APPLYING PRODUCT MANAGEMENT PRINCIPLES T O RGA’S DATA & AI TRANSFORMATION

MAHA REINECKE

Could you share some key milestones in your career that led you to your current role as Director of Data Analytics and AI at RGA?

My journey began with my first job, where I worked with a development team in a public hospital in Singapore. We were tasked with building a digitally integrated hospital by implementing electronic health record systems.

I was involved in defining requirements and collaborating with stakeholders, while considering the kind of data we needed to collect to analyse hospital operations and support decision-making. This was before the term ‘data-driven’ became widely used. Over time, I transitioned from planning and implementation to analysing data, identifying trends, and providing insights that helped streamline hospital processes.

That experience made me realise the potential of data in driving efficiency and innovation. I saw that data analytics would play an increasingly critical role in the future, which led me to change my career path towards data & analytics – both through on-the-job learning and formal courses – and that led me to where I am today.

Were there any significant events that served as stepping stones to your current leadership position?

After leaving Singapore and moving to London, I started engaging with startups. It was a new market and environment for me, so I focused on understanding how companies operated in this region. Connecting with founders and startup teams helped me appreciate their approach to problem-solving and innovation, and I started to collaborate on small projects, assisting with data analysis to understand market trends better. Coming from a healthcare background, working for a data product company in the healthcare space was a logical and natural choice. My domain knowledge and experience in implementing solutions positioned me well to contribute effectively.

This exposure to the entrepreneurial and startup ecosystem was eye-opening. In Singapore, I hadn’t seen innovation at this scale, and moving to London broadened my perspective on application development and data-driven decision-making.

What inspired you to move from a startup environment to a corporate one?

I’ve always worked in small businesses. Even in the hospital setting, whether public or private, it was a localised environment. I transitioned from public to private and then to a product company. By the time I made that shift, I had spent about ten years in healthcare. I wanted to test a hypothesis: Are data skills truly industry-neutral? Can I pivot to another field, apply my skills, and pick up subject matter knowledge on the job? The answer turned out to be yes.

Additionally, having always worked in smaller companies, I felt I was missing the exposure to a corporate culture. That led me to financial services. My long-term vision is to work towards a COO role because I see a strong alignment between operations, data, and management. My career path hasn’t been linear; I didn’t graduate with a data science degree. Ten years ago, such degrees didn’t even exist. Everything I learned was through real-world business contexts and data applications. That background shaped my decision to shift to corporate.

At the same time, I still value and apply the agility and innovative mindset I developed in startups and small businesses. I think that perspective is appreciated in corporate environments, where large-scale projects can sometimes become bogged down in bureaucracy or long-established but ultimately outdated processes.

How have the challenges you’ve faced shaped your leadership style?

I’ve always found myself in greenfield opportunities – every company and project I’ve worked on involved building and implementing something new or testing new ideas. That experience has made me resilient but also enhanced my solutions-oriented mindset and the need to collaborate and convince stakeholders.

I’m also not afraid of failure. My approach has always been to find solutions using the best possible resources available. If something doesn’t work, I pivot. Change doesn’t feel disruptive to me because I see it as part of the process of innovation and problem-solving.

Could you give us an overview of RGA’s journey in digital transformation, within what is a fairly mature sector?

RGA is a reinsurance company, meaning we provide insurance to insurance companies. We’re a relatively young company in the insurance industry – only 51 years old – but we have grown significantly in the last five years. Our business volumes have expanded rapidly, and it’s important for our internal operations, including in data management, to keep pace.

During this growth phase, we realised that we successfully managed the expanding workload, and identified areas where we could optimise data processes. We saw potential to reduce fragmentation, streamline workflows, and establish clear ownership structures. It was as if we had all the right ingredients and were now ready to refine our recipe. This realisation inspired us to evolve our approach, focusing on increased coordination and strategic methodology to better serve our growing business needs.

As an insurance company, risk is our business. We want our time to be spent on deriving insights rather than managing upstream data processes. Currently, the reality in data is that 80% of the effort goes into

MAHA REINECKE

collection, storage, and transformation, with only 20% spent on insights. Our goal is to flip that ratio, or at least reduce the time spent on backend processes.

The key objectives of our data strategy are to standardise our data frameworks and address challenges related to people, processes, and technology with the goal to enable the business to spend more time generating insights rather than processing data.

We’ve invested heavily in technology and data governance, and it’s an ongoing journey. We continually reassess how to improve our organisational structure, attract more talent to work with data across the company, and ensure our approach caters to different business needs.

What have been the most significant advancements at RGA in terms of that journey?

After the enterprise data strategy was conceived and communicated, we began building the enterprise data platform. This platform is designed to enable self-service analytics, distributed ownership, and federated governance.

The platform is constantly evolving, which is a significant achievement. We’re moving away from data sitting in silos within individual business units. Instead, we’re enabling cross-domain analysis, reducing duplicative and repetitive processes. As a result, the number of data products we utilise is growing, with each product having a dedicated owner who ensures proper documentation and governance. And more and more business units are getting on board, eager to create and share data assets with their consumers.

