I have worked with numerous teams in over 40 countries which has helped me working with people of different backgrounds and cultures. Prior to my current role, I was the CEO of a Data Science and AI company in India which is the COE for its parent company, top data science and AI company in Australia. That role was focused on organisational transformation to drive employee engagement, stakeholder engagement, capability development and performance. Having had the opportunity to work in technology, AI, data and analytics and driving organisational transformation, I wanted to work in an organisation where I can utilise my experiences effectively and I believe my current role in CIGNA is most ideal.

2. What is the biggest challenge you face within your role today and how are you looking to tackle it?

Many companies are prioritising their focus on data science and AL to stay competitive, which is great for the data and analytics function. However, as a Chief Data and Analytics Officer, it is important to help my stakeholders understand the reality, risks, opportunities and challenges to achieve favourable outcomes. As the Chief Data and Analytics Officer for Cigna International Markets, I cover over 30 countries and jurisdictions globally, my task of identifying and prioritising use cases becomes an interesting challenge, but a great opportunity to focus on what really matters.

It is natural that every country CEO or regional CEO wants to focus on use cases that would create value for their business.  However, it is equally important for the business leaders and my team to keep in mind the strategic priorities of each of our markets. My team has built a comprehensive use case prioritization framework that applies a number of key decision enabling filters specific to businesses and International Markets that helps us to make informed decisions by bringing good balance between business-specific priorities and our strategic priorities. We apply this framework by working closely with our business leaders and stakeholders in finalising the uses cases that we need to focus on. Any use case my team executes, it needs to ensure that the outcome is operational in the business with the support of the business and our partners, it is monitored and value creation is measured to demonstrate the ROI.

 3. What strategies do you employ to keep current in a technological environment which is rapidly changing and developing?

It is important to understand that today’s technology is tomorrow’s legacy and there is no silver bullet or a single vendor that would solve all challenges around data and analytics. A hybrid technology environment is a must. This is why it is very important to invest in developing a solid technology architecture to support a sound data architecture that that would enable data and analytics to drive business value. The data architecture patterns should be developed based on a clear set of requirements – business, technical, and capabilities, functional and non-functional and, should be independent of technology tools and products to prevent technology lock-in and be flexible.

Selecting a technology tool and trying to fit data architecture requirements is not an ideal approach. It is important to understand what the data and analytics requirements are, then design a technology architecture that supports the data architecture, and then identify what technology tools and products would enable the data architecture.  A solid baseline architecture foundation is a practical and sensible way to manage any changes in this fast-moving landscape.  

Ethics will play a very important role in determining the future of AI and Data Science and this is already a heavily discussed subject in recent years. You may be right complying from privacy and legal perspectives to use customer data in order to build products or solutions, but ethically it may not be the right thing to do. Ethics lies in the eyes of the beholder i.e. how organisations view ethics. Having access to customer data is a privilege customers have given us because they trust that we will use their data in an appropriate, acceptable and meaningful manner and therefore, it is important that we do not break that trust. It is equally important for organisations who are serious about creating value from or monetise on their data to have a comprehensive and well thought out data and AI ethics framework to make sound decisions. Organisations need to strike the right balance between ethics, privacy and value creation through innovation or data science of AI. Ethics should also be directly linked to the core values of the organisation in order to drive the right culture and mindset.  

In the past, I have designed and implemented such frameworks by applying social, ethical, privacy and legal lens before commencing any data science/AI initiatives. The Code of Ethics is the foundation for our interactions with our stakeholders and we take this matter seriously at Cigna.

5. What does a good data governance structure look like? What frameworks should be used?

Traditional data governance approaches and practices no longer are effective in the changing data-driven world, particularly with the increasing complexity of data in terms of volume, variety, value and veracity.  The term “Data Governance” is often seen by businesses as slowing exploitation of data to create value with processes and controls that are seen as policing or bureaucratic. 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. This, therefore, calls for “Smart Data Governance” that would enable the data 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.

Processes and technologies are the easy bits in data governance process. It is the people and their mind set around data governance that is complex and therefore, establishing an effective and efficient data governance practice is a cultural transformation in an organisation. Data governance should not been seen as ticking the boxes around privacy, legal and regulatory aspects around data.

If an organisation is serious about treating data as a key strategic and competitive asset, then it is important to implement data governance practices to ensure the lifecycle of the data asset is managed and analytics is just one component of the many components in the data lifecycle. Big bang approach to address data governance will not work and companies do not have time and money to invest to get value out of it. Here are some key points to consider when setting up data governance practice that is practical, pragmatic and purposeful:

• Start data governance practice by focussing on where data value creation happens
• Aim high, but build capability in an incremental manner – one step at a time.
• Set up data governance framework with an initiative first and take the business through the journey to demonstrate how data governance enables the business to create value.
• Focus on governing the critical data elements and its lifecycle that creates value. Not all data needs to be governed.
• Keep educating business stakeholders on the value data governance brings to the organisation by demonstrating how data governance is helping the data driven initiative
• Apply data risk lens as part of data governance framework by letting the business to make decision depending upon its risk appetite
• Have a data governance decision body that addresses operational data matters/issues/risks in an effective and efficient manner which means the agenda is 100% action oriented to drive value creation out of the data related initiatives
• Once the data governance framework demonstrates its value to the business, now expand its scope in an incremental manner.

Register below for free to hear Ram Kumar, live!