It is very intriguing to think that a misadventure at a wet market in Wuhan, China, has affected the lives and livelihood of people all around the globe. Life after COVID-19 will not be the same. The way organisations work will also not be the same again.
As of now, most of the businesses are bleeding and are cash-starved. To recover, they will look to minimise their expenses subject to meeting appropriate service levels and customer satisfaction levels. One of the primary expense heads is the workforce, and organisations will try to optimise and rationalise the workforce. Having cross-trained employees is considered an asset that helps an organisation remain lean. We are living in an extremely volatile and uncertain world. Skills that were relevant a while back ceases to be relevant now, and people continuously need to upskill and reskill themselves to stay relevant. People also need to be adaptive and flexible and be willing to experiment and take risks. In Darwin’s theory of evolution, he postulated that a species could survive the longest if it can adapt and adjust to the changing environment in which it finds itself.
Similarly, people who are willing to adapt to the changing situation and are flexible will tide through the present situation. However, people who are not willing to adapt may be very severely affected. Also, due to the social distancing norms, organisations will have to work with lesser capacity in the workplace, and some people will continue to work from home. Hence working and managing the extended team will be challenging.
The type of problems that companies will try to solve will be more challenging, but on the other hand, they will have to work with strict budgetary constraints. This will increase the need for human resources who are innovative and frugal problem solvers. To solve a real-world, impactful problem, one cannot work in silos but need to understand the entire gamut of the business landscape. With data being the new oil, one has to substantiate the findings and analyses with appropriate data.
With the immense popularity of sophisticated machine learning and deep learning algorithms, people tend to think that the mere implementation of such sophisticated algorithms will solve the business problem by providing actionable insights. However, the true picture is that these algorithms only act as mere tools for better decision making. The solution to business problems using actionable insights is still an art that requires a thorough understanding of the variables affecting the problem space. A well-rounded decision scientist is not only adept in the technicalities of applying the algorithms but also can provide valuable and actionable insights to solve the business problems. Needless to say, to be an impactful decision scientist, technical and cross-functional business knowledge is required. A problem that may appear as a marketing problem may have important operations and supply chain interdependencies. Hence, to solve the problem, one needs to have an understanding of operations and supply chain as well as the marketing domain.
Rapid change and mutation
Moreover, as data science and analytics is a relatively new and emerging field, the field as a whole is also undergoing rapid change and mutation. Data scientists have to be continuously on top of these changes. Hence, adaptability and cross-functional understanding are ingrained in the DNA of a well-rounded and successful data scientist.
Education in data science, analytics, and decision sciences should impart individuals with the right blend of techno-functional skillsets required to be seasoned decision and data scientists. Individuals should also have leadership traits, be risk-takers, and should have the first-hand experience in solving real-life industry problems. However hard universities may try to impart these skills to their students, corporate sectors have been lamenting on the lack of employability of such graduates as theoretical knowledge is not a very good antecedent for their performance in the industry. Hence there is a need for greater industry-academia collaboration to derive synergies in the education space. Traditionally, this has been operationalised by internships and corporate training by students, yet such short corporate stints are inadequate to have the full flavour of corporate work.
Moreover, the close coupling of theoretical knowledge imparted in classrooms and their application in the internships are missing. To address this gap, a novel counterfactual solution was required. Mu Sigma, the largest pureplay analytics company in the world and T A Pai Management Institute (TAMPI), have collaborated and co-created a unique 11-month programme, Leadership through Analytics and Decision Sciences (LEAD).
Decison science leaders
This programme started as an experimentation by TAPMI and Mu Sigma to build a new breed of decision science leaders who have adequate technical and business analysis skills with the right industrial experience. Students graduating from this programme will be industry-ready from day one. This programme will create young analytics and decision science leaders who can lead global projects and bring great value to Fortune 500 clients.
TAPMI will provide the appropriate and contextual, theoretical knowledge in all facets of management discipline, and Mu Sigma will impart contextual technical knowledge, experiential and on-the-job training to the participants. Students will be exposed to real-world complex problem spaces throughout the programme duration, thus imbibing unparalleled experiential learning. Therefore, students graduating from this programme will be better placed than others in handling the challenges thrown by this extremely volatile, uncertain, complex, and ambiguous (e-VUCA) post-COVID-19 world.
(The writer is Assistant Professor (Operations & Information Science), Programme Chair – Leadership through Analytics and Decision Sciences (L.E.A.D), TA Pai Management Institute, Manipal)