The multitude of Business Intelligence and Data Warehousing is mature enough with necessary tools & technologies, based on this business users are well versed with requirements. However, the new age of data analysis turned into expansion of Analytics which has opened up new ventures/technologies by naming this as Big Data. This leads into crop of data scientists that must highlight scientific insight skills with an overview of existing datasets, multitude of visualisaiton and analysis is endless. So this is where every organisation understands the need of big data and capabilities into their business objectives and procure necessary IT arrangements (cloud and on-premise).
Data science is a discipline, not a rocket science but not easy enough to handle!
In any field the Analysts are treated as experts in data usage, having different ways of analysing necessary data set which is now turning them into data scientists. To get into this trade you don’t need special educational background, but a grip on computer science for machine programming and mathematics with technology to ascertain statistical programming – a framework to understand where to begin. As always business will look up towards data science to build necessary algorithms, data managements, visualisation analytics with ‘affordable’ decision making.
A simple thought like this lead me into search of finding necessary information about comparison between business analytics and data science. Though both of them are like railway track, both are necessary for every organisation, see the attached infographic (Source: datafloq):
Not just with this high-level deep dive information, I’m inclined to show how data science is evolving into multiple disciplines.
Another infographic from Data Science:
A simple search on subject matter from your favourite search engine will lead extensive links for your own amusement, good luck.