Dr. Nilanjan Chattopadhyay, Dean-School of Management, Bennett University has talked about the Demand for business analytics, basic skills for it and much more. Here is the piece by him:
Depending on past data to make decisions for the future is nothing new. Since we do it instinctively, often we do not even realise that we are doing exactly what is called ‘data analytics’ now. Business organisations depend heavily on data to make informed decisions. Therefore, they are hiring trained managers who are familiar to data intuition for identifying patterns from existing data and thereby assisting companies to grow their business.
Business analytics is not a new concept, but with the increasing volume and diversity of data, it has become almost unavoidable for organisations to rethink the way they process data to churn out insights. They need to use sophisticated tools, which can handle large sets of data with better turn around-time and more accuracy.
For decades, data-driven decisions were taken using descriptive statistics run on spreadsheet software like Excel. However, with the availability of advanced tools such as Python and R, professionals have moved ahead from spreadsheets alone. These new tools empower business analytics experts to effectively evaluate data with techniques like inferential statistics, A/B Test, machine learning to name a few.
Demand for business analytics professionals is skyrocketing. Recruiters are looking at Universities to hire the right talent who can help them make accurate business predictions. MBA majoring in Business Analytics is therefore on high demand. To become a good business analyst, one needs to have three basic skills viz. problem-solving, critical thinking and excellent communication. Knowing excel and having knowledge of basic mathematics and statistics are must to have to succeed.
At Bennett University’s School of Management, students are first made familiar to the advanced features of Excel which help them understanding visualisation of data along with the fundamental techniques of data cleaning, collating and presenting. Students are also made familiar to some of the essential methods like regression, clustering, natural language processing, data exploration, data preparation and hypothesis testing with the help of real-life data.
Training on popular software like Tableau, introduces them to the world of ‘storytelling with data’, which is one of the most sought-after skills today. Extensive training on tools like R and Python programming language using real-life business cases and data provides the much-needed ability to convert business problems into data science problems.