Making Data Actionable: Overcoming Key Challenges in Insurance Agencies

Data is often referred to as the “new oil,” but much like oil, it requires refining before it becomes valuable. In its raw form, data can be messy, inconsistent, and overwhelming. Turning it into actionable insights that drive decisions and outcomes isn’t magic—it’s a process. Here’s a look at four major challenges that we need to address to truly make data work for us. One of the main issues is resetting the expectations and timelines of what data should do. Its no easy button - it takes time, work and commitment to make data work for an agency.

1. Understanding the “Why” Behind the Data

If we don’t know why we’re collecting data, we’re setting ourselves up for wasted effort and confusion. The purpose has to come first. Without clarity on the goal, we risk wandering aimlessly through a sea of numbers.

It’s also critical to create a unified definition of key terms and metrics so everyone is on the same page. Are we talking about “written” premiums or “estimated”? Does “in-force” mean active policies or something else? For data to drive decisions, we need a lexicon that works for the culture and the agency—a shared understanding that removes ambiguity and fosters collaboration.

• Why do we want this data? What’s the goal?
• Is it a one-off request or part of a larger strategy?
• Are we aiming for better decision-making, tracking progress, or solving a specific issue?
• Have we established a clear and agreed-upon definition of critical metrics?

When we can articulate the "why" and agree on what we’re measuring, we ensure the data serves us—not the other way around.

2. Access: Can We Get the Data?

Having a goal is great, but it means little if the data we need is inaccessible or unreliable. Data access is often the first technical hurdle. Whether it’s locked in outdated systems, inconsistently populated, or updated too infrequently, these challenges can derail the process.

• How accessible is the data? Is it live, dynamic, or stuck in static files?
• What’s the quality like? Is it accurate, consistent, and well-populated?
• Do we have all the necessary components—policy details, billing data, etc.?
• Are people using the system as intended, or are there gaps in data collection?

The best insights start with great data. And great data starts with understanding how it’s stored, managed, and accessed.

3. Cleaning and Consolidating

Raw data is rarely useful as-is. It’s often incomplete, inconsistent, or riddled with errors. The key is to refine it, correct the issues, and make it usable for decision-making. And understand its limitations.

• Can we centralize the data and address its inconsistencies?
• If perfection isn’t possible, can we grade it to find the most reliable parts?
• Do we need to normalize or adjust the data to make it more actionable and accurate?

This step is like cleaning a house—tedious but critical. The cleaner and more organized the data, the easier it becomes to extract meaningful insights.

4. Delivering Data to the End User

Data is only as good as its ability to reach the people who need it. If users can’t easily access, understand, or apply the insights, the data loses its value. Delivery matters just as much as collection.

• Is it presented in a way that’s intuitive and secure?
• Can users quickly access what they need, when they need it?
• Does it offer more than just graphs? Does it solve problems and provide insights that make sense?

The best tools don’t just push information; they create a seamless experience for users, helping them solve real problems and make informed decisions.

5. Empowering Users

Even the best data tools and systems fail if the people using them don’t understand their value or can’t connect the dots. Too often, users are thrown into the deep end without the necessary support or context to make data meaningful in their roles.

• Do users have enough time to understand and use the data in their roles?
• Does it help them in meaningful ways? Are we explaining the "why" behind what they’re looking at?
• Often, we skimp on helping users connect with the data, and they end up disengaged. If the data doesn’t reflect their world, they won’t care about it.

Empowering users isn’t just a technical challenge; it’s a cultural one. It requires a commitment to training, support, and ongoing collaboration.

The Final Thought

Making data actionable is a journey, not a destination. Each step—understanding the “why,” ensuring access, cleaning and refining, and empowering users—builds on the last. When done right, data isn’t just a tool; it’s a strategic asset that can transform decisions, operations, and outcomes.

But let’s remember: data is there to serve us, not overwhelm us. When we take the time to tackle these challenges head-on, we can truly unlock the power of data and make it work for the people who need it most.