— Abhijit Dasgupta
Data Science and Machine Learning (ML) may have originated from the science stream but the managers of tomorrow would be required to not only understand, but also leverage the technology. An interesting transition that took place in global business is the movement from manufacturing-oriented to customer-driven models, which has given rise to dramatic shifts in the behaviour of the business. Customers expect companies to anticipate what they need and offer products they want in real-time, which is where data science and machine learning come handy.
With globalised trade and commerce, one never knows when and where opportunity or threat emerge. In this scenario, how does an organisation prepare itself for resilience as well as continued success? Here are the top five skills and technologies that most organisations need:
– Data: Architecture, analytics, Data Science
– Content + Social Redefined
– Autonomous Systems – robots, drone, autonomous vehicles
– AI/ML – algorithms, computer vision, silicon for data/AI
– BlockChain – digital asset management, digital identities, cryptocurrency
As a result, the skill shifts in managing tomorrow’s business would be largely driven by the 3As:
– Artificial Intelligence
A new way of thinking needs to be embedded in future leaders to work along with algorithms and machines in the decision-making process. In the Big Data ecosystem of the future, no organisation can operate in isolation.
How does one use these technologies to improve the odds of success?
A function like supply chain and logistics, marketing, sales and customer relationships with a digital approach, manufacturing, etc, would demand an immense amount of automation. Routine activities and decision would be required to be taken by algorithms.
The way we see this evolving is akin to the advent of computers in the early years. Smaller processes would be automated using Machine Learning (ML) first. Once there is stability and confidence built up, more complex automation tasks would be taken up, such as triggering an automated inventory buildup in case of a severe weather forecast.
This speed of evolution is going to create a lot of stress in terms of the ability to understand and work along with these machines. Managers of tomorrow who are students of today would have to understand machine learning in terms of boundaries for their abilities at least in the current context.
They would be required to know how to structure a problem for deploying machine learning-led automation. Incorrect assumptions on efficiency and efficacy of ML would lead to cascading effects.
Will managers be required to code?
Coding, as we understand, is essentially problem-solving (specifically mathematical problems) using a software language. Coding would definitely be required to structure a business logic into a decision logic for somebody to code and set up an environment. An end-to-end thinking approach would require to be ingrained. Many future technologies would be black boxes such as Deep Learning, where decisions taken by machines would be difficult to trace back to typical variables. In light of such situations, a framework would be required to build up trust in the system.
Biases would require to be removed from data and decisions. Mere theoretical exposure of machine learning and data science would only make students hate the detailed math and code behind it. An applied approach is of essence. Any management discipline would have a number of cases, be it marketing, retail/luxury brand management, finance, HR or operations. The differentiation an institute can bring is in terms of the ability to help students conceptualise various applications to business problems.
— The director- BDVA and BDS and assistant professor, S P Jain School of Global Management
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