P&C insurance is an incredibly data-driven industry. Your company’s core assets are data, your business revolves around collecting data, and your staff is focused on using data in their day-to-day workstreams. Although data is collected and used in normal operations, oftentimes the downstream analytics process is painful (think of those month-end reports). This is for any number of reasons:
- Large, slow data flows
- Unmodeled data that takes manual intervention to integrate
- Legacy software that has a confusing backend and user interface
- And more
Creating an analytics ecosystem that is fast and accessible is not a simple task, but today I’ll take you through the four key steps Aptitive follows to solve business problems with an insurance analytics solution. I’ll also provide recommendations for how best to implement each step to make these steps as actionable as possible.
Step 1: Determine your scope.
What are your company’s priorities?
- Trying to improve profit margin on your products?
- Improving your loss ratio?
- Planning for next year?
- Increasing customer satisfaction?
To realize your strategic goals, you need to determine where you want to focus your resources. Work with your team to find out which initiative has the best ROI and the best chance of success.
First, identify your business problems.
There are so many ways to improve your KPIs that trying to identify the best approach can very quickly become overwhelming. To give yourself the best chance, be deliberate about how you go about solving this challenge.
What isn’t going right? Answer this question by talking to people, looking at existing operational and financial reporting, performing critical thinking exercises, and using other qualitative or quantitative data (or both).
Then, prioritize a problem to address.
Once you identify the problems that are impacting metrics, choose one to address, taking these questions into account:
- What is the potential reward (opportunity)?
- What are the risks associated with trying to address this problem?
- How hard is it to get all the inputs you need?
Taking on a scope that is too large, too complex, or unclear will make it very difficult to achieve success. Clearly set boundaries and decide what is relevant to determine what pain point you’re trying to solve. A defined critical path makes it harder to go off course and helps you keep your goal achievable.
Step 2: Identify and prioritize your KPIs.
Next, it’s time to get more technical. You’ve determined your pain points, but now you must identify the numeric KPIs that can act as the proxies for these business problems.
Maybe your business goal is to improve policyholder satisfaction. That’s great! But what does that mean in terms of metrics? What inputs do you actually need to calculate the KPI? Do you have the data to perform the calculations?
Back to the example, here are your top three options:
Based on this information, even though the TTC metric may be your third-favorite KPI for measuring customer satisfaction, the required inputs are identified and the data is available. This makes it the best option for the data engineering effort at this point in time. It also helps you identify a roadmap for the future if you want to start collecting richer information.
As you identify the processes you’re trying to optimize, create a data dictionary of all the measures you want to use in your reporting. Appreciate that a single KPI might:
- Have more and higher quality data
- Be easier to calculate
- Be used to solve multiple problems
- Be a higher priority to the executive team
Use this list to prioritize your data engineering effort and create the most high-value reports first. Don’t engineer in a vacuum (i.e., generate KPIs because they “seem right”). Always have an end business question in mind.
Step 3: Design your solution.
Now that you have your list of prioritized KPIs, it’s time to build the data warehouse. This will allow your business analysts to slice your metrics by any number of dimensions (e.g., TTC by product, TTC by policy, TTC by region, etc.).
Aptitive’s approach usually involves a star schema reporting layer and a customer-facing presentation layer for analysis. A star schema has two main components: facts and dimensions. Fact tables contain the measurable metrics that can be summarized. In the TTC example, the fact-claim tables might contain a numeric value containing the number of days to close a claim. A dimension table would then provide context for how you pivot the measure. For example, you might have a dimension-policyholder table that contains attributes to “slice” the KPI value (e.g., policyholder age, gender, tenure, etc.).
Once you design the structure of your database design, you can build it. This involves transforming the data from your source system to the target database. You’ll want to consider the ETL (extract-transform-load) tool that will automate this transformation, and you’ll also need to consider the type of database that will be used to store your data. Aptitive can help with all these technology decisions.
You may also want to take a particular set of data standards into account, such as the ACORD Standards, to ensure more efficient and effective flow of data across lines of business, for example. Aptitive can take these standards into account when implementing an insurance analytics solution, giving you confidence that your organization can use enterprise-wide data for a competitive advantage.
Finally, when your data warehouse is up and running, you want to make sure your investment pays off by managing the data quality of your data sources. This can all be part of a data governance plan, which includes data consistency, data security, and data accountability.
Don’t feel like you need to implement the entire data warehouse at once. Be sure to prioritize your data sources and realize you can gain many benefits by just implementing some of your data sources.
Step 4: Put your insurance analytics solution into practice.
After spending the time to integrate your disparate data sources and model an efficient data warehouse, what do you actually get out of it? As an end business user, this effort can bubble up as flat file exports, dashboards, reports, or even data science models.
I’ve outlined three levels of data maturity below:
The most basic product would be a flat file. Often, mid-to-large sized organizations working with multiple source systems work in analytical silos. They connect directly to the back end of a source system to build analytics. As a result, intersystem analysis becomes complex with non-standard data definitions, metrics, and KPIs.
With all of that source data integrated in the data warehouse, the simplest way to begin to analyze the data is off of a single flat extract. The tabular nature of a flat file will also help business users answer basic questions about their data at an organizational level.
Organizations farther along the data maturity curve will begin to build dashboards and reports off of the data warehouse. Regardless of your analytical capabilities, dashboards allow your users to glean information at a glance. More advanced users can apply slicers and filters to better understand what drives their KPIs.
By compiling and aggregating your data into a visual format, you make the breadth of information at your organization much more accessible to your business users and decision makers.
The most mature product of data integration would be data science models. Machine learning algorithms can detect trends and patterns in your data that traditional analytics would take a long time to uncover, if ever. Such models can help insurers more efficiently screen cases and predict costs with greater precision. When writing policies, a model can identify and manage risk based on demographic or historic factors to determine ROI.
Start simple. As flashy and appealing as data science can be to stakeholders and executives, the bulk of the value of a data integration platform lies in making the data accessible to your entire organization. Synthesize your data across your source systems to produce file extracts and KPI scorecards for you business users to analyze. As users begin to adopt and understand the data, think about slowly scaling up the complexity of analysis.
This was a lot of information to absorb, so let’s summarize the roadmap to solving your business problems with insurance analytics:
- Step 1: Determine your scope.
- Step 2: Identify and prioritize your KPIs.
- Step 3: Design your solution.
- Step 4: Put your insurance analytics solution into practice.
Aptitive’s data and analytics consultants have extensive experience with roadmaps like this one, from outlining data strategy to implementing advanced analytics. If you think your organization could benefit from an insurance analytics solution, feel free to get in touch to discuss how we can help.
Greg Marsh is a Data Engineer Manager at Aptitive. In his role, Greg facilitates the discovery of business insights from data. From “Big” data like IoT streams or classic relational ERP information, Greg helps companies to unlock the power of their data.