By Actuary List on 30 Mar 2025
In today’s fast-paced, digital world, data is everywhere. But data by itself doesn’t help much—it's what you do with that data that really makes a difference. That’s where data analytics comes in.
For actuaries, businesses, and analysts alike, data analytics is becoming essential. It helps us uncover insights, improve decision-making, and stay ahead of the competition. This post will walk you through what data analytics is, why it’s important, the types of analytics, and how you can actually apply it in real-world situations.
Why Data Analytics Matters
Data analytics isn’t just about making things more efficient—it’s a way to drive innovation and uncover opportunities you might have otherwise missed. Businesses use it to:
- Understand customer behavior
- Spot trends early
- Create better products and services
- Stay ahead in competitive markets
And for actuaries, it’s become a key part of modeling, pricing, and strategic planning.
The Different Types of Analytics (Made Simple)
There are a few main types of data analytics you’ll hear about:
- Predictive Analytics – This looks at past data to help you make future predictions. For example, forecasting how likely a customer is to file a claim or leave a service.
- Prescriptive Analytics – This one goes a step further and actually recommends actions to take. It uses machine learning and algorithms to suggest the best outcomes.
- Cognitive Analytics – Think of this as analytics that "thinks" like a human. It uses AI tools like natural language processing (NLP) and large language models (LLMs) to understand unstructured data like emails or chat logs.
It’s Not About the Tools—It’s About the Job
A lot of people get stuck debating which tool is “best”—R vs Python, Excel vs everything else. But that’s missing the point.
The tool should fit the task, not the other way around.
Here’s what we’ve found works:
- Excel is still great for building clear, traceable models.
- R and Python are better for more advanced analysis and working with big data.
- Learn what you need, go deep into a few tools, and skip the trend-chasing.
What Actually Goes into Data Analytics
Most of us imagine that analysts and actuaries spend all day building models. In reality, most of the work is spent getting the data ready. That includes:
- Collecting data – From websites, customer behavior, systems, or devices.
- Cleaning it up – Fixing errors, filling in gaps, and making sure it's accurate.
- Analyzing it – Using methods like regression or clustering to find patterns.
- Visualizing it – Creating charts and dashboards that make sense to everyone.
- Interpreting it – Turning insights into actions and decisions.
Why Just Hiring a Data Scientist Isn’t Enough
Here’s something we’ve seen over and over: companies hire a data scientist but forget to build the structure around them.
- The data is missing, or all over the place.
- There’s no one to help collect or clean it.
- Expectations are high, but support is low.
This is why having data engineers is so important. They build the pipelines, collect the right data, and make sure everything flows smoothly so the analysts can actually do their job.
Automation and AI Are Changing the Game
As companies mature, tools like:
- RPA (Robotic Process Automation)
- AutoML (Automated Machine Learning)
- LLMs (like ChatGPT)
...are making it easier for analysts and actuaries to focus on the important stuff—like asking the right questions, interpreting data, and making a real impact.
You can now get more done with less time, less manual work, and fewer resources.
What Actuaries Bring to the Table
Actuaries are in a great spot when it comes to data analytics because they combine:
- Strong math and statistics skills
- Programming knowledge (R, Python, VBA)
- Business understanding and communication skills
They don’t just analyze data—they translate it into real business decisions. That’s incredibly valuable.
Ethics: The Side of Analytics We Can’t Ignore
Here’s where things get serious. As data gets more powerful, we have to be more responsible with how we use it.
There have been cases where insurers used hundreds of personal data points—like the browser someone used or the time of day they were browsing—to adjust insurance quotes. Some of these weren’t even related to risk.
Other cases show how credit scores can unfairly impact certain groups, limiting access to loans or insurance just because of where they live.
This creates what’s known as a “poverty premium”—higher prices for people who are already struggling.
That’s why companies must take ethics seriously. Be transparent, follow regulations, and use data responsibly.
Real-World Uses of Data Analytics
Data analytics isn’t just for tech companies. It’s already making an impact in almost every industry:
- Business – Personalizing marketing, managing supply chains, improving customer service
- Healthcare – Predicting outbreaks, optimizing treatments, managing hospitals
- Finance – Detecting fraud, managing investment risk, scoring credit
- Sports – Tracking player performance, planning game strategies
- Government – Improving public safety, planning cities, managing disasters
Curious to explore how actuaries are contributing in each of these industries? Check out our Actuary Jobs by Sector to see where your skills could take you.
The Future: Real-Time Analytics and Edge Computing
As more devices connect to the internet (IoT), analytics is shifting toward real-time insights. Instead of waiting hours or days to analyze something, businesses can now make decisions in seconds.
And with edge computing, data is analyzed closer to where it's generated—making things faster and more efficient.
This means actuaries and analysts will need to keep learning and adapting. The field is moving fast, and those who stay curious will thrive.
Some Practical Advice for Companies Starting Out
If your company is just getting into data analytics, here’s what we’d suggest:
- Start small – Don’t try to do everything at once. Build quick wins.
- Don’t micromanage – Let your analytics team pick the tools they know best.
- Be patient – Value from analytics takes time to show.
- Set clear goals – Know what you're trying to learn or solve.
- Build infrastructure – Invest in data engineers and clean systems.
- Avoid chasing shiny tools – Focus on outcomes, not trends.
Wrapping Up: Why This Matters for Actuaries
Data analytics is no longer a “nice to have”—it’s essential. It helps actuaries become more effective, insightful, and strategic. It also opens doors into new career paths like:
- Data science
- AI modeling
- Risk consulting
- Insurance tech (InsurTech)
If you're looking to grow your career or explore something new in the analytics space, there are plenty of actuarial roles out there for you. Whether you're interested in data science, AI, or working with tech-driven companies, this field is full of exciting opportunities. You can browse actuarial jobs that focus on analytics and find something that matches your skills and goals.
