Over the past some years, there is an instant spurt seen in the quantity of data that is possessed by the enterprises on their clients.

There are lots of disparate sources used for developing this data that include financial transactions, e-commerce, social media, internet, mobile applications, and lots of similar things. As per the estimation is given by the IDC, the growth of digital media take place at a factor of 300 between the periods of 2005 to 2020. It is same as 130 Exabytes to 40 trillion gigabytes.

There are strong chances that the digital world will grow by almost double by 2020. According to the further estimation by IDC, there is only a very small part of the people who become successful in exploring the analytic value. They also state that about 1/3rd of the digital universe will include details that may be useful if analyzed properly by 2020.

In the past, the organizations were successful to analyze a tiny fraction of data with the help of many data mining equipment and tactics. However, things work in a different way now as the data comes with several sources that make data science one of the quickly emerging fields in the industry. With data science, we are talking about the interdisciplinary field of scientific procedures, techniques, and systems for extracting insights through data that comes in different forms same as data mining that include both structured as well as unstructured things.

The main feature of this definition is the insights through which findings are generated that were unknown or impossible to understand through old-fashioned data mining tactics and the analysis of regression. It is the main reason that the data science has now started to be thought a perfect intersection of business domain knowledge, mathematics, software, and statistics.

A perfect example of the utilization of data science for all the organizations is their capability to forecast the churn of the clients in advance. The organization will get a great assistance through it for retaining the customer instead of just targeting the acquisition of costlier customers. There is no doubt that the market has a great variety of tools for the data science, but still, a very important part is played by the utilizing of the cases from the business view in the success of every data science objective.

The technology teams in the majority of companies are adaptive to understand the data and analysis run over them. However, the real truth is that the data science industry is unique in its own way because there are many instances where the team doesn’t know what they really want in the data as the majority of them lacks in the establishment of insights. Therefore, the duties of a business analyst become so important in this industry because there is a need for professionals who are completely trained for the IT domain as well as fluent in the talking with the business leaders.

For instance, the companies have huge expectation from their data science business analysts that they will convert the problem statement of the business “Given the previous performance and present trends, what will the most likely result of a particular action” for a problem statement in IT. According to that, there is a need to analyze the data before its arrival at the insights. After that, the technology department will review the data and outcome will be sent to the business department in the forms of data patterns and insights. In addition to that, the knowledge of the business analyst should be adequate for implementing the different predictive modeling tactics and accurate selection of models for developing instant insights on the problem.

There is an argument among the people regarding the difference between the general business analysts when compared with the one who works in the data science field. In order to differentiate the business analyst in data science field, one of the major skills is the adequate and the right information about the data along with the industry & functional expertise that allow them to get a proper understanding about the business context and assists in identifying the use case. There is need of deeper knowledge and understanding about the data in the data science business analysts because it helps in increasing the depth of data. Moreover, there is a need for the business analyst to work in a partnership the technical and other business departments so they should be fluent to understand their language.

According to the recent report published by the Mckinsey, named “The Age of Analytics”, there is no doubt that data science is an important skill, there is a requirement of the translator in the companies. These people work as the bridge between the practical applications and analytical talent for the business questions. One of the five critical parts is highlighted by Mckinsey to establish analytic transformation as well as successful data. It need use cases that can also be considered as the value source. The business requirements and impact of the project should be definitely articulated by use cases.

Another major duty that needs to be fulfilled by the business analyst for the data science related work is identification the accurate model for the use cases in a real environment. The knowledge should possess by the business analyst for framing the accurate hypothesis for testing it in a right way.

According to Albert Einstein, “If someone gave me 60 minutes for saving the universe, I will utilize 59 minutes to define the issue and remaining 60 seconds to solve it”. In simple language, there is a need to put a roadmap to the process that solves the problems by the business analyst.

It means they should be capable to solve the following questions:

• What is the meaning of regression or clustering?
• What is the right time for utilizing these techniques?
• What is the right method for formulating a hypothesis on my data?

The majority of organizations doesn’t know the right capability of data science assignments because they rely too much on the data scientists who are fully accomplished at the various skills, such as cleaning & modeling software code through tools, such as python and create a valuable data preparation. However, they don’t have the right knowledge and don’t know the data meaning in the business language. It is the area where an important played by the business analyst to reduce the gap between the IT departments & business analyst when it comes to complex data science assignments.