Manish Mittal, country head (India) and head of global delivery at Axtria, a global provider of cloud software and data analytics to the life sciences industry, talks about how AI will catalyse new business opportunities that fuse business and technology skills.
How are data scientists contributing to solving complex global challenges using AI?
Data scientists and AI modelers are the lesser-known stars in the realm of AI-powered healthcare innovation. Their prowess in deciphering intricate patterns with large datasets brings a new era of personalised medicine, predictive diagnostics, and data-driven decision-making for improved patient outcomes.
How are software engineers integrating AI solutions into practical applications for real-world problems?
Software engineering has come a long way, and today, in the realms of healthcare and life sciences, software engineers deal with a potential and highly responsible fusion between technology and healthcare. The life sciences industry has accepted that they must lead with AI to stay relevant and solve complex real-world problems. Software engineering brings this together at scale. The ability to devise a solution to a real-world use case via software that drives the speed of decision-making, efficiency of a business process, and effectiveness of an access channel is the next level we’re already experiencing.
What strategies are implemented to ensure AI solutions are ethically and culturally sensitive globally?
Digital transformations usually start top-down with leadership and then progress to the rest of the functions. As organisations get ready to deploy and integrate DeepTech and SaaS platforms, the realisation of deploying AI responsibly has begun already. In our experience of over 13 years in transforming data into actionable insights, we have experienced that getting the leadership to believe in responsible deployment is not enough.
It must permeate the rest of the organisation. To achieve this, it requires consistent efforts to reinforce that every line of code upholds the highest ethical standards while crafting algorithms and workflows. This step itself may constitute a sizable portion of tech deployment efforts.
One important tip is to be aware that risks are volatile and always in a reinventing mode. Organisations and security control teams must maintain guardrails while institutionalising robust enterprise-wide controls. We know and understand the need to ensure the highest level of data and internet security. As a result, a considerable effort is made to ensure that our security experts are up to date with the latest tools and processes, not just at enterprise level but also at skill level.
How does Axtria envision the skill sets of data scientists and software engineers in rapidly evolving AI technologies? What are the top 3 skills professionals need to succeed in the evolving landscape of SaaS-focused consulting?
Technology consulting and SaaS companies have increasingly raised their concern about the limited availability of talent possessing expertise in DeepTech and SaaS. The sector requires specialised expertise, and for talent to get trained on new platforms, it necessitates an ecosystem that provides access to real-world data, use cases, and the infra that can support work in innovative technologies.
In the evolving landscape where ‘ready-to-serve/ self-serve’ platforms are gaining popularity, there is a demand for professionals with a blend of technical, strategic, and interpersonal skills.
Considering global nature of SaaS, what opportunities do you see emerging for technology professionals in data analytics, and how can they capitalize on this scope for talent at a global level?
The popularity of wearables and health gadgets has risen exponentially and is churning out torrents of user data and advanced diagnostics, yielding ingenious insights; the scope for innovation is enormous. Talent in STEM has the opportunity to use data analytics and generate insights that uncover medical mysteries, offer timely diagnostics, optimise treatment pathways, and pioneer personalised care. Aspirants must embrace algorithms and cultivate a deep understanding of industry nuances to succeed in data science careers in the healthcare industry and capitalise on global potential. Aspirants should be open to new industries and understand the integration of data and health.