Ram Kumar, is a visionary leader with over two decades of impactful experience spanning various industries and regions. As the Chief Data and Analytics Officer at CIGNA’s International Health division, Ram has spearheaded data-driven transformations, enabling CIGNA to offer affordable and effective healthcare solutions to millions across the globe. With an illustrious career across organizations like Quantium, QBE Insurance, IAG, and Oasis, Ram’s leadership has been instrumental in driving data strategies, governance, and innovation. His voluntary contributions to renowned institutions like MIT Sloan School of Management reflect his commitment to global data and technology standards. Ram Kumar’s unparalleled journey sets the stage for his insights in this week’s CDO Insights Series, where he unravels “Empowering Data & AI: Strategy, Prioritization, and Ethics,” shedding light on navigating the complexities of modern data-driven business landscapes. He talks about critical aspects of crafting data strategies that align seamlessly with business objectives, leveraging AI, Data Analytics to drive value and much more.
AIM: Your extensive experience in developing business, data, and technology strategies across diverse market conditions has been immense. How do you envision crafting a data strategy that seamlessly aligns with business objectives and drives value creation through AI and Data Analytics?
Ram Kumar: Embracing the larger goals of organizations, whether it is achieving “digital transformation,” competing on analytics,” or becoming “AI first,” requires embracing and successfully governing, managing data, and effectively and efficiently using it in all its forms through a “well designed” and “implementable” data strategy is an essential prerequisite.
A data strategy is a plan of “data-driven” and “data management” initiatives that support and enable your organization’s business strategic goals. An effective data strategy focuses on improvements or changes to the organization’s structure, processes, policies, and importantly imparts the culture of becoming data-driven/led and enabled through technology to drive business value creation. As a data leader, it is important to design a holistic data strategy for the organization that is implementable in an incremental manner and that would enable data-driven business value creation.
Some of the key traits of a solid data strategy include:
- It should be designed with a business outcome in mind by tightly coupling it with business strategies and priorities, and each component of the data strategy should be linked to business value creation; otherwise, it should not be in the strategy.
- It should not be a technology-led data strategy, meaning it should be a business-led data strategy and is designed independent of technologies and tools, and technology strategy is overlayed on top of the data strategy to enable it. This is the only way to manage increasing changes to technology landscape.
- It should also cover end-to-end data lifecycle management and its supporting components and foundations in addition to analytics and AI, such as data quality, metadata, data lineage, data catalog, master and reference data, data governance, data risk management, acceptable use of data, data literacy and importantly, with clearly defined accountabilities/ownership/stewardship/custodianship.
AIM: Your background showcases delivering pragmatic and sustainable business outcomes. Could you share an example where you’ve effectively prioritized business use cases to harness data insights and AI technologies, resulting in significant operational improvements or innovative solutions?
Ram Kumar: As the Chief Data and Analytics Officer for Cigna International Health, I oversee over 30 countries and jurisdictions globally across most continents. This makes the task of identifying and prioritising data analytics/AI driven business use cases that would create value for the business an interesting challenge, but a great opportunity to focus on what really matters for the business. So, my number one priority is identifying those use cases that are important to the market from an impact perspective and executing, operationalising and measuring the business value created by these use cases and importantly, by handholding my business stakeholders throughout this journey.
We built and successfully tested a comprehensive data and analytics/AI business use case and value creation prioritisation framework. This framework applies a number of key decisions enabling filters specific to businesses and International Markets function as a whole, which would help us to make informed decisions by creating the right balance between business-specific priorities and our division’s strategic priorities. We apply this framework diligently by working closely with our business leaders, business stakeholders and our partners in prioritising and finalising the use cases that we need to focus on. One such successful prioritised business use case is predicting the likelihood of patients with chronic diseases returning to hospital within a specific timeframe and thereby, proactively reaching out to them and enrolling them in a personalised and specialised care management program that would save them hospitalisation and other costs and at the same time help manage their health proactively and better.
AIM: How do you envision the creation of a dynamic data roadmap that not only aligns with immediate business needs but also adapts to evolving technology trends and changing market dynamics?
Ram Kumar: I have been in the data and technology business for 34-plus years. From my experience, one of the key traits of a solid data strategy is to clearly distinguish between data-related components that are foundational and those that are non-foundational. A “Data first” approach or a “Data-led Technology-enabled” approach should be the key foundational principle for building a dynamic and sustainable data strategy and supported by a roadmap. It is important to keep in mind that today’s technology is tomorrow’s legacy. Foundational data components are generally independent of technology trends and the changing technology landscape. The development of the foundational data components must be mandatory and cannot be compromised and must not be tied to a vendor technology solution if one needs to build a dynamic and sustainable data strategy. Examples of foundational data components include:
- Data quality by design framework,
- Data privacy by design framework,
- Data ethics by design framework,
- Data risk management framework,
- Data architecture that is not tied to technology solutions or tools e.g. data integration and interoperability standardized design patterns
- Data Governance Framework
- Policies, Standards, Processes and procedures to manage the lifecycle of critical data assets.
