The use of the term "Data Science" is becoming increasingly common along with "Big Data. The term "Science" implies knowledge gained by systematic study. According to one definition, it is a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe. Data Science might therefore imply a focus around data and by extension, Statistics, which is a systematic study about the organization, properties, and analysis of data and their role in inference.

Nowadays data is generated by computers with various markup languages. Which in turn makes it difficult to achieve the correctness in prediction to ethical and privacy issues. Majority of data is still in unstructured format. From an engineering standpoint, scale matters in that it has rendered the traditional database models somewhat inadequate for knowledge discovery. Traditional database methods are not suited for knowledge discovery because they are optimized for fast access and summarization of data given what the user wants to ask (i.e. a query), not discovery of patterns in massive swaths of data when the user does not have a well formulated query. 

With data science tool kit, it's increasingly becoming easy to use any language with query constructs to extract data specific to the ask. To name few languages they are REST/JSON API, Python, Ruby, Java Script, Excel Macros, and so on.

A simple example of how data science can predict better is by defining the patterns and asking the right question. Machine learning greatly helps in reading the patterns and giving some interesting answers. Example pattern to identify traveler segments is as following.

Traveler of age > 50 and citizen = true and frequent flyer miles >= 10000 and yearly travel spend > $500

Such patterns gets to predict outcome very accurately leaving very little deviation in the expected outcome. And prediction is critical with such patterns in every industry to help nurture available data for it’s own survival.