DATA SCIENCE BUSINESS ANALYST
Tags: beginners, programming, datascience
Published: 2022-10-19 11:01:33

Enterprises' access to large amounts of customer data has suddenly increased during the past ten years.

This information is gathered from several sources, including social media, mobile apps, financial transactions, e-commerce, search, and the Internet of Things, among others. IDC predicts that the digital world will increase by a factor of 300 between 2005 and 2020, going from 130 exabytes to 40,000 exabytes, or 40 trillion gigabytes. The digital world will roughly quadruple every two years from now until 2020. Furthermore, according to IDC, only a very small portion of the digital universe has been investigated for its analytical potential. Up to 33% of the digital universe will have data in it by 2020 that could be useful if studied.

Data science is a rapidly developing field as a result of the explosion in data from various sources.  **Data science** is an interdisciplinary topic that deals with _scientific methods_, _procedures_, and _systems_ to extract knowledge or insights from data in many forms, both structured and unstructured. The word "_insights_," which indicates producing findings that were previously unknown using conventional data mining approaches or straightforward trend and regression analysis, is the definition's major highlight. Because of this, the area of data science is thought to exist at the nexus of software, statistics, business domain expertise, and mathematics.

Although there are many tools for data science on the market, defining the use cases from a business perspective is a crucial step for the success of any data science endeavor. In the majority of businesses, technology teams are skilled in comprehending the data and performing analyses on it. However, the discipline of data science is distinct in that teams may occasionally be unsure of what they are looking for in the data because the insights are frequently not considered to be proven facts. Due to the requirement for someone who is fluent in both the language of business executives and the IT domain, the function of the **business analyst** becomes extremely important in this industry.

For instance, a **data science business analyst** would be required to translate a business problem statement such as "_Given past performance and current trends, what is the likely outcome of a certain action_" into an IT problem statement that specifies what data must be analyzed in order to derive the insights. Following a review of the data by the technical team, the business team would receive the findings in the form of insights and data patterns. Business analysts should have the skills necessary to use various predictive modeling techniques and make the appropriate model choices in order to generate insights for the current issue.

What distinguishes a _business analyst_ who specializes in _data science_ from a _general business analyst_, one could ask. A comprehensive understanding of data, along with industry and functional expertise, is one of the main traits that set apart the _data science business analyst_ and allows him or her to comprehend the business context and recognize the use case. Given the growing amount and breadth of data, data science business analysts must possess strong commercial acumen. Business analysts must also be able to communicate effectively in the languages of the technical and business teams with whom they will be working.

In its research on **The age of analytics**, _Mckinsey_ notes that while being a data scientist is a crucial ability, businesses also require **translators** who can connect analytical expertise with real-world solutions to business problems. _Mckinsey_ also emphasizes the need for use cases, which are sometimes referred to as sources of value, as the first of the five essential components for developing a successful data and analytics transformation. The business necessity and anticipated impact should be expressly stated in the uses cases.

The majority of businesses are unable to fully capitalize on data science projects because they heavily rely on data scientists who are very skilled at data preparation, cleaning, modeling, and software code writing using tools like Python and R but lack _domain expertise_ and _an understanding of what data means in a business context_. In order to bridge the gap between the business teams and the IT department for complicated data science tasks, the **data science business analyst** performs a very important role.