Everyone knows everything is bigger in China, and big data is no exception. This is why it is no surprise that big data interest is gaining a lot of momentum in China.
On August 23 and 24, IBM organized a technical summit in Beijing, and one of the main tracks was about big data. In fact, this
"Value" is the key word in several of my top picks this week. From saving money to saving lives to saving time in who you follow on Twitter, we're still finding new ways to get value from data.
“What Executives Don’t Understand About Big Data,” by Michael Schrage, Harvard Business Review HBR Blog
More and more organizations are turning to Predictive Maintenance. These solutions help predict which asset or part of an asset is likely to fail or needs service, so organizations only replace parts or machinery when needed, not when they are “supposed to.”
Why big data doesn’t have to mean big security challenges
What is big data?
Big data spans four dimensions: volume, velocity, variety and veracity.
Volume: Every day 2.5 quintillion bytes of data are generated from new and traditional sources including climate sensors, social media sites,
Harness the Power of Big Data is a new book by several of the authors of Understanding Big Data, the hugely popular book that debuted in 2011. Big data represents a new era of computing – an inflection point of opportunity where data in any format may be explored and utilized for breakthrough
It isn’t until the lights go out and the HVAC system stops working that most of us appreciate how important utilities are to our everyday lives. For the most part, energy and utility companies have operated in a predictable, linear way with reliable service, in spite of population growth and
As a business asset, “next best action” is not a secret sauce. It’s more like a layered lasagna that–depending on the ingredients, how carefully you assemble them, and how well you bake and serve it all–can be either a highly consumable platform or a sloppy mess of ill-fitting investments that you’
Predictive analytics is not just about forecasting what might happen. It’s also about detecting the warning signs of bad things that, if we don’t act quickly, might prove catastrophic or highly disruptive.
In the engineering world, for example, many organizations use statistical tools to predict
Did you miss me? After taking off the month of August to launch the IBM Big Data Hub (and to run the 199-mile Hood To Coast Relay), I’m eager to be back on the “Top Reads in Big Data” beat, rounding up great articles, blog posts, videos, podcasts and infographics. It was a short work-week in the
Based on his recent blog post, “Why Static Stinks,” Tom Deutsch, program director of IBM big data portfolio, further explains why non-personal recommendations – or “static” – are bad. Deutsch states that it is important to understand not only the trends of consumers, but who they are as people,
According to several research firms, 70% of traditional data warehousing/Business Intelligence projects were considered failures. Fundamental to every data warehousing project is a basic and simple concept – “data integration.” Data integration technology became fundamental as data complexity,
For more information, download the white paper "Managing big data for smart grids and smart meters" or visit www.ibm.com/bigdata
Evolving technologies in the energy and utilities industry, including smart meters and smart grids, can provide companies with unprecedented capabilities for forecasting
As social media tools become more prevalent, those participating in the online conversation become increasingly influential, especially as their opinions travel faster and to a wider group of consumers. The IBM Social Sentiment Index uses analytics and natural language processing technologies to