What are the leadership skills needed to drive effective change in organizations to achieve data and analytics excellence?
Since most Data Science & Analytics projects yield RoI over a period of time, the impact is only visible in the longer run, with little or marginal improvements initially when the project gets implemented/deployed - Hence, it can be a bit challenging, getting buy-in from the senior management & the board. Over this journey, I believe two skills that are most definitely required are, pragmatism & grit. Having a pragmatic view towards analytical projects helps to separate hype from reality and to evaluate objectively, without being biased with the popular opinion. And perseverance helps in seeing through the end-to-end journey from conceptualization to launch, while patiently waiting for results to unfold.
Can artificial emotional intelligence be a substitute for real emotional intelligence?
I think emotional intelligence, is a very "human" thing to have. To have emotions is what takes humans from "sentient" to "being". It's difficult to peg, with all the advancements in Artificial Intelligence & Machine Learning, weather we could achieve emotional intelligence or consciousness at scale using these novel methodologies, without any human element to it. Can it be a substitute of real intelligence, the answer is most definitely yes, but would it be as effective, as empathetic and consciously malleable as it's the human counterpart, is something that only time will tell.
What role does big data and analytics play in the insurance sector?
Insurance is an age-old business which consumes (& generates) unfathomably large amount of data, about its customers. Be it, personal details while purchasing health insurance, or claim details of your personal vehicle during an accident, the insurance companies look into every nook and cranny in order to be able to serve its customers. Hence it's imperative that big data analytics becomes a pivotal aspect the way business is conducted. Typically, it helps an insurer to better understand it's a customer so that products and offerings can be personalised. It helps in product pricing, by the way of looking at historical claims data and learning the associated frequency and severity with the loss.
Big Data is unearthing new opportunities to insure risks which were previously uninsurable, due to lack of experience or data - such as cab insurance, retail weather insurance (covering lack of rainfall/sunshine etc. in the farmlands) which are easy to purchase and comprehend, and with technology-based mobile-first offerings - there is a huge potential of unleashing the same at scale which can be a big opportunity for insurers.
What are the latest practices in customer engagement in the insurance sector?
When it comes to customer engagement, insurance companies are not exactly leading the path - partly because of the nature of the business we are in and the regulations under which we operate. Unlike e-commerce and retail companies where a customer does a lot of transactions, frequently - insurance is a one-time affair where choosing the right product becomes a cumbersome exercise if one is not familiar with the terminology and key concepts. If a customer chooses a wrong product, experience during the claims can be horrific, adversely affecting CSAT & NPS scores and thereby affecting engagement.
That being said, there are steps taken towards establishing trusted point of contact to the customers via online and web-based mediums - in the form of chatbots, and automated self-service portals (for policy endorsements, renewals etc.), thereby making the interactions much more seamless and less time-consuming. Motor Claims are being settled using image/video-based methods, making the whole process much faster and eliminating redundant manual paperwork. There are apps and personalised dashboards for customers to manage all their policies at one place - which helps in improving the renewal rates for insurers as well.
How can data scientists and analysts improve their communication skills?
This is an area where a lot of data scientists lack focus & fail to grasp. Being good with data and being able to explain data are two very different skills.
Taking an analogy from a popular food culture, where just chopping vegetables with great speed & razor-sharp precision is not enough, rather - it is the thoughtfulness of knowing how the chemistry of the ingredients work together & how exactly would your audience react to it, that makes up for a celebrated chef!
Improving communication skills, starts from listening & one should not confuse it with hearing. Active listening involves understanding user/client/customer requirements thoroughly while asking relevant questions that help in narrowing down the problem statement. Empathising with the problem statement and drilling down to key sub-problems can serve a good start. Limiting the use of jargon and speaking in plain terms would help to get the findings across stakeholders who are not well versed with the jargons associated with data science space.
Amitanshu heads the New Products & Strategic Initiatives division at Bharti AXA and has extensive experience in product development & management in the insurance industry, specifically overlooking the development of groups' microinsurance & parametric line of products. Amitanshu is a data scientist by profession, & did his Bachelors in Mathematics & Computer Science from IIT Kanpur.
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