A few weeks ago, we sat down with the data leader of a traditional insurance company. A company with a long history, solid, and a successful business. But his concern was clear: “I have good ideas, I know we could be doing more with data… but I feel like we’re tied down.”
He told us they still use Excel for many critical tasks. They have dashboards in Qlik, but hardly anyone looks at them 👀. The technological infrastructure is outdated. And above all, he feels he doesn’t have enough resources to tackle the implementations he wants to undertake.
This isn’t an isolated case. It’s something we hear a lot from traditional companies that know they need to modernize but face technical, organizational, and budgetary constraints.
With this in mind, and based on our experience, we created this roadmap, which obviously begins with a theoretical perspective but aims to provide a guide to support those leaders who want to move forward but need a strategic, realistic, and actionable plan.
First of all, the diagnosis: Where do we stand?
Before making changes, we need to clearly understand where we’re starting from. In many traditional insurers, we find:
- Underused analytical tools.
- Legacy infrastructure (such as legacy SQL Server or ETL processes in Integration Services).
- Difficulties in accessing real-time data.
- Technical teams overwhelmed by operations 😩
The first thing is to make a complete diagnosis, which includes:
- Mapping of current architecture.
- Audit of the actual use of the data.
- Team capabilities assessment.
- Competitive benchmark against more modern insurtechs and insurers.
It’s not all about technology: connecting with the business is key 💼
The leader we spoke to had a clear intuition: data can help improve business, but we need concrete direction.
And he was right. Data strategy shouldn’t exist in a vacuum. It must be 100% aligned with objectives like:
✔ Reduce the accident rate with better risk models
✔ Improve customer segmentation
✔ Identify cross-selling opportunities
✔ Accelerate the launch of new products
✔ Automate processes to reduce costs
✔ Enhance the digital customer experience
And now yes: modernize the infrastructure without “tearing everything down”
We know there isn’t always the budget or internal muscle for a complete migration. Therefore, we propose gradual and realistic options:
➊ Upgrade your existing Microsoft stack: migrate to modern SQL Server, Azure Data Factory, etc.
➋ Evaluate the cloud (Azure, AWS, GCP) to scale capabilities if you haven’t already.
➌ Design a hybrid architecture: keep what works, modernize what’s necessary.
➍ Incorporate a data lake and modernize the data warehouse.
➎ Migrate to more intuitive visualization tools like Power BI, Tableau, or Looker, but with meaningful, consistent, and usable tools.
But it’s not done alone: you have to put together a team that can make it happen 👥
The leader we met had only one data engineer on his team. And he felt like everything went through him. This doesn’t scale.
For a real transformation, the team must cover these profiles:
🛠️ Data Engineers
🧪 Data Scientists
📊 Business Analysts
📈 Visualization Specialists
We know that building this team from scratch isn’t always possible. That’s why we often work with mixed models: internal talent + specialized partners + in-house training.
Where do we start? Use cases
When budgets are limited, prioritizing carefully is key. We suggest starting with high-impact, low-risk use cases:
- Dynamic pricing models.
- Fraud detection.
- Customer churn prediction.
- Product recommendations.
- Automation of critical reports.
- Chatbots and virtual assistants.
The important thing is to show quick and tangible value to gain traction.
And don’t forget data governance and culture.
Transformation isn’t just about technology: it’s also about culture. That leader told us, “Sometimes I feel like even if we had the best tool, they still wouldn’t use it…”
That is why we propose to work in parallel on:
- Define clear data governance rules.
- Train the teams.
- Promote the use of data in real-world decisions.
- Communicate results and quick wins constantly.
Innovate like an Insurtech (from the experience of a traditional insurer) 🧬
With a modern infrastructure and a data-driven culture, the door is open to new business models:
- On-demand insurance
- Usage models (pay-as-you-drive)
- Real-time customization
- Integration with external services via APIs
- Collaboration with startups in the insurtech ecosystem
Finally, the proposed roadmap!
We know that time and budget are finite. That’s why we suggest dividing the transformation into phases:
| Phase | Estimated Duration | Main Objectives |
| Phase 1: Foundations |
0-6 months |
|
| Phase 2: Construction |
6-18 months |
|
| Phase 3: Innovation |
18-36 months |
|
Bonus: Don’t forget to measure to learn
As we always say: what is not measured, is not improved.
- Has the loss ratio been reduced?
- Were the renovations improved?
- How many users use the new tools?
- How long does it take to generate a report today vs. before?
And just as important as measuring: continually adjusting.
Transformation is not a project, it’s a process.
And you, as a Data Leader in your company or department, how are you driving change and solving problems?
At Tekne, we help technology leaders implement their transformation strategies to take their business to the next level. Don’t hesitate to send us a message.