Newer data sets are convincing firms to migrate to the cloud: Navdeep Manaktala3 min read . Updated: 29 Dec 2019, 11:33 PM IST
B2B companies have done a tremendous amount of groundwork to build businesses that are as large in scale as the B2C startups
In India, a new data revolution led by cheap internet tariffs and explosive growth in consumer internet platforms are giving rise to rich data sets, which in turn can be used for consumer analytics and for targeting online ads. Increasingly, small to large enterprises and tech startups have begun turning towards cloud computing resources to store, process, and eventually use analytics to better serve customers.
In a recent interview on the sidelines of the Amazon Web Services (AWS) annual developer conference in Las Vegas, Navdeep Manaktala, director and head of digital native (startup) business, AWS India, spoke about how new and emerging data sets are prompting Indian startups and other enterprises to migrate to the cloud. Edited excerpts:
How are business-to-business (B2B) startups approaching the cloud? Do B2B startups host and operate differently on the cloud compared with direct to consumer startups?
B2B companies have done a tremendous amount of groundwork to build businesses that are as large in scale as the B2C startups. If you look at Freshworks, for example, they have customers across the EU, the US, Australia and other Asia-Pacific geographies. Essentially, most of the B2B startups on AWS have been on the cloud since day one of their operations. We term them as cloud-native businesses, where they are born in the cloud and built off the cloud, and then you have the other set of companies who are now starting to migrate to the cloud. If you look at Freshworks (a SaaS firm), they have built an artificial intelligence (AI) model for each customer that they work with to reduce the response time in terms of customer support. This capability is built on AWS.
With the boom in data, how is your content delivery networks (CDNs) service coming into play in India?
A CDN is typically used when you need large amounts of content to be delivered to the end customer without being stored on the public internet. All the media and streaming startups use CDNs because they don’t want video content to be stored and played out completely over the internet, as there will be connectivity issues on end devices. These include Airtel Wynk, ALT Balaji, Saavn, and many others. Gaming startups and even B2B startups catering to clients in the learning management systems (LMS) space have started to utilize CDNs to work around content delivery in patchy networks. AWS has 188 points of presence (PoPs) globally for the CDN operations and 17 of these are now in India, which is the largest CDN presence outside the US market.
Several consumer internet firms in India are embracing audio as an interfacing method beyond just touch and it is also being touted as an apt interface for the next set of incoming users in India. Is AWS prepared for this?
Speech recognition is basically three elements. The first is the automatic speech recognition (ASR) part of it. Then there is the natural language processing (NLP), and the third one is the text-to-speech conversion. What we have done at AWS is that we have taken all those three elements out and made it into three independent services. On top of this, we also have a translation service that can then convert text or speech between languages and we also have comprehend service that can understand the emotions and sentiments of the speaker. For India, we have started to add vernacular speech and text recognition in Hindi, Tamil, Telugu, Bengali, and Urdu with more languages in the pipeline.
How are you trying to fix the shortage of skilled data engineers and scientists in India? Are we going to see more data scientist talent moving to freelance basis?
We are doing several things to solve the shortage of data scientist talent. For use cases such as text to speech, ASR, NLP, sentiment analysis, we have pre-trained models available. So, you really don’t need a data scientist to build the model because the data model is already available from AWS. For example, we have made available pre-trained models for forecasting, for recommendation engines. The second way is that we are also adding data science talent to our teams. What we essentially do is provide data scientists to our customers on-demand. These are specialists who will work with clients closely and we are already doing this in India with a bunch of customers.