How to Build Your Insurance Data Strategy Before Your Competitors Win

Transform your operations with an insurance data strategy. Learn why legacy approaches fail and discover the road map to real-time analytics, AI automation, and competitive advantage.

In brief:

• Insurance companies need an insurance data strategy to keep up with digital transformations and advancing competitors.
• Break down legacy silos and accelerate transformation by building cloud-native, API-first architectures and choosing platforms with built-in compliance and security.
• Boost efficiency and agility by adopting self-service analytics tools and automating manual processes with AI.
• Drive success by defining clear business outcomes, engaging teams early, and proactively planning for change management and data literacy programs.
• Create a data-first culture by aligning leadership, setting clear goals, and encouraging everyone to challenge outdated ideas.

Digital transformation arrived fast with bold promises, but many insurers still struggle with broken data systems that hold them back. Insurance companies invested in data visualization, data acquisition, and data storage with hopes of executing sophisticated data-driven strategies that predict patterns and develop differentiation to set them apart from competitors.

But the reality is often different: outdated technology, disconnected systems, and teams that don’t know how to use business intelligence tools.

When it comes to underwriting, fraud detection, and customer experience, agencies are developing modern insurance data strategies, acknowledging where traditional approaches fall short, and exploring how predictive analytics can finally deliver on the original promise of big data.

Why Insurance Data Strategies Fail

Most insurance data modernization plans fall short of delivering their intended value. The culprits? Legacy systems, organizational silos, and even outdated skill sets that haven’t kept up with the pace of change.

Legacy Systems Create Data Silos

Traditional insurance data architectures were built for a different era. These systems store information in rigid formats that made sense when reports were generated monthly and decisions could wait days.

Today’s insurance companies need real-time insights and instant responses.

Legacy systems create “data prisons,” environments where valuable information exists but remains inaccessible for modern analytics.

Siloed Organizations Mirror Siloed Data

The technical challenges mirror organizational ones. Many insurers operate with separate teams for analytics, data warehousing, infrastructure and quality assurance. Each team has different priorities, timelines and success metrics, but they all have the same goal: to streamline operations. All these silos slow down transformation, turning simple requests into complex processes.

Slow Project Timelines Can’t Keep Up With Digital Transformation

Historically, insurers created data initiatives as massive, multiyear enterprise projects. These waterfall approaches require months, if not years, of planning and development, and they are often outdated before they’re even launched. Process transformation needs to happen in weeks or months — not years.

“The more time you spend planning things, the further behind you’re going to get,” says Sean Neben, national insurance practice lead at Centric Consulting. “We’re in the takeoff stages of exponential technological change. Companies are struggling to keep up with the pace of change.”

Skills Gaps Keep Data Unusable

Many team members, especially in nontechnical roles, don’t know how to ask for what they need or edit and create data reports and visualizations.

“The biggest obstacle to insurers with their data strategy is the people. It’s not the technology,” Neben says. “If your users aren’t data literate enough to know what questions to ask of their data, you’re not going to succeed.”

What a Modern Data Strategy Looks Like

Instead of rigid, planned structures, modern insurance data strategies are flexible, fast and aligned with business outcomes. They’re also compliant, secure, and easily accessible with the right safeguards built in. These modern data strategies:

1. Focus on Real-Time Analytics

Modern strategies support immediate decision-making by emphasizing real-time or near-real-time processing. This is in stark contrast to traditional data approaches that use batch processing and overnight runs to prepare yesterday’s data for today’s decisions.

2. Build Cloud-Native, API-First Architecture

Modern data insurance platforms use cloud technology that can automatically adjust to handle varying levels of data as needed. Businesses use pay-as-you-go cloud plans for computing power and usage instead of paying upfront for massive servers that spend more time sitting idle and collecting dust than being used.

Cloud-native, application programming interface (API)-first architecture ensures all systems can communicate clearly. For example, an underwriting system might immediately access relevant data when a claim is submitted.

Additionally, more usable storage formats enable teams to build targeted solutions in weeks or days, which significantly reduces the time and cost of developing new analytics.

3. Choose Platforms With Compliance and Security Built In

Modern insurance data platforms incorporate compliance and security from the outset by automatically categorizing information based on its sensitivity, applying the appropriate security measures, and maintaining a revision log.

