Eleven Foreseeable Trends for Deep Tech in India: Dr. Jai Ganesh, Mphasis NEXT Labs

Dr. Jai Ganesh, Senior Vice President and Head, Mphasis NEXT Labs tells us about the 11 prominent trends that will be seen in Deep Tech in India

Supriya Rai
New Update
quantum computing

A technological shift is being observed in India in the field of deep technologies owing to the pandemic and the digital adoption it has led to. Dr. Jai Ganesh, Senior Vice President and Head, Mphasis NEXT Labs, talks to Dataquest about the future of Deep Tech in India, and the challenges faced in the implementation of the technologies


DQ: Deep tech is a set of relatively new technologies. How has the COVID-19 pandemic impacted this technological shift in India?

Dr. Jai Ganesh: The pandemic has given rise to various uncertainties and forced the world to seek out sustainable ways of engaging and communicating. There has been a significant behavioral transition with people increasing their affinity towards technology and thereby pivoting towards a more digitally sound future.

Deep tech primarily encompasses next-gen technologies like artificial intelligence (AI), Machine learning (ML), Robotics, Blockchain, advanced photonics and quantum computing. To drive resilience in the new normal, the need to scale these technologies to grow and thrive in the new reality is critical. In order to strengthen client relationships, enterprises of the future must harness their digital channels to drive resilience. The focus now falls on creating sustainable businesses that can weather future storms through digital plans that unite the front, middle and back offices, and align around the customer.


In this era, cognitive intelligence is critical in analysing and effectively providing the most contextual and accurate decision metrics from structured and unstructured data. It also helps improve timely decision making by exponentially reducing the turnaround time of business processes, which is a concern with the entire workforce working remotely. Furthermore, firms should focus on image processing capabilities along with cognitive optical character recognition (OCR) which will then enable processing of unstructured, scanned documents faster and reducing turnaround time. For instance, in the BFSI segment, data analysis is critical as insurers generate and use huge numbers of volumes, and because they pull in information from many different sources including - claim applications, health records, emails and call transcripts. But, with cognitive intelligence capabilities, this process can be paced up and simplified.

DQ: What are some of the trends we see as far as deep tech is concerned?

Dr. Jai Ganesh: Deep tech innovation has been accelerating at an unparalleled pace, with the pandemic providing the necessary push for accelerating digital transformation efforts. We are currently standing at the cusp of historic connectivity transformation with the onset of 5G. With the global workforce adopting a Work from Home regime, more devices are connected to the internet which is resulting in connectivity growing exponentially. Furthermore, with sensors embedded in the physical fabric of everyday life, 5G is powering Digital, Ubiquitous & Connected Human and Machine Networks, which facilitates seamless interactions. With intelligence embedded into the fabric of everyday life, Cognitive Futures is making it imperative for enterprises to reach their end-users through a multitude of devices and interaction channels.


Furthermore, sensors in the physical environment will result in a significant data surge. Enterprises will need to identify and respond to the exponential growth in data, captured from various sources. They will also witness unparalleled technology adoption with Digital Intelligent Ubiquitous Computing Systems consisting of human and machine networks acting as the foundation for business and technology architectures. Enterprise focus will be on multi-structured analytics which constitutes combining multiple types of data varied in terms of their type and frequency including structured, unstructured, multimedia data, streaming data, etc. Big data analytics about people and machines would give us a historical picture of customer behaviour, and known elements that constitute a claims fraud and their evolution.

Moreover, predictive analytics will observe acceleration for improving planning, forecasting and decision support (for decisions such as cross-sell, upsell, retention, loyalty management, risk mitigation, fraud detection, campaign management, inventory management, etc.)

Furthermore, enterprises are increasingly adopting technologies that offer immersive experiences for their end customers. The future of immersive experiences would involve combining the physical world and an interactive, three-dimensional virtual world. Augmented Reality has the potential to enable natural interactions and immersive user experiences by blending physical and virtual worlds.


The other trends we will observe in deep tech would fall under the category bionic sensors and hand-held devices leverage for location-based experiences, services and payment processing.

DQ: Similarly, what are the challenges companies face in implementing Deep Tech solutions?

