Facts don’t change the world, people do. We are creating a world dominated more and more by data and technology, and where fewer and fewer know how to use it. Companies sit on top of vast amounts of data and so do governments, NGOs, and academia. But it is not the data, it is what we do with it.
Historically, we started having scientists seeking and producing most of the data to do research, to publish papers, to understand. Understanding has been the core of the scientific revolution, guided by the “free play of the free intellect.” Roughly a decade ago, the private sector, especially in Silicon Valley, wanted help making decisions, more than it wanted understanding and research papers. It wanted, for example, to put thousands of small variations of websites to millions of people, and see what combinations work. They were not really interested in understanding why a particular option worked, but on optimizing. On profiling customers. This is why a new breed of scientist—experts in processing data—was born: the data scientists. We could not have enough, so more and more people became data scientists. Today data science is one of the most demanded and rapidly evolving job markets. Yet, it has no formal training, curricula, or even an agreed-upon definition. The skills and tools of this job, heavily subsidized by the private sector’s thirst for more results, and the fast cadence of technology, are creating ever better, more complex and powerful tools.
An increasing number of people start to see the problem with this trend focusing on better decisions by mostly getting more data and data-driven decisions. Before, we had scientists in knowledge silos, working on creating better skills and tools in their quest for understanding. Now we have data scientists, creating even better skills and tools in decision making. Data can tell us that a certain genetic mutation in our genes predicts more health care complications and extra cost, though morals (and history) warn us against selectively and genetically breeding and optimizing our kids. Data can tell us that a robot can perform heart surgery more accurately than a person, but few would allow being operated on by only autonomous robots. Data can tell us exactly what message each voter should get so they vote a particular political candidate, but manipulating voters in a democracy this way has poorly understood consequences we only now start to understand… Data is what scientists are trained to manage and understand, and data is what we have exponentially more of. Therefore there is an increasing potential and responsibility for scientists to both step in and step out. Step in to engage not only in academics, but also with the consequences and actions. Step out to incorporate and work with experts in other fields and in other realities. For far too long, scientists have built ivory towers where they live in their segmented field of expertise.
Getting a grasp of the concept of impact science has personally been a long path. When I left academia, it was a difficult choice, and remained for many years a challenging time of uncertainty whether I had done the right thing or not. Looking back at those times I wish I knew that there is a professionally viable, market-growing, and extremely interesting world of science outside academia. I wish I knew leaving academia doesn’t mean giving up the “top choice” or that doing so did not mean that I was not good enough. I felt, and was told by a famous political scientist, that society had “invested in me” with a lot of specialized education and I was not using it as I should. That from a public policy perspective, I was a wasted investment. Beyond that, I had no idea what else I could do, where to do it, or how to make that transition happen. This made me feel anxious and frustrated. Moreover, the longer a scientist remains in academia, the harder this transition is. The cost of such transition away from research is facing uncertain rules of your professional value. Losing the hard-earned status one has achieved despite the hardships of academia. These can be very hard moments. I can count more than a handful of colleagues who once were good or really good academics, and faced with this change, they turned into a spiral of medicated depressions, clinical anxiety, and unmet expectations. In the more severe cases, it also meant periods of professional help at mental health institutions or even struggling with suicidal thoughts. So alienating is the way that academia defines success as a scientist. I have since discovered many more people that craved for this change, and found other ways to be a successful scientist while leaving academia, by being what we would call impact scientists.
There are two main reasons I wrote this book. One is that I believe impact science is a new concept that is both needed and unknown, perhaps even undefined. I have used the stories of this book to explore this space and the need of more impact science. On both sides, when it is needed and successfully used, and when it could have been used and didn’t, or it failed when applied. That is the first part of this book.
The second reason for writing this book is that I wish I could have read back when I left academia. The last section of the book is meant for all those who have an academic background but feel something is missing. They still want to be scientists but haven’t yet found their place. Or for their friends and family who want to understand the reason, and path, for such an uncommon and dramatic change from a “normal” scientist. These people curious about impact science wish to engage more with the world beyond academic or public outreach articles. They are tired of the pressures and uncertainties of the academic life and cycle of temporal postdocs, moving countries every two years or so, but they truly don´t know how to even think of alternatives.
Thank you for the interest reading this far!
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