IBM Netezza Blog
June 11, 2012
Here are the quick-hit ponderings that I posted on the IBM Netezza Facebook page this past week. I went deeper on machine learning, continued my meditation on all-in-memory, put out some more Hadoop thoughts in advance of next week's Hadoop Summit (where IBM's Anjul Bhambhri will speak on convergence of Hadoop and data warehousing), and tried to anchor social sentiment in the nitty-gritty of behavioral propensity. I opened up a new thread of meditation: the value of proofs of concept (POC) in the data warehousing (DW) appliance procurement process.
June 8, 2012
As our populations grow in a world of limited resources governments and individuals seek ways to lighten our load on the planet. In the Smart Grid R&D Program, PNNL investigates how modernizing the electric grid can help the US meet its carbon management goals. In The Smart Grid: An Estimation of the Energy and CO2 Benefits, a team from PNNL identify nine mechanisms by which the Smart Grid will reduce carbon emissions by 442 million metric tons, or 12 per cent, by 2030. Making the grid smart will save the nation the equivalent of 66 coal power stations, or enough electricity to power 70 million of today's homes. PNNL’s Smart Grid is probably the largest consumer collaboration underway in the US and this collaboration - addressing the information needs of both supply and demand sides of electricity economics - contributes enormously to the success of the program. Consumers on the grid receive real-time pricing and these signals inform their decisions on how and when they consume electric power. PNNL’s report attributes a quarter of the total saving to the conservation effect of consumer information and feedback systems.
June 5, 2012
Garbage is a precious resource. About 80% of the ton of trash that the average family discards every year could be recycled into useful materials, rather than ending up as pollution that hurts our health and destroys the environment.
June 4, 2012
Here are the quick-hit ponderings that I posted on the IBM Netezza Facebook page this past week. After I got back from the Memorial Day break, I had a few additional thoughts on experience (measuring it), Big Data analytics on smartphones (envisioning it), and next best action (modeling it).
May 29, 2012
Here are the quick-hit ponderings that I posted on the IBM Netezza Facebook page this past week. I started the week in a sentimental mood, then developed my 2020 vision, then tried my best to cram it all in memory, then into the palm of my hand, and then finally crammed far more recommendation engine down my mental maw than a mortal human should be expected to chew on:
May 29, 2012
Optimality is the new nirvana. The promise of "next best action" is that, somehow, we can program the optimal automated response into every business scenario. Of course, this dream presupposes that someone in your organization can specify the optimal response for any scenario that your personnel are likely to confront.
May 23, 2012
Here are the quick-hit ponderings that I posted on the IBM Netezza Facebook page this past week. Clearly, I was focused on the "big" side of big data, and on the "statistics" DNA of the analytics that power big data, and on the limits of what you can in fact "optimize" with big data and analytics:
May 9, 2012
Further to news of SUNY’s exploration of big data to understand possible causes of multiple sclerosis, I spoke with David Smith, VP of Marketing at Revolution Analytics, for a briefing on some advantages of R for analysis of large data sets.
May 8, 2012
If this was a start-up, that would be good for at least $100M... Analytics. Big Data. At a recent conference I attended, one of the keynote speakers stated that start-ups with “Analytics” in their business description are getting about two times the average valuation by the venture capital community, but those that combine “Analytics” and “Big Data” are getting about ten times the valuation. Netezza is no longer a start-up - we at Netezza have been helping customers with analytics and big data since our beginnings over ten years ago. And then there was that little matter of our acquisition by IBM, itself at a pretty healthy valuation. There isn’t really anything new about big data but the name. Companies have had to deal with larger amounts of data, more types of data, and faster generated or changing data since data has existed. Now because the term has gone viral, all the data management vendors are trying to wedge it into every press release and all their social media posts to catch the search engines. (Vendors in other segments seem to be looking for ways to get in on that game. Maybe we’ll see Kellogg’s “Big Data Crunch” on our supermarket shelves soon.)
April 20, 2012
In the context of data warehousing, agility means that the system can quickly and easily adapt to and accommodate; changes in data volumes, new data sources, new subject areas, new applications and/or new users. In order for a data warehouse to be able to do this, it needs to be able to run any query against any data model/schema. It must also be able to process all of the data, no mater what the query or what table(s) are being accessed, and do so quickly – without impacting the other users of the system.