The challenges of managing mountains of information

By John Brandon, Computerworld |  Business Intelligence

Fast access to viewer data is business-critical, Brown says, and for that the company uses IBM Netezza appliances for its data warehouses. Tags are automatically added to data to retrieve specific measurements details. For example, Nielsen can find out how many viewers activated surround-sound audio or whether they used a Boxee device for scheduling their shows.

"We have very granular information needs, and we sometimes want the information summarized up to a broader level -- say, for a customized study of viewer habits," says Brown.

Adapting the techniques

These organizations are proving grounds for methods of handling tremendous amounts of data. StorageIO's Schulz says other companies can mimic some of their processes, including running checksums against files, incorporating metadata and using replication to make sure data is always available.

When it comes to handling massive amounts of data, Schulz says the most important point to remember is that it's critical to use technology that matches your organization's needs, not the system that's cheapest or the one that happens to be popular at the moment.

In the end, the biggest lesson may be that while big data poses many challenges, there are many avenues to success.


Originally published on Computerworld |  Click here to read the original story.
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