The Future Of Data Analytics And Predictive Analysis In India
Data Science and Machine Learning have become the talk of the town, what do you think the future will be for this domain?
The future of the Data Science and Machine Learning domain is certainly bright, fast-paced, and transformational. Right now, in terms of capability, we have just scratched the surface and the best is yet to come. From predictions to robotics, AI/ML will help us across the length and breadth of the organization spread across various sectors. With the uncertainty that came into play due to COVID-19, companies would increasingly move towards automation to save cost, predictions for futuristic strategy, and personalization for enhanced customer experience. We will witness exponential growth in AI adoption in both the public and private sector and Citizen Data Scientist would be a prevalent term.
Currently, ML needs a large amount of data to train and produce accurate predictions, in the near future, we will witness predictions becoming more precise even with lesser amount of data due to inclusion of many more variable factors. Also, the shift from supervised algorithms to unsupervised ones will be notable, using which, with mere input data and variables, the system would be able to give affected variables and their accurate outcomes.
ML needs a large amount of data to train and produce accurate predictions, in the near future, we will witness predictions becoming more precise even with lesser amount of data due to inclusion of many more variable factors.
Do you see India adapting and adopting to new age technology? How and why?
Definitely! India is adapting and adopting to the new age technologies quite fast. In fact, based on a recent survey by PwC India, India has seen the highest adoption of AI-driven technologies in 2020. The government is also driving this adoption by approving Rs 3660 crore on technologies like AI/ML, IoT, robotics, big data, etc and establishing AIRAWAT for AI research and development. AarogyaSetu and many civic bodies' mobile apps are another great examples of this adoption. We are also witnessing multiple startups in this do-main as well as large organizations investing in and developing these new-age technologies. We are the frontrunners in the IT sector which will give us leverage in fast adoption. Also, the skilled manpower and the inclination toward AI education will be detrimental factors for further growth. Industries like healthcare, manufacturing, retail, and TMT will be navigating us on this road, but at the same time, the government would also need to implement AI in the public domain more and more.
A large deluge of data has been generated owning to COVID, how can data analytics help brands cull out only data which is relevant to only a specific domain.
Brands can utilize variance analysis to understand the effects of COVID on their business. Through this, they can see the difference between the actual data and forecasted value. They can know the specific variance for each attribute and the cause of this variance. Brands can now comprehend which values show the difference from the previously forecasted one and find out the reason behind it, which could be COVID or anything else, which has not been taken into consideration yet.
How is AI/ML helping the brand in under-standing the customers?
AI/ML can transform the way brand understands and communicates with their customers. AI enables companies to create accurate consumer profiles by tracking and analyzing consumer's demographics, past purchase patterns, social activities, click-through rates, email open patterns, and other data. With this, companies can get a deeper understanding of their customers, which allows them to personalize recommendations rewards, and more. ML also enables to gather and analyze and segment consumer data in real-time to understand the underlying sentiment. It can enhance the consumer experience by letting marketers know which campaign will work for which consumer and also suggest the right time, channel and message to deliver to any customer to induce sales.
AI/ML can also recognize customers and providing an enhanced and personalized digital experience. AI-powered bots are also helping customer to solve their issues and in turn generate valuable data to improve the services. From recognizing current demand to identify new opportunities the scale of AI/ML implementation can be vast. Brands need to democratize the data further to reap full benefits.
Shopping apps can leverage traditional home customs like scanning a hand-written shopping list, voice-based search, that too in local languages. A buyer can click his kitchen shelf pic and get his cart prepared automatically.
Do you think the use of AI will cause a breach of individuals' privacy?
No, AI is a layer that can rather abstract customer data into meaningless numbers and protect personal information from being misused. AI generally works on large volumes of data and for it to produce accurate predictions and unbiased results, the data certainly needs to be more representative. Companies can consider only collecting the data required for training AI algorithms and taking user's consent on which data will be collected and what the data will be utilized for, to maintain privacy as well as deliver an excel-lent consumer experience.
This concern about privacy is not just about technology, it is now much more about how much consumers trust a brand with their data. Hence, for developing consumer trust, the companies need to invest not just in AI development but also in how AI can be used to enhance privacy, given the technology is fairly capable of doing so. Engineers can surely develop systems and algorithms that would minimize the privacy issue and companies can have better data governance policies to address these questions regarding privacy and security.