MBA students learn the nitty-gritty of ‘Business Applications of Data Analytics’

Data analytics is crucial to every business today – big or small. And why is data analytics important? That’s because it allows a business to create reports and find patterns that can help it operate more efficiently. Finding key data and analysing  the same help a company to improve decision-making by enabling  it to predict trends in its industry and identify what its customers will want next. These predictions help organisations stay on the cutting edge and remain competitive. 

Thus, to train budding professionals on data and its key functions,  the Department of Operations Management and Business Analytics at Apeejay School of Management, Dwarka, jointly organised a  seminar on ‘Business Applications of Data Analytics’. 

Industry experts Mr. Kshitij Sharma, Executive Director, Grant Thornton (GT) India, and Mr. Vishal Tiwari, Senior Manager and Global Lead, Demand Analytics Centre of Excellence, Carrier Air-conditioning and Refrigeration Limited shared their views on the topic with the students. 

Mr. Kshitij Sharma highlighted various avenues in business, where data analytics can become a powerful tool to drive business profitability. He emphasised, “As management students, if you are aspiring to get into business analytics, you must focus on deriving useful insights from the results of data analysis and hence, make more informed and appropriate decisions in a certain situation.” He also introduced the MBA students to various types of analytics – descriptive, diagnostic, predictive, prescriptive, adaptive and autonomous. 

Also Read: MBA school conducts workshop on ‘How to start a business’ 

Further, Mr. Vishal Tiwari explained in detail about the application of data analytics in operations and supply chain i.e. demand analytics. He also pointed out the importance of the difference between sales forecasting and demand forecasting along with constrained and unconstrained demands. Using a specific example, he elaborated on the use of statistical parameters such as Average Demand Interval (ADI) and square of Coefficient of Variation
(CV 2 ) for more accurate demand profiling. 

In the end, both experts involved the students in an interactive exercise.