Cigna International Markets’ Chief Data and Analytics Officer, Ram Venkatachalam, shares some key aspects of his wide-reaching data and analytics strategy
If the successful application of a data strategy in a single business is a satisfying accomplishment, the notion of doing it in businesses across 30 countries must appear astounding by comparison.
That’s the remit happily assumed by Cigna International Markets’ Chief Data and Analytics Officer, Ram Venkatachalam, whose comprehensive data and analytics strategy incorporates priority, capability and growth.
The executive, who is set to share further insights on adapting to the speed of business and making good business decisions at CDAO Online ASEAN in June, discussed elements of his journey since joining the healthcare and health insurance provider in October 2020.
Venkatachalam’s first mission was to make Cigna’s numerous international markets businesses understand the fundamentals in selecting, designing and implementing data-led business projects.
“For the money you invest in data and analytics, you must always be thinking about what the ROI for the business is going to be,” he says.
“Every data and analytics initiative should be tied to business value creation and its goals or KPIs around growth and revenue, operational cost savings, or affordability, i.e., making health services more affordable to our customers.
“How data and analytics can enable or contribute to achieving those goals and KPIs needs to be the focus.”
Prioritising the Right Projects
The ‘fundamentals-first’ principle that Venkatachalam brings to the organisation, honed over 30 years of work in the industry, serves as the basis for his structured approach to manage a remit that encapsulates the Americas, EMEA and APAC countries.
“We have developed a comprehensive Data and Analytics Business Use Case Prioritisation Framework to identify the right data and analytics initiatives to focus on,” he says.
“Think of a use case as a business analytics project, for example: a fraud detection model. Various prioritisation filters are used to finalise the use cases, such as data availability, business commitment, quick wins, size of the prize, speed of execution, ability to operationalise and so on.
“We work closely with the business during this prioritisation exercise to help them identify and pick the right data and analytics use cases to focus on with clearly defined and agreed value creation estimates.
“We agree on these priorities at the beginning of the year, but we don’t stop there. We review the use cases again with the businesses before the beginning of each quarter to ensure that their commitment to the agreed business use cases are still valid (as business priorities could have changed). If necessary, we reprioritise the use cases to align with changes in business. This helps the business and my team to be relevant and stay focussed on business value creation.”
One Size Doesn’t Fit All
Another important consideration that arises when Venkatachalam and his team are applying their framework is the varying level of data and analytics maturity that exists across the businesses.
“One size doesn’t fit all. So you need to understand the maturity of those countries or regions in data and analytics in order to decide on the right use cases. To understand maturity, you need to have done a data and analytics capability maturity assessment of each country or region against a set of criteria. This must be done on a regular basis as capability is developed,” Venkatachalam says.
“For example, one country might be all hyped up about data and analytics and want to do predictive analytics on customer retention, but they may not even have a basic portfolio understanding of customers to answer the ‘what’ and ‘why’ questions through an insights dashboard.
“It is therefore important for data and analytics functions to help that business understand and focus on the right areas, including building foundations that are important, helping them understand why they are important, and taking them through the capability development journey.”
While one business may not have the data and analytics maturity to build certain projects, The strength of Cigna’s international reach means capabilities can be developed elsewhere, for the benefit of all.
“There may be another country that is in a better position in terms of its maturity to implement a customer retention predictive analytics model. This would give the opportunity for other countries to then leverage this work. Our data and analytics function’s core fundamental principle is: ‘Leverage or reuse before buy or build’,” Venkatachalam says.
“This is the approach we take to achieve economies of scale across our markets while helping the countries to invest wisely and thoughtfully, and build data-driven capabilities.
“Educating the markets on data and analytics as part of capability development is core to our function. Data literacy is not just about storytelling associated with analytics and value creation only.
“To me, data literacy is about storytelling across the whole lifecycle of data and how each component (analytics is one of the many components) of the data lifecycle can help achieve business benefits.”