Sometimes it’s easy to get carried away and focus on data science too much, losing sight of the applications of it. Although this is something somewhat common in an academic setting (particularly in universities that don’t have any ties to the industry), it may happen in companies too. When this happens, it’s usually best to walk away, since data science without any real-world application can be problematic.
Data science and A.I. that’s geared towards data analytics, involve a lot of scientific methodologies, which are quite interesting on their own. This may urge someone to get lost in that aspect of the craft and neglect the application part, particularly the one where these methodologies are employed for solving real-world problems. That’s not to say that doing data science research is bad. Quite the contrary. However, when the research is without any application, focusing too much on the math side of things, it is bound to be a waste of resources (unless you are doing this as part of a research project, e.g. for a research center or a university, in which case this is expected). The reason is that data science is by definition an applied field, much like engineering. Particularly when it is undertaken by a company (e.g. a startup), it needs to be able to deliver something concrete, and more importantly, something useful.
It’s hard to over-estimate the value of this aspect of data science that has to do with the end-user. After all, this person is often the one paying the bills! Also, focusing on the application part of the craft enables something else too: the more practical implementation of the technologies developed and the inception of new methods that are more hands-on and therefore useful. This is one of the reasons that data science has veered away from Statistics, a field which is by its nature more theoretical and more math-y than applied Science. That’s also the main reason why data science involves a lot of programming, oftentimes building things from scratch, even if it’s simple scripts. That’s quite different than using an all-in-one software package, like SAS or SPSS, where the user merely calls functions and does rudimentary data processing.
You can come up with ingenious methods in data science, that would be able to fetch a journal publication or two. However, if these methods don’t add value to an organization, they are not that great, from a holistic standpoint. This is observed in other parts of Science too, e.g. Electromagnetism. Despite the various theoretical aspects of that field, its usefulness is also apparent. People who practice this part of Physics tend to be very practical and oftentimes come up with interesting inventions that add value to their user (e.g. in the case of electromagnets, or power transformers). Data science is not any different.
All the clever mathematics behind a method may be enchanting for the mind, but it’s when this method is put into practice and yields some oftentimes actionable insight when it really becomes meaningful. That’s something worth remembering, since it’s easy to lose sight of the questions we are trying to answer, and focus too much on the possibilities that we discover. And some may argue that it’s the journey that matters, but for a journey to be a journey there needs to be a destination. The latter is usually some person who doesn't care much about the science behind the insights, but more about their applicability and usefulness. Companies like MAXset LLC may be completely ignorant of that, but this doesn't make it a viable strategy. On the other hand, companies that have a chance of providing true value to the world make the business aspect of the craft their priority.
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.