It takes a lot of effort to identify and prepare the right data to get desired results. That’s why obtaining clean, rich data is an ongoing challenge for many commercial brokerages. Brokerage principals, producers and commercial lines managers need to know that to get the insights analytics can produce, they must have the best data possible to do it.
But, how do you separate the best data from the garbage? Here’s a few things to consider when it comes to verifying what is good data, bad data and data with potential.
Your problem may be that you haven’t collected enough data to make it valuable. For example, if you’re generating a producer analysis including classes, premium, and policies-in-force (PIF) count; a producer that was hired last month may have enough data entered to appear on your dashboard.
That’s not garbage data, that insufficient data.
False analysis is not garbage data
What happens if an analysis is done poorly and generates garbage results? That’s not the fault of the data. In this instance, the data was only as good as the person generating the report.
Our President, John Eastly said in a recent blog, “Remember to cross-reference the different views of your business and investigate discrepancies. You might find a mistake in the dashboards; or you might discover something new about your business.”
By not using garbage-in-garbage-out as an excuse, it may help you find out why something was or wasn’t happening and how to fix it.
Analytics you don’t need today is not garbage data
While generating these insights, you may find one or two analytic reports that may not be relevant, and you’ll be tempted to file them under the garbage-in-garbage-out category. However, that shouldn’t stop you from revisiting that data in future.
What happens if there’s a sudden change at your brokerage; like writing more of a certain class of business that you previously dismissed as garbage data? It’s much easier to analyze data you already have, instead of starting again from scratch.
The data you have should always have some perceived value compared to data you don't have, like data fields that are left blank. However, blank data fields won’t stay empty forever and that is what will turn garbage data into gold.
Analytics for principals
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