4. Buy Self-Service Tools That Cut IT Bottlenecks

Modern platforms enable business users to build their own analytical solutions without constant information technology (IT) involvement. Tools like Microsoft Fabric allow data analysts to build pipelines, create data marts, and develop reports within a single environment.

“You’ve got a data analyst who’s working in Fabric. They can build the pipelines, some marts, some cubes, and the reporting Power BI in one environment,” Neben says. “That’s super powerful when you think about being agile and being able to respond to the business in real time.”

Now that we’ve explored what these modern data strategies look like in practice, let’s discuss how to build them in your organization.

How to Create a Better Insurance Data Strategy Using Analytics

A transition is always exciting, but it’s not always easy. Here’s a clear road map for transitioning from legacy insurance data approaches to a modern, agile insurance data and analytics platform.

1. Identify Manual Processes That Slow Down Core Workflows

Identify where people spend the most time to compensate for bad tech. Each manual step represents both immediate cost and opportunity cost. Time spent on data preparation is time not spent on analysis and decision-making.

Take a look at a sample workflow below:

• Employees enter data manually between systems that should communicate automatically.
• Analysts spend hours preparing data instead of analyzing it.
• Underwriters re-create information that exists elsewhere in the organization.
• Claims adjusters make decisions without a complete customer history.

These manual processes are ripe for automation.

2. Use AI to Automate Underwriting and Claims

To avoid spending employees’ time on routine work, explore artificial intelligence (AI) and automation. Modern systems can:

• Extract information from unstructured documents like medical records and repair estimates
• Flag suspicious claims patterns for investigation
• Suggest underwriting adjustments based on real-time risk assessment
• Automate routine processing to free up human expertise for complex cases

3. Begin Your Project Plan With Outcomes

Instead of tool shopping, focus on problems within specific business outcomes.

“A lot of insurance companies don’t have a very well-articulated business strategy, which makes it hard to do data strategy well,” Neben says. “Without that kind of guidance in terms of the business strategy, you can’t really measure how effective a data strategy is.”

To build your project plan around business goals, ask questions like:

• How quickly do we need to respond to competition?
• What customer experience improvements would most impact retention?
• Where are we losing money due to inefficient processes?
• Which regulatory requirements pose the most significant compliance risk?

4. Engage Teams Early

Avoid building in isolation and then wondering why adoption struggles. Instead, involve business users throughout the design and development process.

“You can have a business user sitting next to you while you build analytics,” Neben says. “That’s unheard of if you were using old technology platforms.”

5. Proactively Plan for Change Management

Data governance remains one of the weakest links in most insurance agencies because it requires collaboration across departments and navigating political issues related to data ownership.

“Data governance is super important, but it’s super hard to get right,” Neben says. “You need strong leadership around data to have an effective data governance in your organization.”

Change management success requires:

• Support from top leaders
• Clear definition of who is responsible for what
• Regular meetings with different teams to discuss data and plans
• Training everyone in the organization to understand how to use the data

6. Explore Data Literacy Programs for Continuing Education

Cultural change always takes time, but it’s critical for long-term success.

“Every organization should have a data literacy program,” Neben says. “Basic data literacy, such as how to build a simple SQL query. Just the basics.”

Common Ways Insurance Data Strategies Can Fail

Success requires good technology, but it also demands good process and strong change management. Here’s how to avoid common mistakes when transitioning to a new insurance data management and analysis process:

• Define Clear Outcomes First: Clearly define the business outcomes you want to achieve before choosing tools or partners.
• Engage Business Users Early: Involve business teams throughout design and development to ensure solutions meet real needs and drive acceptance of new tools and processes.
• Plan for Change Management: Gather support and budget to provide training and enablement support — not just for data itself, but for navigating new processes and platforms.
• Align Leadership: Executives should work closely together to ensure plans for technology, daily work, and long-term goals are all balanced.
• Create a Data-First Culture: Ensure everyone understands data, set clear goals, and encourage people to challenge old ideas.

Moving Toward a Data-Driven Future With Centric

What worked 10 years ago doesn’t work today, and it certainly won’t work in five years. Companies that don’t modernize their data strategy now risk losing their competitive edge, churning customers, and losing out on revenue. It’s easily understandable why traditional data approaches fail in the modern day.

Still, with the right planning, change management, and expert partners, you can turn your biggest data challenge into your biggest competitive advantage.

Take a look at Centric Consulting’s insurance analytics approach for a more detailed breakdown, and talk with our team today to start your journey into modernizing your data. Contact us