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Case Study: Paragon Insurance Holdings
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Enabling Advanced Insurance Analytics and Reporting
Enabling advanced insurance analytics and reporting is challenging but highly rewarding—and if you want to improve your bottom line and get a leg up on your competition, then it’s a must for 2025.
This article explains the importance of data analytics for insurance companies, why it’s challenging, and how you can enable advanced insurance analytics and reporting with modern data solutions.
Topics Covered:
- The importance of data analytics for insurance companies
- What is advanced insurance analytics and reporting?
- How advanced analytics and reporting help all industries
- Why is advanced analytics and reporting so difficult?
- Why insurance companies need modern data solutions
- Two different data approaches for advanced insurance analytics and reporting
- Should you use a data warehouse for advanced insurance analytics reporting?
- Examples of insurance KPIs
- Preparing to design your data warehouse for insurance analytics
The Importance of Data and Analytics for Insurance Companies
Analytics are nothing new to the insurance industry. Long ago, successful insurance companies mastered the use of statistical analysis to assess the probability of loss and price their risks accordingly.
Despite these advances, most teams today (from insurance carriers to agencies to third-party administrators) lack the information, skills, and tools they need to power modern, advanced insurance analytics and reporting. In the long run, this means they’re missing the opportunity to transform their business into a truly optimized, data-driven organization.
What Is Advanced Insurance Analytics and Reporting?
Advanced insurance analytics and reporting can be a confusing topic, but the underlying concept is quite simple.
With advanced insurance analytics and reporting, you give every employee in your organization the information they need to make fact-based decisions in real time. This empowers employees to ask questions about data and better understand the impact of their actions on performance. Most importantly, they can make data-driven decisions without needing technical skills or resources.
How Advanced Analytics and Reporting Help Your Business—No Matter the Industry
The specific key performance indicators (KPIs) you track with advanced insurance analytics and reporting depend on your organization, your employees’ specific roles, and your unique needs and challenges. But across the board, implementing advanced analytics and reporting gives your insurance company consistent advantages over your competitors.
Why? Because no matter what line of business you’re in, having the ability to make fact-based decisions in real time is the foundation of success. This is true whether you’re working in insurance, healthcare, finance, supply chain, or other industries. Don’t just take it from us:
- Forrester’s report on The State Of The Insights-Driven Business revealed organizations with advanced insights-driven business capabilities are eight times more likely to outgrow their competition by 20%.
- A report from McKinsey shows that data-driven organizations report above-market growth and increased EBITDA (earnings before interest, taxes, depreciation, and amortization) up to 25%.
By giving stakeholders easy access to data, advanced reporting solutions shine a light on business processes and performance issues. In turn, this allows managers to identify missed opportunities and specific areas of improvement where they can take action.
Bottom line: Using analytics and reporting to implement targeted, data-driven changes enables organizations to improve their bottom lines.
Insurance companies are no exception. If you want to outperform your competition, start by implementing advanced analytics and reporting.
Why Is Advanced Insurance Analytics and Reporting So Difficult?
At one point or another, all companies face challenges with analytics and reporting. And at the end of the day, all challenges stem from the same root cause: The necessary data exists to solve your problem, but you can’t access or consume it.
Does Power BI Help with Analytics and Reporting?
Modern reporting tools, like Power BI and Tableau, make data easy to visualize. However, their resulting reports are only as good as the data that’s being fed into them. If you put unorganized data in, then unorganized (and unhelpful) reports come out.
In order to create high-quality reports that empower your organization to make accurate, evidence-based decisions that improve your bottom line, you need high-quality data.
Unfortunately, reporting tools alone can’t solve your reporting challenges….
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Data Collection Is More Complex Than It Seems
How do you collect high-quality data that can power impactful, actionable reports? It should be a simple process:
- Source all data from internal systems, partners, and third parties
- Synthesize all data into something useful
Unfortunately, there are many obstacles wrapped up in these two steps. For example, before you can use data to produce accurate, meaningful analytics and reports, it may need to undergo one or more of the following processes:
- Movement
- Integration
- Aggregation
- Grouping
- Filtering
- Cleansing
Moreover, as the amount and complexity of your data grows, these processes become more difficult and your reporting needs become more advanced. Without dedicated data analytics expertise, this two-step process quickly becomes a confusing, multi-step challenge.
Data Collection Is Incredibly Time-Consuming
Another reason insurance organizations struggle to produce report-ready data is because the data collection process is labor- and time-intensive.
In one study surveying 456 data practitioners and leaders, more than half of respondents say “organizing data sets for analysis” is their number one task. Meanwhile, 57% call maintaining data quality one of their biggest obstacles, up from 41%.
Anyone who tries to collect raw data and prepare it for analysis (e.g., to calculate a loss ratio by accident year) will quickly realize that advanced insurance analytics and reporting is much more complex and time-consuming than people immediately realize.
To Drive Advanced Analytics and Reporting, Insurance Companies Need Modern Data Solutions
Every data problem starts with a data solution.
