But research is historical, isn’t it? It isn’t really up to date, is it?
This couldn’t be further from the truth.
While it is true that research often relies on historic data and observations, it is far from being solely retrospective. It is actually a dynamic, continually updating field, that frequently sets the trends and shapes the future.
Research often involves identifying and understanding trends. By looking at data over time, researchers can discern patterns that indicate a trend. For instance, in health research, studying the incidence of a disease over a period of time could show if it is increasing, decreasing or staying the same. This is essential for public health planning and resource allocation.
Research is not just about documenting what has happened, but also about predicting what will happen. By understanding the variables that influence an outcome, researchers can create predictive models that forecast future scenarios. A clear example of this is climate change research. By studying past weather patterns and greenhouse gas emissions, scientists can predict future climate conditions.
Research can also lead to technological and scientific breakthroughs, shaping the future in unforeseen ways. For instance, research in quantum computing or artificial intelligence can lead to new technologies that revolutionise the way we live and work.
As for references, there are numerous examples. In “Trends in Infectious Diseases: Implications for Public Health Policy” by Salinsky (2006), the author used historical data to understand and project trends in infectious diseases. Similarly, in “Predicting Future Climate Change: A Primer” by Collins et al. (2018) from the Intergovernmental Panel on Climate Change, the authors showed how research has been used to predict future climate change scenarios. Finally, in “The Future of Artificial Intelligence – Opportunities and Challenges” by Russell & Norvig (2021), the authors discussed how research is actively shaping our future, using AI as an example.
While research does build upon historical data, it is by no means stuck in the past. It is often dynamic and forward-looking identifying trends, making predictions and playing a crucial role in shaping the future.
Additionally, most of the studies we base our briefings on are right up-to-date, being published in the last few days or weeks.
Here are some examples of research with strong predictive value and which has shown future trends:
- The work done by the Institute for Health Metrics and Evaluation (IHME) on COVID-19 is an excellent example. They developed a model to predict the spread of the virus, the demand for hospital services and the number of deaths, with adjustments for different public health interventions. These predictive models were regularly updated with new data, allowing for increasingly accurate predictions and informing public policy.
- The work of economists Carmen Reinhart and Kenneth Rogoff, as presented in their book “This Time Is Different: Eight Centuries of Financial Folly”, is an example of how economic research can predict future trends. By analysing financial crises over eight centuries, they identified patterns and warning signs that could help predict future economic downturns[i].
- A study on employee turnover by Griffeth, Hom and Gaertner (2000)[ii], “A Meta-Analytic Review of Predictors of Job Turnover”, analysed relationships between factors (such as job satisfaction, organisational commitment, job search behaviour and the availability of alternatives) and the likelihood of an employee’s leaving their job. This research allows organisations to proactively predict and address high levels of job turnover issue.
- IBM’s Smarter Workforce initiative and Google’s Project Oxygen are using data-driven HR strategies to predict future employee behaviour and improve organisational performance, including turnover, performance and recruitment success.
Many studies are both up-to-date and predictive.
[i] Reinhart, C. M., & Rogoff, K. S. (2009). This time is different. In This Time Is Different. princeton university press.
[ii] Griffeth, R. W., Hom, P. W., & Gaertner, S. (2000). A meta-analysis of antecedents and correlates of employee turnover: Update, moderator tests, and research implications for the next millennium. Journal of management, 26(3), 463-488.
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