We've had Amazon Web Services (AWS) available for some time, and used for some interesting projects that wouldn't be possible if groups had to purchase their own equipment. From password cracking to cancer research, there are some amazing possibilities. Windows Azure works as well, and there are customers such as banks, using the cloud to perform complex analysis of data. Not to be left out, Google announced recently they were provide their Google Compute Engine to anyone, without any limits on scale. You have to pay for it, but if you want 770,000 cores to solve a problem, they'll provide them if you can pay for the resources. It's US$2m/day, but it's there.

I don't know what that means for us as data professionals, but I suspect there will be any number of companies that will consider using these types of resources to work on complex analysis of problems. Running a simulation might be something that costs a few hundred dollars a day, using resources for minutes at a time. Looking over some of the different projects people have used cloud computing engines for, it seems that many businesses might find this to be a cost effective way to perform data mining or BI type workloads.

If you can get the data to the engine. I still wonder how challenging this is, apart from all the security concerns. Just moving that much data around, refreshing it, removing the stale data, could be very complex. It concerns me that as more people attempt this type of work, we will have more and more poor decisions made because of data quality, age, or accuracy. After all, garbage in, garbage out has been a tenet of data professionals for decades.

If you're using cloud resources for computation, I'd love to know about it. I know many of the data professionals out there have reservations, and that's healthy. It shouldn't blind you to the possibilities that this is a most cost effective way of actually getting analysis and answers for your users.

Steve Jones

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