AIM: Given your experience in international markets, how do you plan to establish a data governance framework that ensures compliance to data residency and local regulations, security, and quality while also nurturing an environment conducive to innovation and agile decision-making?
Ram Kumar: My view is that traditional data governance approaches and practices are no longer effective in the changing data and AI-driven world, particularly with the increasing complexity of data. Moreover, organizations do not have the patience or budget or time to support big bang initiatives. They want speed of delivery. The term “Data Governance” is often seen by businesses as slowing the exploitation of data with processes and controls that are seen as policing or bureaucratic, and slowing data-driven value creation. Applying the same data governance principles and frameworks used for managing operational data environments may not work efficiently and effectively for data-driven value creation using analytics/AI. This, therefore, calls for “Smart Data Governance” that would enable critical data that really matters for businesses to consume and create value with minimal fuss, and at the same time ensure that the data is used in an acceptable, appropriate, and meaningful manner from a risk management perspective. With my role to build data and analytics and AI capabilities in over 30+ countries, big bang and traditional approach to data governance will only slow things down.
I have successfully implemented an innovative and smart data governance framework across international markets that is nimble and agile and that is applied across all business, analytics/AI, and technology initiatives across international markets and applies the concept of a business risk lens. Its first of its kind in industry. The objective of the framework is to enable data for value creation and not to slow down data use. I have a team of only four in the data governance function overseeing international markets. We use an incremental approach rather than big bang by demonstrating value creation to the business thereby, winning their support and confidence in what we do.
AIM: You are an experienced champion in building the right data culture in an organization to create value out of data analytics and AI. How do you do it?
Ram Kumar: Recent surveys demonstrate that building a data-driven organizational culture is the biggest challenge organizations are facing, and less than 10% of the organizations have done it right. Unfortunately, the percentage is decreasing year on year, which is disappointing. There is a general perception in industry that performing analytics/advanced analytics and building data-driven intelligent (AI) solutions to drive business outcomes using data means an organization is “data-driven” or has a “data-driven culture.” However, in my view, this is limited. Data culture is more than that – it’s about how the “lifecycle of data” is managed and governed effectively and efficiently. This enables an organization to organize, enable/democratize its data for consumption to drive activities in an acceptable manner to make informed decisions, create value, resolve conflicts, and manage risks. I have been fortunate to build an effective data culture in organizations to the extend where a certain percentage of CEO and his/her senior management teams’ performance bonus is tied to driving the right data culture across the organization. A true data-driven organization does most or all the following things to drive the right culture proactively, being forward-looking by sincerely and religiously treating data as a “first-class citizen” or “crown jewel” of their organization.
- Accountability from the Top
- “Data First” Mind-set and Culture
- Data and Analytics/AI is a Business Function
- Data Strategy that is Executable
- Prioritization of Value Creating D&A/AI Use Cases
- Measure, celebrate and reward success and, communicate
- Build relationships
- Collaborate and ensure alignment
- Democratize Data for Easy Access, Sharing, and Use
- Data Risk Management
- Smart Data Lifecycle Governance and Management
- Data Quality by Design Culture
- Data Literacy Embedded as part of Learning and Development
- Acceptable Use of Data – social, privacy, ethical and legal
AIM: With your expertise in crafting business strategies, navigating data intricacies, and fostering a culture of innovation, how do you foresee the growth of AI, data analytics, and ethical considerations shaping the future of industries, and what role do you envision your leadership playing in this transformative journey?
Ram Kumar: I started my AI journey back in 1986 and spent over 20 years engaged in fundamental R&D and applied AI work by working closely with some of the founding father and pioneers of AI. Throughout this time, I held a strong belief that AI would eventually disrupt the world. Witnessing AI’s significant rise now, after nearly four decades, brings me immense joy. AI, in conjunction with analytics, signifies the next pivotal wave of transformative technology since the inception of the World Wide Web. Its potential, which we can’t yet fully fathom, holds the capacity to reshape industries, spawn new ones, and address global challenges across sectors. AI’s permanence is irrefutable—it’s here to stay. An organization lacking a robust business strategy encompassing data, analytics, AI, automation, digital technology, and talent management will encounter difficulties competing in the future. This conviction is deeply ingrained in my perspective.
As a leader tasked with steering value creation and innovative solutions through data, analytics, and AI, my responsibility extends to shaping the organization’s future. Guiding the transformation journey to optimize the potential of these areas thoughtfully is paramount. This involves experiential learning, deploying solutions for enhanced productivity, delivering positive experiences to stakeholders, and fostering competitive and innovative solutions. But all these should be linked to business value creation. The merging of AI and automation is projected to disrupt the workforce, leading to job displacement and economic disparities. In this rapidly changing and disruptive landscape, my role is pivotal in supporting employees, fostering resilience, and adapting to emerging realities.
While AI holds the promise of positive transformations for my organization, it also presents substantial challenges, particularly concerning ethics and data bias. As a leader, maintaining vigilance and ensuring that AI serves the greater good is essential. Approaching AI with cautious consideration and establishing ethical frameworks and guidelines to govern its application are crucial steps in this journey.