How Data Science adds value to the enterprise Technology

In the world of big data, it became a challenge for the enterprise industry to store data. However, this challenge was tough till 2010, later on, few framework and solutions were made to store data and that was Hadoop and several other frameworks.As a solution for the storage of data was provided the focus was shifted for the processing of data. Data Science acts as a secret stuff here and adds value to the business.
Why do We Need Data Science
Traditionally, data was found so structured and less in size, which can be analyzed by simple tools, but today data are not structured rather most of them are either semi-structured or unstructured.Thus simple tools were not able to handle or process such a large amount and variety of data.This is why there is a need for an advanced tool for analyzing, processing and withdrawing valuable insights from it.
Few Reason Why Data Science has become so Popular
Whenever someone makes the purchase from your site, you have the information or details of customers like customer’s past purchase history, past browsing history, income and age etc. with the help of you could somehow understand the requirement of customer and can suggest the product accordingly.However earlier also you were having such details but vast growing data it becomes a little bit complex to analyze, hence there is a need to improvise the tools and recommend the precise requirement of customers
How to define data science
The mingle of algorithms, various tools, and principles of machine learning with an aim to discover unseen patterns from the raw data is termed as data science. As the job of data analyst is to go through data and explains the details by analyzing the history of data. But data scientist does much more than this that means apart from analyzing the data, with the help of various algorithm they could identify the future occurrence event.
To make decisions and prediction data science make use of Predictive causal analytics :- If we want to predict the possibility of certain event that might occur in future, there is a need to implement predictive causal analytics.suppose if you are lending money to someone on credit, then it becomes a matter of concern that whether that person will do future credit payment on time or not. So here you can implement a tool which can make a predictive analysis of the payment history of the customer to predict the future payment will be timely or not.

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