Dr. Jai Ganesh: Deep tech innovation currently faces a myriad of challenges that firms are focused on resolving. To begin with, it is critical to have the right talent to navigate and scale up these technologies. This is proving to be a major challenge for enterprises that are looking to scale up their deep tech operations. Furthermore, with lack of business alignment, short term cost pressures restricting experimentation, ethical challenges with respect to model building, the scalability and adoption of these technologies is being hindered. The other challenges that we foresee include lack of access to real life data sets; privacy, security and data protection issues and most importantly the explain-ability of deep tech outcomes to the end user.


DQ: Kindly shed some light on the kind of Deep Tech that is being used in Mphasis?

Dr. Jai Ganesh: Mphasis DeepInsights is a Cognitive Intelligence platform, which enables enterprises to have faster and more effective access to insights from data. Cognitive intelligence is one of the major pillars on which tomorrow's enterprises are founded. By turning nearly every aspect of decision making, it is revolutionizing the competitive differentiation of enterprises.

DeepInsights is powered by state-of-the-art algorithms in machine learning, neural network, deep learning, semantics, image analytics, graph theory, predictive analysis and natural language processing. This further enables enterprises to engage with their customers through personalized experiences and explore newer business models that leverage the potential of anywhere any time on any device computing capabilities.


Below are a few use cases facilitated by DeepInsights:

Knowledge Management driven by ML & Semantic technology

  • Natural Language Processing based Knowledge Indexing and recommendations. Automate and optimize the key processes around content management and recommendation engine. This need was to automatically create abstract from free text, identify key topics in text to improve search and recommendations, keeping it updated, enhancing the search functionality, and making targeted recommendations to increase customer engagement and satisfaction.

Supply Chain demand prediction

  • Intelligent Demand Forecasting with Automated Model Selection & ML Ops. Set up a Machine Learning pipeline for seamless production deployment and faster deployment of new changes/models into production. Automatic model selection framework which identifies the best model combination (out of 40 time series, machine learning and deep learning models) for each demand category.

DeepInsights performs four critical functions:

  • Smart Data Ingestion: DeepInsights brings together the best of semantic analytics and image processing to intelligently extract the information from variety of sources including scanned documents, digital documents, incident tickets, events logs, source codes and emails,
  • Cognitive Analytics: The intelligent cognitive engine presents actionable business insights through deep analysis of extracted information by leveraging state-of-the-art algorithms in machine learning, neural network, deep learning and natural language processing domain,
  • Automated Decisions and Reasoning: Contextual and temporal decision making to generate the right insights from data and trigger downstream workflows for manual actions or Robotic Process Automation and
  • Versatile Interaction: Interact with enterprise systems and channels such as virtual and human agents, databases as well as APIs across industries

Some of the benefits offered by DeepInsights include the following:

  • A powerful cognitive engine automatically leverages the best of machine learning and deep learning algorithms to analyze data
  • Cloud-based and provides the most contextual and accurate decision metrics from structured & unstructured data
  • DeepInsights enables timely decision making by exponentially reducing the turn-around time of business processes
  • DeepInsights work with multiple file formats and types and presents information in a structured format for downstream consumption
  • Image processing capabilities along with cognitive OCR enables it to faster process unstructured scanned documents
  • Cognitive analytics-driven straight-through processing

DeepInsights offers value both in terms of time and cost-saving for its clients. As mentioned in the enclosed table, DeepInsights can be included in any process where more than 3-4 resources are manually extracting the data. DeepInsights not only provides cost-saving but also time-saving (~70%), by transforming manual processes to straight-through processes, to allow enterprises to make data-driven decisions at the right time.

DQ: What according to you is the road ahead for Deep Tech?

Dr. Jai Ganesh: Some of the trends we foresee in the near future include:

  • Cloud-first computing models
  • Data lakes to manage varied data sources
  • Quantum Cloud Computing Machines for targeted use cases
  • Flexible, Scalable, Lean & agile IT-architecture to support emerging data and analytics needs
  • Seamless support for emerging channels, end devices & immersive interfaces
  • Loosely coupled systems using Microservices for agility, stability and scalability
  • Software-as-a-Service & Platform as a Service for faster time to market
  • Automatically configurable Infrastructures
  • Improved developer productivity through intelligent assist systems
  • Pre-configured, reusable & intelligent software building blocks
  • Crowdsourcing of development resources