We’re also strengthening our data governance practices, including developing an enterprise data catalogue and improving data quality management. And while we have developed a framework for distributed ownership, it is now about ensuring the business units adopt it effectively. For example, what does it mean to be a data product owner, a data steward? Since this role is in addition to a person’s day job, we need to define responsibilities clearly and support those taking on these governance roles.

We want our time to be spent on deriving insights rather than managing upstream data processes.

Do you have different classes of data products at RGA?

There’s a lot of debate around what constitutes a data product but we haven’t imposed strict definitions. Currently, most data products are tables or data assets published in our marketplace or access repositories, allowing consumers to request access.

Beyond that, these data assets can be leveraged for analysis, and dashboards or reports built from these

assets can also be considered data products. We focus on providing the foundational building blocks that enable various forms of data utilisation.

Some users are data-savvy and require direct access to datasets, provided the right governance and security controls are in place to ensure appropriate data access. Others need automated pipelines where data flows seamlessly into dashboards, allowing them to monitor changes without handling raw data themselves. Since every business unit has different needs, our goal is to support all these use cases effectively.

How has your experience as a product manager influenced your approach to data and AI at RGA? I always say I am a data analyst first, who then gained exposure to product management. The way the industry has evolved has allowed me to merge both skill sets. My background in chemistry involved analysing large datasets, and that analytical mindset carries over to my work in data and AI. Product management, on the other hand, is a methodology I’ve learned that helps me apply structured thinking to my work.

One key principle I follow is not relying on a single perspective. Whether it’s interpreting data, designing an architecture, or structuring a workflow, I always compare multiple approaches before deciding on the best path forward.

Another important rule I follow is to never act impulsively. When people come with urgent requests, it’s easy to react immediately, but I’ve learned to apply reasoning first. Impulsive actions often stem from inadequate knowledge, so it’s crucial to take a step back and assess the full context before making decisions.

The concept of a data product also influences my approach. Unlike a project, which has a defined endpoint, a product must continuously deliver value at scale. Whether I’m creating a dashboard, sharing data assets, or designing tables, I always think about its role within the broader business and data ecosystem. Understanding the end users and their needs is critical – rather than just executing tasks, I focus on what they’re ultimately trying to achieve.

Effective data and AI strategy isn’t just about technology and tools – it’s also about streamlining processes. Technology provides the necessary uplift, but true value comes from the intersection of technical expertise, domain understanding, and user empathy. That overlap is where real impact happens.

Are there any specific methods or frameworks from product management that you’ve found effective in your current role?

Prioritising and breaking down the problem is key. One framework I frequently use is the ‘five whys’ methodology, originally developed by Sakichi Toyoda, the founder of Toyota Industries. Asking ‘why’ five times helps uncover the root cause of a problem. Often, when people present multiple issues, they all stem from a single underlying cause – such as an incorrect data model or poorly structured data.

By addressing the root problem, I can implement a single solution rather than creating multiple workarounds. Always ask ‘why’ multiple times to determine what is truly important versus simply lifting and shifting an existing process.

User empathy is also crucial: who are the consumers, what are their needs, and how will they use the product? Will it effectively serve multiple users rather than just one? The goal is to develop and deliver at scale while reducing variation.

Often, when people say they want to move to the cloud, they see it as a project. But from a product management perspective, moving to the cloud is just a step, not the goal itself. My focus is always on the underlying problem – are we aiming for better compute and storage, or is the goal improved data accessibility and performance efficiency?

Another key approach is reviewing, refining, and iterating. Work in increments rather than committing to large-scale, long-term projects. Traditional financial institutions often plan three-to-five-year initiatives, whereas I normally work within a six-month roadmap. This approach allows me to be agile and adapt to new developments and innovations in the industry.

How has your product management background influenced how you prioritise projects within RGA’s initiatives?

This is where business context becomes crucial –aligning projects with business KPIs. It’s not about how many products I’ve published or how many requests I’ve serviced, but whether I’ve delivered a product that moves a business metric.

For example, if a team’s main challenge is inefficient data processing – spending too much time gathering, transforming, and storing data – then improving that workflow becomes a priority. Rather than focusing on arbitrary output metrics, I align my work with broader organisational goals.

Last year, RGA introduced a refreshed strategy that includes key elements such as an enterprise-first mindset, adaptability to change, and enabling worldclass functions (HR, administration, operations, data, and technology), which resonates deeply with me. My work directly supports these objectives.

Can you share an example where your product management approach directly contributed to the success of a data or AI project at RGA?

One project involves addressing fragmented data sources and redundant processing. We found that multiple teams were using the same data source but

generating several outputs. That inefficiency prompted me to investigate. Who are the consumers, what do they truly need, and why are so many versions necessary?

By analysing these issues, we realised that the data model itself could be simplified. Instead of running separate queries for each request, we are working on exposing data at a more granular level with proper governance and access controls. This way, users can generate their own outputs dynamically rather than relying on predefined reports.

