Data science is the discipline that studies the life cycle of digital data from appearance to use in other fields of knowledge. It involves researching and analyzing substantial amounts of information and is focused primarily on obtaining practical results. The definition suggested by Os Keyes presumes that this science is the reduction of humanity to something that can be counted. This perspective is engaging in a way that Keyes’s perspective opposes the standard definition of data science. Perhaps, her expression implies that everything in this world could be measured since the discipline is concerned with countable objects and quantitative analysis. There is also an assumption that data science systems want to devour everything possible, including a human’s life, and quantify it. Finally, when it comes to the decision-making process, the systems eliminate the human factor, excluding individuals, thus making it inhumane.
Numerous standardized tests are based on an algorithm that does not allow people to express their opinions. Typically, they are multiple-choice credits where one should choose the right answer. One mistake and one is at a point of no return. It means that systems only accept correct data, not allowing a human to err. As a result, humanity is accustomed to giving the right answers when necessary and at the same time being afraid to say something that will not be automatically approved.
On the other hand, data science is vital in areas where mistakes should be avoided. For example, in healthcare, workers use different electronic systems that assist them in choosing the proper treatment and its dosage. Medication errors occur due to human factors mainly, for instance, when professionals forget to check the expiration date of a drug. Meanwhile, the system will suggest which pharmaceutical is better for the patient and ensure it is not expired.