Top 10 Reasons Which Separate Data Science From Big Data Analytics And Business Analytics

With the advent of data and technology turning into big scale businesses, the world has changed. Technology and business have coupled together to form a huge sphere which has engulfed the world community. Both the elements of business and technology are connected and related to each other in a vast number of ways. None of them can go their separate ways alone. Each of them has its own set of contribution in spreading and supporting each other for the great learning. But there are a few things or reasons to be exact that separates data science from big data analytics and business analytics. In this article, we will talk about the top 10 reasons for the above query.

1.  Concept is the first and foremost discriminator: Data science is the field which tackles all sorts of structured and unstructured data and prepares, analysis and presents them. Big data analytics refers to the huge amount of unprocessed data that needs to be analyzed and prepared. While these two may sound similar, business analytics is the process which helps the manipulated data to take a form and reach other people around the world based on algorithms and other elements. Therefore, it is the basic concept of each of the following names that separates them from each other before anything.

2.  Application of the data science, big data analytics and business analytics: The three names have separate applications and fields of their own to deal in. Data science is applied in the fields of internet search, digital and online advertising and recommended systems which need its help. On the other hand, big data analytics is applied when companies have a huge stock of data related to their business and wants them to analyze and use them. Lastly, business analytics is applied to analyze and process the big data that have been collected and the company wants to use them for their business.

3.  Similar yet different fields: Data science helps to communicate and develop the value of the particular institution. Whereas data analytics involves itself with the work of gathering the data and processing them. Business analysts combine algorithms and other necessary development elements so that the business can progress in a good way.

4.  The minute line between the two: Data science explores various ways to develop the business even in the online market. You can consider it as a tool for tackling big data. But the major work of data analytics involve the analysis and processing of the data and putting it into a form which can be used to help the business.

5.  Data Analytics Vs Data Science: Big data analytics can be used in the field of communication. It helps in gaining new customers and expand the business within the current subscriber count. But in the field of data science recommender systems are present which make it easier to find relevant products from other products.

6.  Different working process and target: Data science is used as a tool to develop and predict results and other assumptions that is used to establish critical business solutions. On the other hand data analytics is almost the basics of data science but they deal with the visualization and have the ability to turn them into real-life data.

7.  Working terms: There is a huge deal of difference between the working terms of the data scientists and the data analysts or the big data analysts. A data analyst usually deals with the work of describing statistics and communicate data for retrieving points for drawing a conclusion. Whereas on the other hand data scientists need to work all time to promote and improve ways which can be owned. They try to make a connection between the business and the data that has been collected and processed. Big data analysts deal with huge amounts of data that needs to be polished and made to something useful.

8.  The indirect connections: Data science plays a very promising role in understanding and in the development of machine learning and artificial intelligence. It is also used to develop and produce algorithms which can be derived from the data collected. But business analytics does not revolve in the development of machine learning. It is, though, required to break down the data that has been collected, process them and come to a necessary conclusion that is prominent for the growth and progress of a business.

9.  Different yet linked areas: Data science is the future of the whole sphere. It is developing more with every passing year and unlike business analytics or big data analytics, data science is used to track down useful information from a set of data that has been processed by business analysts, and build connections which will help the business to reach higher goals in the future. Data science offers the company to look at the data that has been jotted down from different perspectives which can help the business to tackle different issues in the coming future. Big data analytics and business analytics are more focused and concentrated towards the business and strategic side of the whole idea we are discussing about.

10.  One is the present and another is the future: Business analytics and big data analytics are used by every company that needs to process huge amount of data, from where they want the great learning about different aspects of the human world. It gives an insight of business growth to facilitate the growth of the business. But data science, is a much more broad aspect which tries to deal with the future and wants to use the data collected, to bring in new point of views that was previously not known to the company and will help them in the future. Scientific methodology in data science explores the world of unknown and reaches a conclusion.

Therefore, as you have read through the article, you might have understood that all the three aspects of data processing and gathering are so much related to each other but at the same time, there are reasons which separate them as well. Great learning comes from great reading, so read more articles, books and blogs to have a better understanding of the topic.