Importance of Data in Data and Analytics

Have you ever been promised that if you just had [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. And that context is simple: What business problem are you trying to understand or solve for? Increase your renewals? Analyze your outsourcing partners? Improve your insurance operations? Once you’ve identified what you want to know, you’ll understand what it is that you need to be measure.

Without context, I’ve seen insurance organization’s 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.

The fundamentals of data-driven decision-making is 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:

Solidify the Scope — you don’t have to boil the ocean or try and get every data point in your insurance organization ‘clean’ prior to gleaning value from your data. Set a defined scope to eliminate the runaway nature of these projects.

Contextualize the Problem — articulate the metrics and data points which you think are going to provide context and understanding to your problem.

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 get caught in the trap always of wanting more detail to help ensure they are making accurate decisions, when it may not be necessary.

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

It’s time for insurance organizations to embrace data and analytics, as we’re uniquely positioned to leverage the massive amounts of data we’re already collecting. More importantly, data analytics are an important competitive advantage that are necessary in today’s changing insurance industry. 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.