The basic big data problem is simple to understand: we create too much data to store and analyze it all. The problem gets bigger, however, when you consider the related factors: our problems themselves are getting bigger, the analytics needed to solve them are more complex and the data is
Are you one of those people that love doing analysis? If so, there is nothing quite like analyzing customer patterns, revenue trends, inventory levels, cost optimization, etc. -- especially when you can use that analysis to make changes that will optimize your business. But before you can start the analysis,
“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
