Business Analytics & Data Science – An Indestructible Duo

Business Analytics and Data Science are versatile skills that open up new opportunities across all the industries that have now acquired the status of being ubiquitous across operations and domains.

Over the last five years, about 90% of all small, mid-size, and large firms have adopted analytical capabilities to remain competitive in a market where enormous amounts of information are generated each day. They utilize it to create analysis models, recreate events, comprehend reality, and forecast future situations.

According to a recent LinkedIn report, some of the fastest-growing in-demand professions are related to data science over the past year and the years ahead. Hiring for Data Scientist, Data Science Specialist, Data Management Analyst, and Statistical Modeling positions have increased by 46% since 2019.

Finding ways to enhance a business is indeed the core purpose of business analytics. Business analytics as a concept has been employed since the late 19th century. A business analyst would rationally and analytically address the issues that a company encounters, by using data and statistics.

Analyzing business data, predicting the future based on historical data, optimizing strategies, or enhancing data visualization with graphs and charts all are examples of business analytics. One would be responsible for not only evaluating all of this data but also disseminating the findings and interpretations to other employees in order to implement the reforms that were recommended.

On the contrary side, data science is significantly broader than business analytics, yet adheres to a similar approach. Data scientists employ analytics and data to drive innovation, similar to the way business analysts do. However, data scientists would apply their abilities in a wider range of industries, such as academics and technology, in addition to the conventional business and finance industries a business analyst may work in, rather than emphasizing on only business-related issues.

In a nutshell, business analytics uses analytics to generate data-driven recommendations, bridging the gap between information technology and business. While the analytics element requires knowledge of data, statistics, and computer science, the business component necessitates a profound understanding of business. Whereas, data science is the study of data using technology, statistics, and algorithms, it is the process of using data to answer questions and forecast consequences.

This is true since the majority of tools and approaches are simple to use and have widespread applications among experts from a wide range of fields including BFSI, marketing, agriculture, healthcare, genomics, and others.

Additionally, the foundation of data science and business analytics is the use of basic human intelligence, which could be used to address the issues in any sector.

As a result, creating analytical models does not necessitate sophisticated mathematical procedures. The math taught up to the 10+2 level is sufficient and could be used as a foundation by experts in all disciplines.

Current technological systems and digitization like the Internet of things and cloud computing produce a vast and continuously expanding repository of data each day.

To retrieve knowledge enabling decision-making, analysis of such massive data gathered from various sources necessitates a great deal and more effort at multiple stages and so, business analysis is a new and developing field of study that is of larger demand.

Following the identification of numerous critical challenges, there are countless untapped opportunities for exploiting big data across diverse fields. The solutions to many business-related challenges could be found by analyzing the data using the proper tools and techniques.

Consequently, new career paths and job responsibilities in a variety of industries and sectors are being developed. Such professions are pertinent to business and revenue, and are currently in high demand.

An MBA or PGDM programme in Business Analytics & Data Science is designed with a mission to equip students with the skills necessary for enhanced employability and job prospects in the fields of data science and analytics. The curriculum intends to educate learners on the efficient use of data to back up management decisions. The MBA or PGDM in Business Analytics and Data Science as a specialization emphasizes skill-based learning through a hands-on approach.

Business Analytics and Data Science could be the ideal profession for individuals who are interested in enhancing their careers in empirical research and strategic reasoning ability.

Enroll in an MBA or PGDM programme in Business Analytics and Data Science as a specialization at one of the finest and most prominent B-Schools in India, where the emphasis is on preparing students for life by instilling in them the attitude of an eternal student. This will help students become leaders who are always learning, driven by a thirst for knowledge, commitment to excellence, and the desire to achieve for life.

Here are a few of the best high-paying jobs in this field to consider:

1. Data Scientists

Data Scientists must understand business challenges and provide the best solutions through data analysis and processing. For example, they are expected to perform predictive analysis and comb through "unstructured or disorganized" data to provide actionable insights. They can also do so by identifying trends and patterns that will assist businesses in making better decisions.

2. Data Architect

A data architect creates data management blueprints so that databases can be easily integrated, centralized, and secured with the best security precautions. They also ensure that the data engineers have access to the most up-to-date tools and systems. A career in data architecture necessitates knowledge of data warehousing, data modeling, extraction, transformation, etc.

3. Data Analyst

A data analyst defines data in order to apply the findings to a specific business problem or bottleneck that must be resolved. It contrasts with the role of a data scientist, who is involved in identifying and solving critical business problems that, if solved, could add enormous value. They use statistical techniques to interpret and analyze data, improve statistical efficiency and quality, and implement databases, data collection tools, and data analytics strategies. They assist with data acquisition and database management, identify patterns in complex data sets, filter, and clean data by reviewing it on a regular basis, and perform analytics reporting.

4. Data Engineer

Businesses today invest significantly in data, and the data engineer is the person who builds, upgrades maintains, and tests the infrastructure to ensure that it could handle algorithms devised by data scientists. They create and maintain architectures, align them with business requirements, identify methods to ensure data efficiency and reliability, perform predictive and prescriptive modelling, and interact with stakeholders about analytics initiatives. The best part is that data engineers are in high demand across a wide range of industries.

5. Analytics Manager

All of the aforementioned operations are managed by an analytics manager who assigns assignments to the various team leaders in accordance with requirements and capabilities. The majority of analytics managers are proficient in SAS, R, and SQL technologies. To lead a team of data analysts, source, configure, and deploy analytics solutions, and create systems for data analysis to derive useful business insights, they must possess a comprehensive knowledge of the company's requirements, goals, and objectives.

The average Data Science and Business Analyst compensation may begin from a range of 6,00,000 and 8,00,000 as a skilled fresher. The salary at the entry-level with less than a year of work experience is at the lower end, and the salary for individuals with two to four years of work experience is on the higher end.

Data science is considered to be a domain that is of very high demand in the future and it is ideally the best choice for today’s young managers to be skillful and able in this area.

About the author

The article has been written by Prof. Sheetal Mahendher-HOD Data Science & Business Analytics, ISBR Business School.