“We’ll know that data has finally made it as the second most valuable asset in our company when all senior executives understand what 'metadata' means." This was an absolutely wonderful quote from a senior leader at a large insurance company with accountability for an enterprise data management initiative. (Even better,
The insurance industry is full of clichés regarding data: “data rich, information poor,” “data is the lifeline for insurers” and the old favorite “garbage in, garbage out.” Insurance is an industry that relies on solutions to help businesses gain insight into risk, cause of loss and resulting claims. They need
Many of us in the SAS world are responsible for the data that feeds the various business intelligence, analytics and business solutions provided by SAS. We’ve been involved in data integration, data migration, data quality, ETL / ELT, data access projects that support SAS solutions or are used for other
Can data management reduce your risk exposure? Many managers are quick to point out that they’re awash in data but need help to make sense of it. While this may be true, it’s still worth it to ask: do you have the data you most need to make better decisions?
What's the Fifth Law of Data Quality? Jim Harris explains.
The Mobile Communications article, Why retailers need to be mobile for the 2010 holidaysincludes a lot of compelling reasons for taking your sites mobile. I pulled together a list of six reasons from that article plus a few other sources so you can see the full argument here in a
@ocdqblog explains the fourth law of #DataQuality in today's CoE blog post
The First Law of Data Quality explained the importance of understanding your Data Usage, which is essential to the proper preparation required before launching your data quality initiative. The Second Law of Data Quality explained the need for maintaining your Data Quality Inertia, which means a successful data quality initiative requires a
The First Law of Data Quality explained the importance of understanding your Data Usage, which is essential to the proper preparation required before launching your data quality initiative. This makes sure that you neither climb every data mountain, nor make a mountain out of every data molehill—but instead focus on
“You don't talk about data quality.” No, wait—that's The First Rule of Poor Quality Data. The First Law of Data Quality: “Data is either being used or waiting to be used—or wasting storage and support.” Although understanding your data is essential to using it effectively and improving its quality, as
