If you joined us or tuned in for IBM’s Fast Track Your Data broadcast from Munich last week, you heard us talk about the history of cars – a most appropriate location for the discussion. But it wasn’t until Henry Ford and the assembly line over twenty years later that the automobile was advanced
Many of today’s top business performers successfully leverage a discipline – data science. Machine learning is one major way to apply data science and with machine learning, the more data we feed in, the better it performs. However, much of the world’s value data cannot be found on the Internet. It
We’re living through the third great revolution in modern business. First came economies of scale, which we harnessed with the Industrial Revolution, the assembly line, and the creation of global markets. Second was network effects, seen most obviously in the rise of the Internet and the Web. Third
This is the fourth in a series of blogs on analytics and the cloud. Read our introduction to the series. This blog concerns itself with the rise of open source software and how it is used for a whole host of analytical purposes. However, as will be seen in this blog, there are significant gaps in
Although NoSQL database technology has been around for a long time (before SQL actually), not until the advent of Web 2.0, when companies such as Google and Amazon began using the technology, did NoSQL’s popularity really take off. Market Research Media forecasts NoSQL Market to be $3.4 Billion by
This is the second in a series of blogs on analytics and the cloud. We will consider the rise of the Internet of Things (IoT), analytics used on that data and how the cloud can be utilized to drive value out of instrumenting a very wide range of ‘things’.
This is the first in a sequence of blogs that takes a peek at what is driving analytics onto the cloud, what are the challenges that will need to be overcome over the next 5 years and how they will be tackled.
Now that we’re into the swing of 2017, the time is ripe for the first CrowdChat of 2017 to explore the goals, challenges and strategies that CDOs and CIOs are focused on for their organizations. Get involved and share your thoughts in this kick-off IMB Big Data CrowdChat.
Why has IBM created its own distribution of Apache Hadoop and Apache Spark, and what makes it stand out from the competition? We asked Prasad Pandit, program director, product management, Hadoop and open analytics systems, at IBM to give us a tour of the reference architecture for IBM Open Platform
From self-service analytics to the cloud, chief data officers (CDOs) had a wealth of information at their fingertips on the first day of IBM Insight at World of Watson 2016. Catch the high points of some of Monday’s most relevant sessions for CDOs in this quick recap.
Data science seems to be experiencing a renaissance when it comes to advanced open source tools. Get a glimpse into creative application development with IPython Notebooks, Jupyter Notebooks, Apache Spark, the PixieDust open source library and more at IBM Insight at World of Watson 2016.
IBM extended Big SQL, which was formerly exclusive to the IBM Open Platform (IOP), to the Hortonworks Data Platform (HDP) in September 2016. I recently spoke with Berni Schiefer, an IBM fellow in the IBM Analytics group, to learn more about the offering and the ongoing IBM focus on SQL.
How can we ensure that metadata about all types of data is accurate, available, ubiquitous and universally accessible? Standards are certainly necessary, but we also need a new way to think about how metadata is created, managed and maintained.