Data science is evolving as a separate academic branch and is gaining huge traction in the world of business and economics, mainly because of new technological advancements in the field of processing and interpretation of data. As huge volumes of data is available for analysis, companies are trying to leverage data to improve business decisions, agility and competitiveness.
It is heavily speculated that the number of applications of data science are going to far exceed the applications of information technology, and there needs to be a dedicated effort to train the new workforce to take advantage of this new emerging technology. Years of investments by various academic institutes in the field of artificial intelligence is finally paying off as the industry witnesses growing demand in the applications of data science technologies, mainly due to the proliferation and accessibility of data.
Although we are still at the beginning of this technological evolution, we already see many real applications of data science, and these applications are expected to grow further. Data science is many times confused with data mining, which is analyzing of huge amounts of data to derive insights in order to support a specific hypothesis, but that is just one aspect of the entire data science spectrum. Although the role of data scientist is very prominent in this field, the data science teams usually comprise of a variety of roles and huge team sizes.
The key difference between the traditional data mining and business intelligence systems and the modern ones that leverage data science is the removal of intermediate human intervention that was traditionally required. This means that machines will actually take many decisions on their own, just to manage their regular operations.
What drives these decisions are the algorithms that run in the background, and there would be thousands of these algorithms interconnected to each other that would help the machines to take decisions. Self learning algorithms rely on the data that they are exposed to, and suddenly we see that the data is the key driver that will start influencing the behavior of these self learning machines.
Data security and integrity will become enormously important with data science rising to prominence and while most of the activities will be done by the machines in the future, managing data ethics and the algorithms that influence machine behaviors will definitely keep the future humans busy.