This shift aligns with our broader goal of self-service analytics – enabling users to query and interpret data independently rather than submitting repeated service desk requests. The aim is to reduce manual ad hoc reports and streamline operations, freeing up the development team from handling routine data dumps and requests.

This project’s shaping to be a major step towards making data more accessible while optimising internal workloads.

How does your team handle the translation of data insights into strategic business decisions? Has your product management background influenced this process?

One key principle I follow is ensuring that my development team produces an outcome that allows the end user to enhance their decision-making ability. We do not want to build something simply because it was requested or has been used in the past, but we engage with the business domain experts and subject matter specialists early on to ensure we understand what they are trying to achieve. You could call it ‘beginning with the end’.

Using agile practices, we ensure that the solutions we develop remain modular and can easily adapt to changing business needs. Since business requirements evolve, having this iterative process helps us refine solutions within a short timeframe. Ultimately, we aim for the end user to be in a better position to gain data insights that will then translate into business decisions.

As part of RGA’s transformation efforts, you set up an internal Data Skills Academy. What motivated that initiative?

When I joined RGA, we were already a few years into our enterprise data strategy. Significant investments had been made in tools and technology, but we needed to increase awareness and adoption across the company. Many employees were unfamiliar with the tools and felt intimidated by them.

Having previously developed a similar program in another organisation, I saw the need to create a structured approach to upskilling employees. Without proper training and support, these investments would remain underutilised.

Some employees showed a strong interest in learning how to use these tools but lacked the guidance to do so effectively. Many found traditional upskilling methods – such as one-day workshops – too disconnected from their actual work. To bridge this gap, we designed a program that aligns directly with their job functions, providing hands-on learning opportunities.

By building the Data Skills Academy, we aim to help employees adopt and integrate these tools into their workflows, ultimately driving the success of our enterprise data strategy. This initiative ensures that our workforce is empowered to leverage data effectively, rather than relying on fragmented or siloed approaches.

Was the Academy aimed at certain roles or business areas?

The Academy was developed for everyone. Using my product management approach, I engaged with users across different business functions – leaders, middle management, and analysts – to understand their challenges, experiences with tools and technology, and learning preferences.

Through these conversations, I identified different user personas within RGA, recognising that each business unit has unique needs. This led to the development of an operating model that aligned with our enterprise strategy, which emphasises a winning mindset and the need to embrace change.

The core pillars of the operating model focused on curating tailored learning pathways based on user personas and job roles. The goal was not simply to have a certain number of employees learn Python by 2026. Instead, the focus was on providing content that aligned with their daily work and RGA’s technology stack.

For example, if someone primarily used Excel, the objective was not to push them towards Python but to enhance their skills with Power Pivot and Power Query to automate repetitive tasks. Many users were unaware of these built-in Excel features, which could significantly improve their efficiency.

For those with some coding knowledge, the focus was on advancing their skills – learning CI/CD, source control, and GitHub, ensuring they could leverage existing tools effectively.

Additionally, many actuaries, particularly those in the process of qualification, had backgrounds in mathematics, computer science, or econometrics, giving them some exposure to programming. The Academy allowed them to apply their academic experience at a practical level within their job roles.

Ensuring that our content and training providers catered to these varied personas was a crucial element of the Academy’s design.

What impact has the Academy had on the transformation journey as a whole, and have you been able to measure that impact?

Because it’s early days since we have implemented the Data Academy in RGA, most of the data we are collecting is related to user behaviour such as active user, time spent, courses completed, etc. That gives us some base level KPIs, which will help us improve the content and program as a whole. In addition, we collect qualitative data in the form of one-on-one feedback sessions, surveys, etc. to gain real user insights into how the platform is helping RGA colleagues.

For example, someone mentioned taking an Alteryx course on DataCamp and using that knowledge to refine a workflow. What previously took a full day of manual manipulation in Excel now takes just a few minutes in Alteryx. That’s a tangible success story.

I’ve also received messages from employees who appreciate the tools and training. Many have realised that learning about data isn’t just about coding; it’s also improved their ability to write better prompts for our internal generative AI tool. This has helped them get more accurate and useful responses to their enquiries.

We’re still early in this journey. Last year’s small pilot programs demonstrated strong demand and clear benefits, so this year we’re expanding the program enterprise-wide, with a focus on scaling the initiative and formally measuring its impact. Our goal is to align the Academy’s success with the broader enterprise data strategy and quantify the value of investing in data upskilling.

Looking back, what advice would you give to your younger self at the start of your career?

I would say to put myself out there more – beyond just my day job. Engaging with the professional community in my field would have been beneficial.

Also, I’d tell myself to take more risks and seek out opportunities earlier. I used to think I had to achieve certain milestones – another qualification, more experience, or a specific project – before pursuing what I really wanted.

Instead of letting these self-imposed barriers hold me back, I should have trusted my ability to navigate challenges. Because when faced with difficulties, I’ve always found a way to overcome them through diligence, persistence and perseverance.

By building the Data Skills Academy, we aim to help employees adopt and integrate these tools into their workflows, ultimately driving the success of our enterprise data strategy.