What Drives Successful Data Analytics for Insurance Companies?

Have you ever been promised that if you just had the right analytics software and [fill in the blank] data, you could generate game-changing insights that would transform your insurance operations? If only it were that simple. Unfortunately, insights don’t come from simple osmosis by staring at your new reports and dashboards. That is a recipe for analysis paralysis, and we’ve all been there. Because data by itself is meaningless.

So, what is most important when thinking about data and analytics? That would be the context. Data, and ultimately data decision-making, needs context. That context is simple: What business problem are you trying to understand or solve for? Increasing your renewals? Once you’ve identified what you want to know, you’ll understand what it is that you need to measure.

Without context, insurance organizations can get bogged down by an expanding project scope, get lost in the fallacy of always needing perfectly accurate data, and then under-deliver on the objectives.

Three Success Factors for Data Analytics in Insurance

The fundamentals of data-driven decision-making are the ability to identify the problem, contextualize it through several measures that will support your understanding, and then collect the needed data. Here are several important success factors to any data analytics project:

1. Solidify the Scope

You don’t have to boil the ocean or try and get every data point in your insurance organization “clean” to find value in your data. Set a defined scope to eliminate the runaway nature of these projects.

2. Contextualize the Problem

Articulate the metrics and data points which you think will provide context and understanding to your problem.

3. Get Comfortable with Your Data Quality

Each business decision requires a different level of data quality. There is a continuum that you and your team need to understand where you feel comfortable making assumptions or trusting that the data is “directionally correct.” Leaders often become caught in the trap of always wanting more detail to help ensure they are making accurate decisions, even when it may not be necessary.

If you keep these points in mind, you’ll be able to leverage your data and predictive analytics more effectively and efficiently for insights.

Remember, Data Analysis Begins with Context

Data analytics are an important competitive advantage that are necessary in today’s changing insurance industry. Luckily, insurance organizations already have massive amounts of data at their fingertips. Whether you’re handling your data analysis internally or by outsourcing the initiative to experts, asking the right questions and setting the context should always be your starting point.

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