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New Delhi: Few terms get thrown around in the tech industry as frequently as ‘digital’. Initially considered to be a fad by many, digital is now well recognized as a transformative force and includes such emerging areas as blockchain, artificial intelligence (AI), and the latter’s sub-fields like machine learning and deep learning. To understand the progress that these technologies have made, Mint’s Enterprise Technology Summit organized a panel discussion on ‘How companies across sectors are using digital technologies’. The discussion was moderated by Leslie D’Monte, national technology editor at Mint. Edited excerpts:

Anshuman Bapna, chief product officer (new developments), MakeMyTrip

MakeMyTrip is a digital-born company and we have had our business in the digital format for more than a decade now—the number of data points we have is simply astounding. We take a step-by-step approach to digital, starting with applying analytics but in a disciplined manner across the organization. The big data mindset, to me, is not only about how big the data is but obsessing about collecting each and every data point about your business. For instance, while you can do a lot of analytics by looking at which flight options a customer clicked, there is a lot of intelligence around the real estate we have in terms of the screen size and we have been able to do a lot by analysing the options that did not get clicked even though they were right up there. Furthermore, we applied machine learning to discover patterns in user behaviour that we earlier did not think to be important but that turned out to be potential drivers of transactions.

Manik Nangia, director (marketing) and chief digital officer, Max Life
Insurance Co.

Manik Nangia.
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Manik Nangia.

We are not digital natives but like teenagers learning to walk. To judge how digital an organization is, you can look at it in terms of culture, strategic orientation, capabilities, and talent and organization. I would think we are somewhere between six and seven today (on a scale of one to 10 in digital readiness). We are strategically aligned in terms of the direction that finance is taking and how consumers want to engage with financial services organizations. Customer behaviour is driving these things and the moment customer-centricity is at the cultural heart of the organization, I think the organization could be well past mid-point in digital. But we have a lot more to do such as collaborating more with start-ups and understanding what’s new in the world and bringing it on for our customers.

Prateek Garg, MD and CEO, Progressive Infotech Pvt. Ltd

Prateek Garg.
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Prateek Garg.

Data-driven transformation is really happening. The world is beginning to realize there is so much of data available. Recently, for instance, we worked on data analytics for a healthcare delivery company in the US, which realized it was losing revenue; we worked on creating a data model for them to deal with the complexities of handling data, giving them insights from that data and thus helping them to curb revenue leakage. Also, when machine learning was used on the data, they were able to more accurately predict their revenues. There is also disruption in the services industry, where many things are repetitive in nature. We are able to use machine learning algorithms to be able to help drive efficiencies of our own people and reduce the cost of delivery while improving customer experience.

Sanchit Vir Gogia, chief analyst, founder and CEO, Greyhound Knowledge Group

Sanchit Vir Gogia.
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Sanchit Vir Gogia.

There is so much of hype; what boils down in the end often is pretty much the same thing that has been done over the years. For instance, sometime back I met the CEO of a large hospital who spoke about AI and when I asked him how AI would help in their business, the example he gave was of analytics. The point is, a lot of organizations are still troubled by the differentiation in technologies. I generally divide companies into two categories: organizations that are physical-asset heavy and those that are data-asset heavy. The idea on both these sides is: How do you sweat these assets? Is plain-Jane efficiency enough? Actually, not any more. Even if my asset is doing 80%, is it enough for me to do just 80% or can I do something to get more out of these assets in, say, non-peak hours? For a data-asset heavy company, the question would be, How do I get even more out of the millions of data points and deliver, say, an even better customer experience?

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