IBM has just pushed competition for big data analytics business up a notch with the opening of its new Accelerated Discovery Lab (ADL). While most vendors are happy to provide big data analytics applications, or functionality, for their customers, Big Blue is offering its customers an entire research lab to help them draw meaning from their data.
IBM Research, Labs
This is not the first time that IBM has used its research and academic weight to muscle its way to the top of the big data and analytics space. In the past we have seen that it has hooked-up with a number academic institutions to ensure a good supply of analytics experts to fill the skills gap that is rapidly developing in the space.
It has also teamed up with entire cities to get to bring its Smart Planet initiative down to earth, and set up Customer Experience labs in 12 different locations (to date!) all over the world to feed analytics into its customer experience software.
However, while much of this will only produce practical applications for enterprises in the medium term, the opening of ADL appears to offer enterprises immediate access to IBM’s advanced analytics without even having to buy analytics applications.
IBM describes ADL as a collaborative environment that offers IBM clients the possibility of discovering previously unknown relationships between different data sets.
And IBM is really offering everything here. It provides access to a huge number of data sources, offers access to domain models, text analytics combined with natural language processing capabilities that it developed around the Watson supercomputer project, as well as access to human expertise in biology, medicine, finance, weather modelling and a lot more.
The thinking, IBM says, is that while enterprises probably have some expertise that they can bring to bear on a number of datasets, they won’t be able to bring the kind of expertise that IBM is talking about here.
Using the IBM approach, enterprises will have not just top-end tools, but also a wide set of perspectives offered by this vast network of experts that offer different contexts and draw new value in new ways from data.
If we think about Big Data today, we mostly use it to find answers and correlations to ideas that are already known. Increasingly what we need to do is figure out ways to find things that aren't known within that data," said Jeff Welser of IBM Research Accelerated Discovery Lab.
IBM is also adding into the mix its collaboration abilities, which means that the network of experts and expert technologies that can be drawn on can be spread across the entire globe, but still working on the same infrastructure.
If this looks a bit vague, then IBM also provides examples of how it might be used. Social analytics is a case in question. While marketers amass terabytes of data and spend billions of dollars on technologies to gain customer insights, they still often get it wrong.
IBM says that this is because they still focus on indices like age, sex, marital status, dwelling place, or income as opposed to developing strategies based on patterns identified on social media behavior. Because of the ability to dissect this information, patterns can be built, or identified, even in very small amounts of information.
There is another aspect to this that IBM is understandably reticent about introducing here. IBM deployments are notoriously complicated to use and long to implement. However, if enterprises can farm out their analytics needs, it really does simplify the whole process.
IBM has not explained how this will work, or how much enterprises will have to pay to access these services, but it is not likely to be cheap. But then if it turns redundant data into potentially profitable insights then it’s likely to generate considerable interest.
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