As stated by Kerry Small, former Global Head of Commercial at Vodafone, in Harvard Business Review’s The New Decision Makers report, “It all starts with the data. If you don’t have the right structures in the beginning, it’s going to be difficult.”
In your insurance organization, you already have all the data needed to generate advanced insurance analytics. The problem is that this data isn’t report-ready. In other words, it’s raw, unstructured, or even incomplete.
To make data report-ready, you need to write and automate processes that re-engineer the data nightly (or more frequently, as needed). These processes transform your raw, unstructured data into a new, validated data source—one that’s ready to be consumed using standard reporting and data visualization software.
2 Different Data Approaches for Advanced Insurance Analytics & Reporting
Enabling data-driven decisions is important for your insurance organization, but transforming raw data into meaningful, actionable reports is complex, challenging, and time-consuming—and data visualization tools aren’t robust enough to do it on their own.
Instead, there are two common approaches to prepping data for advanced analytics and reporting. Considering the time, effort, and expense that goes into advanced analytics and reporting, identifying the right approach for your organization is a critical decision.
To help you choose, here’s an overview of the two different data approaches for advanced insurance analytics and reporting.
Decentralized Data Approach: Distributed Analytics
The decentralized data approach to analytics and reporting (AKA distributed analytics) involves the work of multiple teams and departments who independently manage their own data with separate tools and processes.
Also worth noting is point-solution data science, a type of distributed analytics that focuses on specific tasks, like predictive modeling and segmentation.
Why the Decentralized Data Approach Doesn’t Work for Insurance Companies
Compared to the centralized approach (more on that below), the decentralized approach to analytics and reporting isn’t a good fit for insurance companies because its multi-team strategy can easily lead to inconsistencies, duplicated efforts, and a lack of unified data governance.
Taking the decentralized approach is also much more complex. It requires intense data transformation and other data science expertise. This isn’t practical for most insurance companies, as onboarding employees with deep data science knowledge in house is a huge expense.
Plus, in the long run, it usually doesn’t lead to the best results. Often, a decentralized approach results in duplicate efforts and different answers to the same questions, leaving your team misaligned and unable to make confident, data-driven decisions.
When Does the Decentralized Data Approach Make Sense?
Of course, the decentralized approach to analytics and reporting exists for a reason. There are types of data analysis that the data warehouse can’t support, such as:
- Predictive model development
- Segmentation model development
- Other types of extensive statistical analysis
In these cases, distributed analytics (and point-solution data science, specifically) is the better fit.
Still, when it comes to day-to-day insurance reporting, distributed analytics is no match for the consistency and efficiency you get with a centralized, governed data solution like a data warehouse.
Centralized Data Approach: Data Warehousing
With the centralized approach to analytics and reporting, all data is consolidated into one system. Often, this system is a data warehouse.
Benefits of the Centralized Data Approach for Insurance Companies
In short, a centralized approach with a data warehouse is the best choice for advanced insurance analytics and reporting because it efficiently integrates data from multiple sources to produce consistent, accurate dashboard and reports that enable your organization to track KPIs on an ongoing basis.
Here are six advantages of a data warehouse for insurance analytics and reporting:
- It’s cost-efficient: Data warehouses are more cost-efficient than a distributed approach to analytics because they eliminate redundant data processing, reduce the need for multiple tools, and streamline data management.
- It empowers self-service: With a centralized approach, every employee in your organization is given access to self-service analytics and reporting, empowering them with the information they need to make data-driven decisions in all activities.
- It creates a data-driven culture: By democratizing access to analytics and reporting, data warehousing helps create a data-driven culture within your organization, deepening engagement and increasing strategic decision-making across all teams.
- It identifies areas for improvement: With all data consolidated in one system, data warehouses easily illuminate data and business processes that need improvements.
- It reduces the number of systems: Rather than depending on multiple source systems, a data warehouse consolidates data into a single source of truth for your entire organization.
- It gives you a 360-degree view of your business: A data warehouse integrates data from all sources, giving you a holistic, 360-degree view of your business, enabling strategic alignment across all teams.
What’s the difference between a data warehouse and a data lake?
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Should You Use a Data Warehouse for Advanced Insurance Analytics and Reporting?
There are many different options for data solutions. Choosing the best data solution for your analytic needs depends on factors including:
- Number of data sources
- Data quantity
- Data quality
- Reporting requirements
- Existing data engineering
- Existing reporting infrastructure
Compared to other data solutions, the data warehouse has many advantages for insurance companies. Eventually, that’s why most insurance companies grow to the point where the benefits of a data warehouse outweigh the cost.
The Dimensional Model Makes the Difference
One of the biggest benefits of data warehousing for insurance companies is the dimensional model.
The dimensional model is a unique data structure specifically created to facilitate advanced reporting. When properly implemented, it allows non-technical users to easily ask very complex questions about data—and receive answers in just a fraction of a second.
Often called self-service analytics, this concept is so powerful that we refer to it as the key to analytics that everyone should understand.
