with machine learning to develop the proper methodology required to explore the data set. With the collaboration of various data experts, modern researches can ask the questions they need to, without worrying about an impossible large data set.
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