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Analytics
Shaun Barry 0
Celebrate (?!) Tax Day

Happy Tax Day, America! Today marks our annual ritual of filing tax returns in the United States.  And complaining about taxes.  And cursing the IRS (even though it's misguided to shoot the messenger, in my opinion). Think you know a lot about taxes?  Let's travel back in time to 1913, when

Rick Wicklin 0
Examine patterns of missing data in SAS

Missing data can be informative. Sometimes missing values in one variable are related to missing values in another variable. Other times missing values in one variable are independent of missing values in other variables. As part of the exploratory phase of data analysis, you should investigate whether there are patterns

Stuart Rose 0
Innovation in reinsurance – no longer an oxymoron

Insurance is a tough marketplace, but in many respects reinsurance is tougher! Today, the reinsurance industry is faced with an unprecedented number of challenges especially with what appears to be an increasing frequency and severity of man-made and natural catastrophes. To combat these challenges, reinsurers are turning to technology for

Data Management
Jim Harris 0
Pushing data quality beyond boundaries

Throughout my long career of building and implementing data quality processes, I've consistently been told that data quality could not be implemented within data sources, because doing so would disrupt production systems. Therefore, source data was often copied to a central location – a staging area – where it was cleansed, transformed, unduplicated, restructured

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Advantages of a standard insurance data model

In my first blog article I explained that many insurance companies have implemented a standard data model as base for their business analytics data warehouse (DWH) solutions. But why should a standard data model be more appropriate than an individual one designed especially for a certain insurance company?

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