Advanced Insurance Analytics in Real Life: Examples of Insurance KPIs
Every insurance company has unique metrics to track based on your organization’s type, focus, and challenges. These metrics can be related to production, underwriting, claims, and other common insurance data.
Here are some examples of insurance business intelligence KPIs you might see on an insurance dashboard:
1. Written Premium
- The amount of premium written by policy effective date
- Usually reported alongside forecasted written premium reports for the same period
- Can be further segmented by new policies versus renewals; specific lines of business, etc.
2. Earned Premium
- The amount of premium for the elapsed period of each policy
- Calculated using your preferred logic and business rules
- Typically reported based on policy effective date
- Can be further segmented by coverage, line, program, risk state, and other relevant dimensions
3. Commissions
- The amount of money earned or paid as commissions by date
- Can be further segmented by coverage, line, program, employee, partner, etc.
4. Distribution Performance
- Policy counts, written premium, and commissions by distribution channel partner by date
5. Rate adequacy
- Comparison of bound policy premiums to technical premiums by policy effective date
- Can be further segmented by coverage, line, program, underwriter, etc.
6. Cycle Time
- Number of days it takes a policy to progress from a submission to a bound policy
- Can be reported by period, underwriter, line, program, etc.
7. Hit Ratios
- Ratio of bound policies to submissions and bound policies to quotes
- Can be reported by time, line, program, underwriter, etc.
8. Underwriting Process Efficiency
- Visual depiction of policy flow showing status changes from submission to bound by quantity
9. Loss Ratio
- Ratio of incurred loss expense to earned premium
- Can be reported by program year, accident year, and calendar year
10. Incurred and Reserve Amounts
- Can be reported by calendar year, accident year, program, line, etc.
11. Policy Counts
- New, renewal, and endorsement transaction counts
- Can be reported by time, program, line, coverage type, market segment, risk state, etc.
12. Policy Renewal Retention Rate
- Percent of expiring policies renewed
- Can be reported by period, cohort, program, line, etc.
Your preferred KPIs may look different than these examples—but the data warehouse design process allows for that.
Want to see and explore a sample insurance dashboard and reports?
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Preparing to Design Your Data Warehouse for Insurance Analytics
When designing a data warehouse, every business unit and all relevant stakeholders across your organization should have the opportunity to participate.
To build a successful data warehouse that effectively enables advanced analytics and reporting, it’s important that it captures the reporting requirements of all departments—so you want everyone to have a seat at the table when it comes time to define the data model and data transformation rules. At this time, you can also define hierarchies in the data to enable users to drill down to the most granular transaction details or roll up results into custom groups you’ve defined.
7 Surefire Ways to Improve Data Warehouse Outcomes
Given its complexity, scale, and technical challenges, implementing a data warehouse for advanced insurance analytics and reporting can be difficult. In the end, 83% of organizations aren’t fully satisfied with the performance of their data warehousing projects, and 93% believe their data collection, management, storage, and analysis processes need improvements, per The State of Data Management – Why Data Warehouse Projects Fail.
But there are steps you can take to reduce or even eliminate the risks of building a faulty data warehouse. Follow these seven tips to improve your data warehouse outcomes:
- Commit to analytics as a business function: Secure executive buy-in and ensure leadership prioritizes data-driven decision-making. Top-down commitment to data warehousing will smooth operations during and after implementation.
- Involve data warehouse experts early: Bring in data architects and engineers via a
fractional data team from the start to develop an effective, scalable data solution—and avoid costly missteps down the line. - Include a persistent staging area (PSA): Maintain an unaltered copy of raw data throughout the process to improve traceability and simplify debugging if needed.
- Use well-defined design patterns and code standards: Follow best practices for design and coding to ensure consistency, scalability, and maintainability in the long term.
- Load less data, not more: Avoid unnecessary storage costs and improve query performance by focusing only on collecting and transforming relevant data.
- Use agile instead of waterfall: Adopt an iterative, flexible development approach to accommodate evolving business needs and feedback.
- Leverage data warehouse automation: Use automation tools to streamline ETL processes, reduce manual errors, and accelerate deployment times.
Get Help Setting Up Advanced Insurance Analytics and Reporting—We’ll Do the Work For You
In a competitive, fast-changing market, it’s the insurance companies that adopt data-driven decision-making that will come out on top. But deciding to implement advanced insurance analytics and reporting and doing it (and doing it well) are two very different things.
No matter what, some of the work must be done in house, like developing a culture of information and a data-driven mindset within your organization, choosing to invest in data governance, and recognizing analytics as the critical business function it is.
The rest of the work (and the more technical pieces) are best left to expert partners with demonstrated experience, niche skills, and specialized tools that can help you not just attempt advanced insurance analytics and reporting—but implement it in a way that improves your bottom line.
That’s what we do. At LeapFrogBI, our clients typically see 10X ROI within six months.
Want to see that, too?
Let’s get you started with advanced insurance analytics and reporting…
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