We keep hearing this from all quarters: ‘The world is changing rapidly’. While it’s a fact, we need to understand better what these changes are, how they can affect industry, and how management education needs to prepare students for these changes. Let us look at two prominent emerging areas that are both disrupting as well as enabling businesses: data science and machine learning.
It’s is a vast field that transcends businesses, mathematics, statistics and computer science. This truly multidisciplinary field extracts knowledge and insights from data in various forms with the use of scientific methods, algorithms, processes and computer systems; further, it is used for business objectives. The field has gathered significant momentum and interest in the last few years, with the explosion of computer processing power, decreasing cost of data storage, and the quantum of data generation and recording. Big data analytics, a specific data sciences tool, is a popular term for the processing of extremely large amount of structured, semi-structured and unstructured data sets. It uses special software and algorithms to get enhanced insights into the domain that generated them (manufacturing, sales, marketing, etc) to help decision-making and process automation. By this definition, the application of big data analytics naturally transcends disciplines. The use of predictive analytics pervades diverse disciplines such as oil & gas, marketing & sales, sports, molecular biology, drug designing, waste management, and finance. Let’s look at some examples of how different sectors/industries use big data: Smart cities are a melting pot where a variety of big data technologies mesh with one another to transform a city into a semi-intelligent being; In marketing & sales, big data is fast emerging as a potent tool to gain deeper insights into customer behaviour and thereby act as a powerful driver in spurring innovation; In manufacturing, advanced analytics is used by operations managers on historical data and processes to identify relationships among discrete process steps and inputs, and then optimise the factors that prove to have the greatest effect on yield. The benefits to business are tangible and substantial:
Cost reduction: Big data technologies bring significant cost advantages when it comes to storing and analysing large amounts of data, Faster and better decisions: With the speed of in-memory analytics, businesses can make faster and better decisions based on the insights gained, Effective product launches: Customer need and launch medium insights help businesses deliver what the customer actually wants.
It is the scientific study of statistical models and algorithms that computer applications use to effectively perform a specific task without using explicit directions, relying on patterns and inferences instead. It is seen as a subset of artificial intelligence, and an enabler for big data analytics. Applications of machine learning and deep learning range from computer vision to speech recognition and translation to marketing to drug discovery. It is one of the fastest growing fields of artificial intelligence. Examples of how data science is being leveraged as a business tool abound, and a few are illustrated here. As one of the world’s largest FMCG companies, P&G generates significant amounts of data on market insight. However, the data volume was growing by the day, the costs of compiling and analysing were growing, and the time-to-insight required in terms of the business event cycle in terms of decisions to take was shortening—all of which were a challenge for the global major. P&G built an analytics system that integrated and analysed data many years ago. It was considered a pioneering effort at the time. Closer to home, Hindustan Unilever Ltd (HUL), one of India’s largest FMCG companies, is using artificial intelligence software Jarvis to extract details such as who all visited the grocery store over the last three months, and what they are likely to buy the next time, which will help distribution, manufacturing and positioning in a big way. The phenomenon of data science is not limited to the FMCG sector. Companies in one of India’s largest conglomerates, the salt-to-software Tata Group, are partnering Tata Insights and Quants (Tata iQ), a big data firm, to analyse data collected from users and consumers and make sense of it to put changes in place in multiple business functions. At FORE School of Management, we have recognised the importance and the immense potential of these fields. We have integrated modules on these subjects into our flagship programmes for those intending to specialise in related domains—we run specific online certificate programmes in big data analytics for business and management and machine learning and deep learning. Both these programmes are conducted in association with Tech Mahindra, which helps to lend a practical and industry touch, alongside academic rigour. With certificate programmes and integration of data science and machine learning in regular programme structures, business schools can deliver relevant and up-to-date education to tomorrow’s business leaders. Moreover, this will position management institutions as robust enablers for the business world to derive the full benefits of these exciting fields. The author is director, FORE School of Management, New Delhi.