Over the last year I have spent a good amount of time thinking about the cost of analytics, and a few things worry me about our industry and how vendors price in this industry.  In the Data Warehousing and BI industry, we're starting to see pricing models based on data volume or size. I know of one vendor which prices by specint, so - get a bigger system or virtualize your systems - get a big bill.

 The problem with these licensing schemes is that they actually make the cost of doing analytics more expensive over time.  If I am a good technology user, over time I'm driving down the costs per unit of data volume to create and maintain the data flowing into my DW.  In the simple case you could say that the cost to generate data is declining at the inverse rate of Moore's law, particularly for a web business where most of the cost is in hardware.

Back to the point, using Moores Law as the example, the cost of data generation declines per unit of volume at a rate of ~50% every 18 months. 

Analytics as a Service

What Do you think about Agile Analytics? Every heard about it? Well, here are a couple thoughts from the guys who deal with it on a daily basis.  

Analytics as a Service 

Looking forward to seeing your comments on this

Next week I will be attending the next iteration of the xldb group events organized around eXtreme Large Database Applications. xldb workshop

With 100s of Peta Bytes of information waiting to be captured and analyzed, new concepts are required to scale today's platforms by 1-3 orders of magnitudes.

Today we 'limit' ourselves to 'only' capture 40TB/day of incremental incoming data volumes, next generation requirements demand a much more detailed collection of event detail data. 100TB/day are already on the horizon giving us just 10 days of history per Peta Byte. With deep historical requirements of 3+ years of information, data volume growth will outpace Moor's Law. And I would not be surprised if next year this time we will be thinking about how to deal with 250TB/day — the writing is on the wall.

Improvements in Processing Power per CPU, advances in Memory and Storage are not going to the able to make up for the exponential growth of data processing requirements.